DEV Community: N Chandra Prakash Reddy

Moving Beyond Vibe Coding: A Deep Dive into AWS Kiro and the AI-DLC

N Chandra Prakash Reddy — Tue, 23 Jun 2026 17:08:50 +0000

One of the best ways to keep up with the fast-moving cloud sector is, frankly, to go to community tech events. I had the privilege of attending the AWS User Group Chennai Meetup on 15th November 2025. There were loads of great topics throughout the day on all sorts of cloud technologies but one of them really changed how I look at programming in the future.

The presentation focused about using AWS Kiro, an agentic IDE for building AI-DLC (AI-Driven Development Lifecycle). As someone who writes and tests code on a daily basis, I could definitely relate to this talk. This blog is for you if you’ve ever felt AI coding tools are powerful but a bit unpredictable.

Here is everything I learned from the session, broken down step by step.

The Problem: Why "Vibe Coding" Is Not Enough

Does this sound familiar? You start an AI chatbot, describe the app you want in a few sentences, and the model spits out hundreds of lines of code quickly. The program operates. It looks good. And you feel like a magician. This is what we call "vibe coding" - coding by just defining the overall mood or idea of the application.

Vibe coding is perfect for short prototypes or weekend tweaks. But here is the thing, the instant you worry about enterprise security, team cooperation or deploy to production, it falls apart.

The speaker revealed a surprising statistic: independent research has shown that roughly 45% of AI-generated code is filled with security flaws. These include harmful injection faults and bad dependency choices.

Because the AI is only guessing off of your “vibes,” it will hallucinate APIs, software packages, and functions that do not really exist. This results in exploitable flaws and a terrible supply-chain danger known as “slopsquatting,” when malicious actors register the identities of the phony dependence the AI hallucinated.

Picture this: you're ordering food on Swiggy, but you just type in, "I want something spicy." You could end up with a great curry or a bowl of raw chillies. Without explicit parameters you are leaving the results to chance.

AWS Kiro: The Answer to Vibe Coding

The session then offered AWS Kiro as a solution to this mess. Kiro is an AWS-native, agentic IDE (Integrated Development Environment) built to address exactly these kinds of difficulties.

Instead of letting the AI run free, Kiro offers structure. It introduces spec-driven programming, routing rules and automatic hooks. In other words, it assures your AI-assisted coding is well-structured, highly auditable and truly production-ready.

The Spec-Driven Flow

Traditional AI tools go straight from your prompt to writing code. AWS Kiro implements a smThe Spec-Driven Flowarter 5-step workflow:

Idea / Prompt: You describe your initial need.
Specs: Kiro generates concrete requirements, user stories, and acceptance criteria.
Tasks: It creates an implementation checklist.
Code & Tests: The AI writes the code alongside automated tests.
Run & Integrate: Everything is deployed and connected.

Imagine your database is a library and you are adding a new wing. You don’t just bring in builders and instruct them to start building bricks based on the “vibe” of a library. First you would draw blueprints. Kiro is the architect, having you develop those plans before a single line of code is produced.

A Real-World Example: Building a Shopping Chatbot

The speaker guided us through a live example of constructing a grocery buying chatbot tied to GPT-4o during the lecture. It was fun to see the Kiro UI in action.

Kiro didn’t merely throw out Python code when the project began. Instead, it asked explanatory questions like: Which platform do you prefer ? What shopping features would you like? Already have an API key from OpenAI?

Generating the Blueprints

When the developer responded, Kiro created a requirements.md file in the EARS format (a formal means to express clearly defined system requirements). It laid out specific user stories, including letting a buyer search for products or get cooking suggestions based on dietary constraints.

From there Kiro went on to the design process. It automatically created specific markdown files for the architecture, data flow, error handling and unit testing approach for the solution. Lastly, it made a Kanban-style task board (tasks.md) to graphically track progress.

To be fair, it would take a dev team days to setup all this manually. Kiro completed it in seconds, yet with the rigor and documentation of a senior technical team.

The Secret Sauce: Hooks, Steering, and MCP

And this is when it gets interesting. AWS Kiro is more than simply a chat window, it is tightly integrated into the developer workflow with a few killer features.

Agent Hooks

Kiro features an automated Agent Hook System to remove repetitive tasks. It watches your workspace for certain occurrences (e.g. saving a file or making a code commit).

It executes a specified prompt when an event occurs. For example, the speaker displayed a hook that automatically updated the master requirements when a new file was requirements.md file to keep the documentation exactly in sync. Think of it as a smart thermostat that automatically changes the temperature when you open a window.

Project Steering

If everyone on a team codes in their own way the code base quickly becomes a nightmare . Kiro implements a steering.md file to solve this.

This is the steering wheel for the AI agent. It enforces code standards, architectural patterns and organizational principles. This enables team alignment and very consistent code generation, regardless of who in the team is using the tool.

Model Context Protocol (MCP)

Kiro can also connect to additional tools and data sources via MCP (Model Context Protocol). Looking at the mcp.json configuration file I saw how Kiro could be plugged directly into AWS documentation and give the AI agent real-time, correct context from the source.

Traditional SDLC vs. The AI-DLC

You’re probably thinking, why do we need a new tool solely to create code? The speaker made a strong case for how our existing Software Development Life Cycle (SDLC) is actually holding AI behind.

The classic SDLC (Plan --> Design --> Build --> Test --> Deploy) was built for human-driven, long-running procedures with plenty of heavy planning, meetings, and manual handoffs. Most organizations are merely adding AI to this old procedure right now. We employ AI as a narrow aid to develop a rapid function but the basic obstacles are still there.

Enter the AI-Driven Development Lifecycle

“To truly unlock the potential of generative AI, we need to rethink the whole lifecycle. The AI-DLC is bringing AI into the heart of the partnership, not as a bolt-on sidekick.

The core working pattern is a continuous, high-speed loop:

The AI creates a plan.
The AI asks clarifying questions.
Humans validate and make the critical decisions.
The AI implements the validated plan.

New Language and Rituals

The process is so substantially faster that standard Agile language is expanding under the AI-DLC

Use highly intense "Bolts" (hours or days) instead of long "Sprints" (weeks).
Massive “Epics” are now just “Units of Work.” Instead, the new methodology promotes “Mob Elaboration” and “Mob Construction” over developers working in isolation, where the entire team comes together to collectively make technical decisions and solve complicated business challenges around the AI’s output.

The Practical Benefits of AI-DLC

Bottom line: The shift to this spec-driven, AI-first architecture has huge benefits for organizations and developers alike.

Massive Velocity: Requirements, design and coding requirements, design and coding 5x-20x faster. Work that took weeks is compressed into hours.
Higher Quality: Kiro needs constant explanation, so the end result is much closer to the genuine business goal. It also enforces organizational norms throughout the board.
Better Developer Experience: Less time spent on boilerplate programming means more time for innovative issue solving and genuine business value.

Key Takeaways

I walked out of this seminar with a new perspective on where cloud development is headed. Here are the primary things I got from the talk:

Architecture Metrics Matter First: You can’t build a fantastic AI application on flawed architecture. Event-driven patterns such as Amazon EventBridge and AWS Step Functions are still important for developing robust systems.
Specs are the New Code: In an AI-driven environment, your ability to write precise, organized specifications is more important than your ability to type syntax. Specs ground AI and keep it correct, and tools like Kiro prove it.
Security Cannot Be an Afterthought: Almost 50% of code produced by AI is vulnerable, hence for production systems we need automatic guardrails like steering files and tight review loops.
The Developer's Role is Shifting: We’re moving away from just syntax writers and heading toward system orchestrators, architects and strategic decision-makers.

Conclusion

In the end, AI is transforming software development at a basic level, but if we’re serious about building safe, enterprise-grade systems, we can’t just rely on raw “vibe coding.” The transition from traditional SDLC to the AI-DLC is a tremendous leap in the delivery of value by the teams.

AWS Kiro gives you the framework, the guardrails and workflows you need to treat AI as a true and dependable engineering partner. Programming is not about typing faster. It's about thinking clearer. And directing intelligent agents with accuracy.

About the Author

As an AWS Community Builder, I enjoy sharing the things I've learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don't hesitate to get in touch! 🚀

🔗 Connect with me on LinkedIn

References

Event: AWS User Group Chennai Meetup

Topic: Moving Beyond Vibe Coding: A Deep Dive into AWS Kiro and the AI-DLC

Date: November 15, 2025

Also Published On

AWS Builder Center

Hashnode

When Systems Listen: Event Driven Architecture

N Chandra Prakash Reddy — Sun, 21 Jun 2026 08:48:06 +0000

To master real-time AWS applications, focus on events rather than handling a constant stream of requests.

Attending a full-day tech conference can be overwhelming with all the information you take in. On 01 November 2025, I got to attend the AWS Student Community Day in Tirupati. The event featured many great sessions about different parts of cloud computing.

The last session of the day really changed how I think about building modern applications. Poobalan gave a talk called "When Systems Listen: Event Driven Architecture." Even though it was late in the day, everyone was energized, and the ideas he shared were very practical.

I took lots of notes and snapped photos of the slides to capture everything. Now, I’ll share what I learned about Event-Driven Architecture (EDA) and explain why it’s important for developers today.

The "Are We There Yet?" Problem

Before we can see why Event-Driven Architecture matters, it helps to look at how traditional systems talk to each other. Poobalan kicked off the session with a familiar example: ordering food online.

Think about ordering your favorite meal on a food delivery app like Swiggy or Zomato. After you place your order, you watch your screen, waiting for updates. With traditional request-driven systems, your app would keep asking the restaurant's server, "Is it ready yet? How about now? Is it cooking now?"

Does this sound familiar? This is known as polling. Imagine being on a road trip with a child in the backseat who keeps asking if you are there yet. In software, this kind of constant checking uses up resources, puts extra strain on servers, and can cause annoying slowdowns. In the presentation, a funny meme showed a guy sitting alone on a park bench, which summed up the pain of always 'checking for updates.'

You might be wondering, isn't there a better way to handle this?

Enter Event-Driven Architecture

Here’s where things get interesting. Event-Driven Architecture changes the usual approach. Instead of components always checking in with each other for updates, the system follows the old Hollywood saying: "Don't call us, we'll call you."

In an EDA setup, components communicate by sending and receiving events. When something happens in your application, it sends out an event. Other services that are interested in that event listen for it and respond as needed.

The Anatomy of an Event

The slides explain that an event is just a change in your system’s state. There are also three main rules that define what makes something a true event:

Past tense: Events should describe something that has already happened. For example, use names like "Order Created" or "User Signed Up" instead of "Creating Order."
Immutable: After you publish an event, you will not be able to make any changes to it.
Decoupled: The system that publishes the event does not know who is consuming it, and it is not concerned with this information.

The Three Musketeers of EDA

The system that sends out the event does not know who will receive it, and it does not need this information.

Event Producers: These systems or services create events whenever something changes, such as when a customer clicks the checkout button on an online store.
Event Router/Broker: You can think of this part like a post office. It takes in events and sends them to where they need to go.
Event Consumers: These services wait for certain events so they can respond and do their jobs, such as sending a confirmation email after an order is placed.

Traditional vs. Modern: The Showdown

If you usually build request-driven applications, moving to event-driven architecture means thinking differently. In the session, a clear side-by-side comparison helped show exactly how the two approaches differ.

In a Request-Driven setup, communication happens in real time. The client sends a request and waits for a response. Since the systems are closely linked, if one service is slow, everything else slows down too. This setup does not scale well, and heavy traffic can quickly lead to bottlenecks across the system.

In EDA, communication happens asynchronously. When a client sends an event, it can continue working right away. The systems are loosely coupled, so responses do not block the process. This setup makes it possible to handle millions of independent events without slowing down the main application thread.

Here’s the main idea: request-driven systems are like calling someone and expecting them to answer right away. EDA is more like sending a letter; the person can read it and respond when it suits them.

Why We Should Care

Switching to an event-driven model brings several important benefits for today’s cloud applications:

Loose Coupling: Since services don’t need to know about each other, different teams can develop, deploy, and update them independently.
Real-Time Responsiveness: Systems can respond to changes the moment they happen, which allows for instant decision-making.
Resilience: If your email notification service goes down, your checkout service keeps working. It continues sending "Order Placed" events, which can be replayed when the email service is running again.
Cost-Effective: When you remove the need for constant polling, you can greatly lower your operational and computing costs.

Building EDA on AWS

At AWS Student Community Day, Poobalan moved on to talk about the main AWS services that support Event-Driven Architecture. These are the tools you use to build these systems.

Amazon EventBridge

This service is a serverless event bus that links your applications. It works like an event traffic controller by taking in events, checking them against rules you set, and sending them to the right places.

Amazon SNS (Simple Notification Service)

This service uses a publisher/subscriber model for messaging. Its main job is to broadcast messages. Picture it as 'one message, many listeners.' If you need to notify several backend services at once when an event happens, SNS is the right choice.

Amazon SQS (Simple Queue Service)

SQS is a message queue. It stores messages safely until the coSQS is a message queue that keeps messages safe until your services are ready to handle them. Imagine your database as a library, and 500 people try to check out a book at the same time. SQS works like a message waiting room, putting everyone in order so the librarian is not overwhelmed and no requests are missed.

Seeing it in Action: The Ride Booking App

Learning theory is helpful, but seeing it in action really helps it sink in. In the presentation, we watched a live demo with a made-up taxi ride-booking app.

We looked at the Amazon EventBridge console on the screen, which showed different routing rules. There was one rule named ride-to-driver-matching and another called driver-to-notification.

When someone clicked "Book a Ride" on the app, it triggered an event. We saw the Service Control Panel and the Ride Status Dashboard update right away. Thanks to event-driven architecture, the Driver Matching Service and the Notification Service worked on their own without depending on each other.

The dashboard displayed progress bars moving from "Ride Requested" to "Driver Assigned" and then to "Confirmation Sent." If the Notification Service went down for a bit, the app kept running. The driver was still assigned, and the notification just waited in a queue until the service was back online to handle it.

Is EDA the Silver Bullet?

EDA is impressive, but it is not the right solution for every project. The speaker made it clear when to use it and, just as importantly, when to avoid it.

When to use EDA: Event-driven design works best when you need real-time responses, high scalability, and flexibility through decoupling. It is ideal for complex systems with many producers and consumers, or when integrating different platforms.

When NOT to use EDA: Setting up event buses and queues can make your architecture more complex. If you are working on a simple CRUD application, a small prototype, or an app that needs strictly ordered and immediate responses, it is better to use traditional request-driven architectures. In some cases, a simpler approach is best.

Doing it Right: Best Practices

If you are ready to start using EDA, here are some important best practices to help keep your systems stable:

Idempotency: This idea sounds complex, but it is simple. Make sure your consumers can handle processing the same event more than once without causing errors or creating duplicate records.
Error Handling: Networks sometimes fail. Set up retries for temporary issues, and use Dead Letter Queues (DLQs) to store events that cannot be processed, so you can review them later.
Scalability: Set up your event channels with partitioning and load balancing. This way, your system can handle more traffic smoothly when demand increases.
Monitoring & Logging: Since events move through different systems at different times, debugging can be hard. Use observability tools to track and visualize event flows, so you always know what is happening in your system.

Key Takeaways

Here are the main ideas I’m bringing back to my own projects after this packed session:

Stop the polling: Switching from always checking on progress to a setup that just waits for notifications can save a lot of computing power.
Decoupling is a superpower: If your backend services don’t depend on each other, teams can build, scale, or even have issues without affecting the whole app.
AWS does the heavy lifting: Tools like EventBridge, SNS, and SQS work like a post office and waiting room, so it’s easy to route and store messages safely.
Complexity isn't always the answer: Even though I enjoyed the session, EDA isn’t always needed. Simple apps don’t need this much engineering, and that’s totally fine.

Conclusion

In short, going to the last session at AWS Student Community Day Tirupati was a great way to wrap up an already fantastic event. Poobalan managed to take a complex architectural idea and explain it using real-life examples.

Event-Driven Architecture helps our systems act more like the real world by reacting to events as they happen, instead of needing constant updates. It takes a new way of thinking, but the benefits in scalability, cost savings, and resilience make it worth learning.

About the Author

As an AWS Community Builder, I enjoy sharing the things I've learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don't hesitate to get in touch! 🚀

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: When Systems Listen: Event Driven Architecture

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

The Future is Conversational: Analyzing Cloud Networks with GenAI

N Chandra Prakash Reddy — Tue, 16 Jun 2026 06:18:17 +0000

I had the fantastic opportunity to attend the AWS Student Community Day in Tirupati on November 1, 2025. The sessions were great and covered a wide spectrum of cloud subjects but one particular speaker particularly caught my attention.

The session was titled “The Future is Conversational: Analyze Cloud Networks with GenAI” and was led by Yeshwanth L M. Yeshwanth is a multi-talented individual in the tech community. He is a Solutions Engineer at Confluent, an active AWS Community Builder, a Microsoft Certified Trainer, an IEEE Volunteer, and the creator of the YouTube channel “Tech With Yeshwanth.”

His lecture handled a big pain point for cloud engineers and made it easier with Generative AI. This is what I took away from his highly entertaining talk.

The "Khatabook" Problem: Managing a Giant Network

Yeshwanth opened the discussion with a fantastic everyday analogy to explain cloud networking. Imagine your AWS network as a huge college hostel and you as the warden.

In this analogy, you have 10,000 pupils (your cloud servers), thousands of dormitories (your applications), and one main entrance (the Internet). As the warden your major job is to be aware of who is coming in and out of the main gate and to make sure the pupils are safe.

Let’s face it, keeping track of 10,000 students isn’t easy. At the main gate, your security guy logs everything into a big notebook. In the realm of AWS, this logbook is called a VPC Flow Log.

Now picture this logbook with 100 million entries a day! This leads to what Yeshwanth aptly termed the “Khatabook” (logbook) dilemma.

The Warden's Nightmare

This vast amount of data creates a terrible scenario. Suppose the college principal asks you a very precise inquiry. “Did anyone try to sneak into the girls' hostel (a secure server) at 2 AM last night?”

In old school environment your job? You have to go through 5,000 pages of the logbook with a pen and locate the answer. This is the “Old Way” that cloud infrastructure was. Technically this entails building complex, very precise SQL queries to sift through millions of logs.

Does this sound familiar? It’s sluggish. It’s unpleasant. And by the time you finally compose the appropriate query, get the answer, the intruder is already long gone.

The "What If" Moment

Here’s the thing… what if you didn’t have to create those complicated queries? What if you could just naturally ask a question?

Picture yourself pulling out your phone, texting an assistant: “Hey, show me all ‘REJECTED’ entries after 1 AM." Or, more college-campus relatable: “Which student (IP address) is ordering the most Swiggy or Zomato orders?”

This simple transition from writing hard-coded code to having a natural discussion is exactly where Generative AI comes in to revolutionize the game entirely.

Meet the "Super-Smart Warden": Amazon Bedrock

Yeshwanth’s answer to the “Super-Smart Warden” of the AWS ecosystem was Amazon Bedrock.

Amazon Bedrock is a fully managed solution that brings Generative AI into your cloud environment. Imagine giving your hostel a super brain It’s like having a Ramanujan, a mathematical genius, on your administrative staff. Instead of you scanning the book with a pen, this genius can read all 5,000 pages of your logbook in one second and just tell you the answer.

Traditional vs. Intelligent Architecture

To see what a major shift this is, we have to look at the warden's "Filing System".

The Traditional Architecture:

In a normal legacy arrangement, network traffic passes via your Virtual Private Cloud (VPC) and gets captured in VPC Flow Logs. The data is then dumped onto storage such as an Amazon S3 bucket or Amazon CloudWatch. You would normally use a technology like Amazon Athena to query the S3 data and then develop a visual Dashboard to interpret this. It works but it’s a lot of heavy lifting, ongoing maintenance and a lot of manual query building.

The Intelligent Architecture:

The current technique streamlines this pipeline entire. Your VPC logs continue to flow to S3 and CloudWatch. But the data is immediately streamed into Amazon Bedrock, not through Athena and constructing fancy dashboards. You just ask the AI “Is there any strange activity from someone I don’t know?” and it answers in a conversational manner.

The Magic Trick: How It Actually Works

You may wonder how it works behind the scenes? Is this a magic? Yeshwanth broke down the technical reality into a four-step approach.

Step 1: Getting the Logbook Pages

First of all, the system needs raw data. For example, a script calls a function get_flow_log_data. It is precisely the same as going up to the security officer and asking him for all the logbook entries from the last 24 hours. It extracts the raw text messages directly from Amazon CloudWatch Logs.

Step 2: Pre-Processing in Python

Even before you talk to the AI, the script gets clever. It executes a Python function called analyze_flow_logs_summary that uses the Pandas package. This is where you take the raw data and make a high level summary, automatically finding things like total records, top IPs, total bytes moved, etc.

Then it calls a method called format_flow_logs_for_bedrock to provide a nice, legible text "printout" of the first few log entries. Think of this phase as cleaning up your desk and summarizing your notes before your boss comes to see a report.

Step 3: Building the "Super-Prompt"

This is where things become fascinating. This is where the true magic happens. We don’t just ask a plain question to the AI. We feed one giant dossier of information into Amazon Bedrock, all at once. This “Super-Prompt” has three primary components:

The Role: We give the AI a persona by stating, "You are an AWS network expert."
The Summary: We provide the Pandas data we just created: "Here is a summary I already made: {Total Records: 5000, Top IP: 1.2.3.4...}"
The Raw Data: We feed it the actual log entries: "Here are the actual log entries: {1. 10.0.0.1 -> 10.0.0.2 [REJECT]...}"

Step 4: The "In-Context" Answer

Finally, we append the real question to the very end of this huge prompt. We add for example: "...Now, tell me this: 'Are there any suspicious activities?

Bedrock reads the pre-computed summary and the raw logs we just gave it. It uses that current context to locate the solution. Bottom line. The AI is not going out and searching a regular back-end database. It’s basically reading and understanding the huge chunk of material you just given it and giving you a human like answer.

Live Demo & Beyond Security

After discussing the principle, Yeshwanth walked into the AWS Console for a live demo to "catch some intruders" and how this architecture parses flow logs in real time. Seeing the AI immediately evaluate complex network traffic was super cool.

But the main point of the session’s message was much broader than merely AWS security. This log analysis architecture is just the beginning, the future is conversational. You can execute this very same four step “magic trick” on just about any data generating domain:

For a Cricket Analyst: You could feed ball-by-ball IPL data to an AI and ask, "What bowling pattern makes Virat Kohli get out most often?"
For a College Admin: You could upload raw campus Wi-Fi logs and ask, "Which classroom's Wi-Fi is the slowest?"
For Developers: You could take your user-click logs and ask, "Which part of my app is making users angry?"

Key Takeaways

If you only remember a few things from this session, here is what you need to know:

Goodbye to Complex Queries: You don’t have to write complex SQL anymore, nor search through millions of lines of text manually. With GenAI you can just ask natural questions to examine large datasets.
Context is Everything: The answer to making this work is not just asking a smart question. It’s creating a “Super-Prompt” that first sends Amazon Bedrock your summary data and raw logs so it gets the precise context it needs to offer a great answer.
Endless Possibilities: Yeshwanth used AWS network security for his example, but this conversational design works for anything. GenAI can be your analyst on call, whether it’s cricket statistics, college Wi-Fi, or app user behavior.

Conclusion

At the end of the day, going from writing rigid code syntax to having a simple discussion with your infrastructure is a big game shift. Generative AI is not just a pretty new toy to generate emails. It is a very practical, powerful technology that can be the ‘super-smart warden’ for your most complex operational data.

Attending this session at AWS Student Community Day was eye opening. It is getting easier fast to grasp complex systems. If you haven't yet played around with Amazon Bedrock or GenAI for your own data, I advise you to give it a try. Write a simple script, give it some logs and see what happens. The future is indeed conversational ! And all it takes to get started is understanding how to ask the correct questions !

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: The Future is Conversational: Analyzing Cloud Networks with GenAI

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

Demystifying Terraform: Bridging the Gap Between Infrastructure and Cloud

N Chandra Prakash Reddy — Mon, 15 Jun 2026 16:28:10 +0000

Let’s face it, tech events are thrilling, but can also be quite overwhelming. I went to AWS Student Community Day on 1st November 2025 in Tirupati. There were some great sessions all day long on different elements of cloud computing. But there was one particular session that had me totally immersed.

Senior Cloud Engineer & AWS Community Builder Keerthivasan Kannan took the stage to discuss about ‘Bridging IaC and Cloud’. He took Infrastructure as Code, a subject that could be scary, and made it consumable and logical.

I took notes and photos throughout his talk so that I could share these ideas. If you are a student or an aspiring cloud enthusiast, this blog will help you understand how you may control the cloud resources by using the code.

The Cloud Management Problem

If you’ve ever utilized a cloud provider, you undoubtedly started by logging in to the web portal. You went through menus, choose regions, set up networks and ultimately started a server. Does this sound familiar?

Manual clicking is fantastic for learning, but a nightmare in the real-world. Imagine you have to build up fifty servers, apply sophisticated security rules and reproduce that same setup for a testing team. Doing that manually is slow, frustrating and prone to human mistake.

This is where the idea of Infrastructure as Code, or IaC, comes in. You don’t click buttons, you just write simple config files that tell you exactly what you want. You provide those files to a tool and it will create the infrastructure for you.

Enter Terraform

Terraform was introduced by Keerthivasan as the IaC tool of choice in the session. But what's so special about it?

Not Just for One Cloud

You might be wondering if knowing Terraform limits you into one platform. The slides were clear: Terraform supports multi-cloud environments. It’s not AWS-only. It works with Google Cloud, Azure and dozens of other providers.

It's like a universal remote control. You don’t need a separate remote for the TV, the sound system, and the air conditioner, you use one standard remote to operate everything in your house.

The Core Workflow: Terraform Stages

Here's the thing: Terraform operates in a very predictable, logical sequence. The session broke this down into four main stages.

Init (Initialize)

Before you can build anything, Terraform needs to prepare its workspace. The init stage downloads the necessary plugins to talk to your specific cloud provider. Imagine you are about to cook a new recipe; this step is like going to the grocery store to gather all your specific ingredients.

Plan

This is your safety net.” When you run a plan, Terraform examines your code and informs you exactly what it’s going to produce, alter, or destroy. It doesn’t affect anything for now. This is like looking over the blueprint with your contractor before the concrete gets poured.

Apply

Run apply when you approve the proposal. That’s when Terraform really speaks to the cloud and makes your resources. This is when you really turn the stove on and start cooking (in our cooking example).

Destroy

When you don’t need the infrastructure anymore, the destroy command tears it all away cleanly. It’s critical for saving money on cloud bills after a project finishes.

Remembering the Past: The State File

To be fair, managing infrastructure requires a good memory. Terraform needs to know exactly what it has created so it can manage it in the future.

Tracking Changes

Terraform maintains a record of your infrastructure in what is called a “State file.” This document ties your code to the real-world resources that are running in the cloud.

Think of your state file as like a save file in a video game. When you quit the game and return tomorrow, the save file remembers which level you are on and what stuff you collected. Without that, you'd have to start from scratch.

Cloud Storage

The slides stressed that this state file should be stored in Cloud Storage, not on your local laptop. If you are working in a team , everyone has to have the same " save file " , so you do not mistakenly overwrite each other .

Keeping Things Organized: File Structure

As your cloud environment grows, your code will too. Keerthivasan shared best practices for structuring Terraform files.

Standardizing Files

You don't just write a huge wall of code . You break it down in logical files. The presentation also showed a typical directory for an EC2 (virtual server) deployment:

providers.tf: Tells Terraform which cloud to connect to (like AWS).
variables.tf: Stores configurable values so you can easily change them later.
locals.tf: Defines local, reusable names or tags within the module.
output.tf: Displays important information after the build finishes, like the server's IP address.

Real-World Architecture

We also viewed a practical architecture diagram of a website domain (www.mbu.asia). The diagram demonstrated Terraform provisioning an S3 bucket for storage, moving traffic through a network architecture, and ultimately arriving to EC2 instances. Properly structured files make complex setups like this much easier to manage.

Building with Blocks: Modules and DRY

This is where it gets interesting. As developers, we hate doing the same work twice.

The DRY Principle

DRY is an acronym for "Don’t Repeat Yourself. The workshop taught how to do this with Locals and Variables. If you need to apply the same “Project” tag to fifty resources, you don’t write it fifty times. You define it once as a local or a variable and then refer to it everywhere.

Terraform Modules

If you find yourself writing the same code for an S3 bucket or a Security Group over and over you can construct a Module .

Think of a module like buying a cake mix. Instead than weighing out flour, sugar and baking powder every single time you want to bake, you just grab the mix, add water and you are good to go. The presentation illustrated folder structures splitting ec2, s3 and sg (security groups) into reusable modules that may be called with a few lines of code.

Advanced Magic: Variables, Loops, and Logic

To make your infrastructure truly dynamic, Terraform offers several advanced features.

Variable Validation

Sometimes you want to restrict what inputs are accepted. The slides indicated a variable block for “environment” with a strict constraint. It did validation to ensure the environment name was just "dev", "staging" or "prod". If a user inputs “testing”, Terraform will error out.

Count vs. For_each

When you want to create multiple resources, you have two main options.

Count: The slide presented an example of Web Servers creation with count = 3. If you want exact replicas, this is great. Imagine ordering three simple pizzas the same for a party.
For_each: This example shows how to create different IAM users using a map. This is best if each object has unique qualities. Imagine you are ordering meals on Swiggy for three pals, one wants a burger, the other wants pasta and the third wants a salad. For_each handles those special orders well.

Dynamic Blocks

Security Groups control what network traffic is allowed in and out. The presentation included “Dynamic blocks” that automatically create multiple entrance rules. Instead of hardcoding each and every port rule, Terraform iterates over a list and dynamically constructs the rules.

Built-in Functions

Terraform is not simply resource declaration. It can conduct math and string manipulation. The functions slide was an overview of numeric, string and collection functions. One simple example provided was the ceiling function. Ceil(5.1) immediately rounds up to 6.

Conditional Statements

Finally, we looked at conditionals. The syntax shown was condition ? true_val : false_val. In other words, it is a simple decision-maker. The slide's example checked if a variable was empty. If it was empty, it assigned a default value; otherwise, it used the provided value.

Key Takeaways

If you are looking to start your journey with Terraform and IaC, here are the core lessons I picked up from the session:

Automation is Non-Negotiable: Moving away from manual console clicks to code isn’t just a “pro” move, it’s important for consistency and speed.
Trust the Workflow: Always follow Init -> Plan -> Apply The “Plan” stage is your best buddy since it prevents costly or damaging mistakes from happening.
The State File is Sacred: Think of your state file as a treasure map. You lose it, or corrupt it, and Terraform doesn’t know what it built anymore. Keep it on the cloud (like an S3 bucket) and not simply on your laptop.
Modules Save Time: Don't write the same code twice. Create modules that are “blueprints” of common resources like servers or databases that you can install instantaneously in new applications.
Logic Makes Infra "Smart": With variables, loops (count and for_each) and conditional expressions your infrastructure can be flexible to new needs without you having to rebuild the entire code base.

Conclusion

AWS Student Community Day at Tirupati was an enlightening event. There were a lot of good lectures during the day on everything from AI to Security but this one on “Bridging IaC and Cloud” really caught my eye because of how practical it was.

When you first look at Infrastructure as Code it can feel like a mountain to conquer. But watching everything broken down into basic files, logical steps and reusable modules made it feel much more like playing with Lego blocks than building difficult software.

Terraform is a skill that will serve you well whether you are just starting your cloud journey or looking to scale a project. My best advice? Don’t just read about it: install Terraform, get a free-tier AWS account, and try deploying your first S3 bucket using the apply command. There’s no better way to learn than to do it!

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: Demystifying Terraform: Bridging the Gap Between Infrastructure and Cloud

Date: November 01, 2025

Also Published On

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From MLOps to LLMOps: A Practical AWS GenAI Operations Guide

N Chandra Prakash Reddy — Fri, 03 Apr 2026 16:12:34 +0000

The vibe at AWS Student Community Day Tirupati on November 1, 2025, was different from what I thought it would be like. There were lots of students, cloud fans, and builders in the room. They were all there to learn, meet, and geek out about AWS. Throughout the day, there were several classes, and each one added something new.

One lesson, though, made me sit up and pay more attention. Raghul Gopal, a Data Scientist and AWS Community Builder (ML), walked up to the stage to talk about something that most people don't think much about: how do you run AI models in real life? Not just make them on a laptop and be happy about it; consistently test, watch, and scale them.

"Generative AI Operations: FMOps, LLMOps Integration with MLOps Maturity Model" was the title of the talk. When it was over, I had a whole new perspective on the AI/ML lifecycle on AWS.

The Question That Kicked Everything Off

"AWS gives you everything in one place to build ML models," Raghul said to start the talk, and it really hit the mark. But are we really using it right in production?"

Sense a pattern? A model can be trained by many teams. It's a whole different task to get that model to work reliably for a lot of real users.

To put it another way, making a great meal at home is one thing. It takes a lot of different skills to run a restaurant kitchen that feeds hundreds of people every day without any problems. That's what this meeting was all about.

What "ML in Production" Actually Means

Before getting into answers, the session gave us a really helpful list of questions that can be used as a litmus test to see if your machine learning setup is really ready for production:

Are your model's features (the pieces of data it uses to make predictions) kept separate and tracked correctly?
Is your model that you learned kept in a model repository or registry?
Is the model being watched all the time to make sure it keeps giving correct answers?
Is model lineage being kept? This is a list of which data made which version of the model.
Are there CI/CD pipelines (automated delivery systems) that move code from development to pre-production to production, with approval steps that need to be manned by manual?
Is testing done automatically in every environment?
Does ETL (Extract, Transform, Load) automatically load data so that machine learning engineers can start working on projects without haThe event also had a number of other great sessions, such as ones about cloud design, hands-on demos, and more. But this one helped me learn how to organise my thoughts in a way that I will use in all future AI projects.ving to wait for data teams?

There are a lot of people like you who answered "not really" to most of those questions. That's exactly what MLOps is meant to fix.

The Three "-Ops" You Need to Know

Let's be honest: the words can be hard to understand. It's simple like this:

MLOps (Machine Learning Operations): The process of putting standard machine learning solutions into production in a smart way. Examples include fraud detection models, recommendation systems, and churn prediction.
FMOps (Foundation Model Operations): Massive AI models like Claude or Titan are trained on terabytes of data with billions of parameters. This is an extension of MLOps for Foundation Models. FMOps includes use cases for making text, images, music, and videos.
LLMOps (Large Language Model Operations): A part of FMOps that is used to operationalise Large Language Models. This is the technology that makes chatbots, writing helpers, and coding tools work.

Imagine three rings stacked on top of each other. MLOps is the outer ring, FMOps is inside it, and LLMOps is in the middle. It doesn't matter what kind of AI model you run, all three work the same way.

The MLOps Maturity Model: Four Levels

Now things really start to get interesting. Raghul showed a four-level MLOps Maturity Model, which is a plan for how teams move from small tests to using machine learning on a large scale. It's kind of like getting better at a video game.

Level 0 - Initial Phase: Experiments and Ideas

At this point, data scientists are just looking around. To make and test models, they use Adobe SageMaker Studio (AWS's cloud-based ML IDE) or local tools like VS Code and PyCharm. This is what the technology stack looks like:

Amazon SageMaker: Core ML platform with Data Wrangler (data prep), Pipelines (automation), Feature Store, and Clarify (bias detection)
Amazon S3: Stores your raw ML training data
AWS Glue: ETL service - cleans and transforms data before feeding it to models
Amazon Athena: Run SQL queries directly on data sitting in S3
AWS Lambda: Trigger automated jobs and workflows
The event also had a number of other great sessions, such as ones about cloud design, hands-on demos, and more. But this one helped me learn how to organise my thoughts in a way that I will use in all future AI projects.
Code Repository: AWS had its own CodeCommit, but now most people use GitHub or Bitbucket to store and track their work.

That's fine; everything here is done by manual and by exploration. The beginning of every fully developed machine learning system.

Level 1 - Repeatable Phase: Automating the Workflow

From doing runs by manual, the team now goes on to automated pipelines. You don't have to re-train a model by manual every time because SageMaker Pipelines can do the data preparation, training, evaluation, and packaging for you. The SageMaker Model Registry is a central list of all your model versions that gets updated when new models are trained.

"I trained this once" became "every training run is tracked, versiThe event also had a number of other great sessions, such as ones about cloud design, hands-on demos, and more. But this one helped me learn how to organise my thoughts in a way that I will use in all future AI projects.oned, and reproducible."

Level 2 - Reliable Phase: Adding the Safety Net

This is the quality gate before going live. You introduce:

Automated testing: Unit tests, integration tests, and evaluation metrics that are specific to machine learning are all run immediately.
CI/CD Pipelines Using AWS CodePipelines and AWS CodeBuild to move code from development to pre-production to production, with approval steps that need to be manned by manual.
Different testing strategies based on how data arrives:
- Batch requests: Tested via Lambda and S3
- Real-time requests: Handled through Amazon API Gateway
- Streaming requests: Managed with Kafka and Amazon MSK

To be fair, this level demands real engineering discipline. But it's what separates a prototype from something you'd stake your business on.

Level 3 - Scalable Phase: Multi-Team, Enterprise-Scale

Everything from Level 2 is multiplied across different teams and machine learning solutions at the same time in the last level. New things added here:

Multiple data sources: NoSQL databases like DynamoDB and DocumentDB for different team needs
IAM (Identity and Access Management) to manage roles and permissions at scale
CloudFormation or Terraform for Infrastructure as Code - your entire environment defined in code, replicable in minutes
Your team can choose to use GitHub Actions or Jenkins instead of AWS CodePipelines if they already know how to use those tools.

What is the goal at this level? From idea to production in days instead of weeks, and use more than one option at the same time.

Making the Leap: MLOps → LLMOps

When you have a strong base in MLOps, moving on to LLMOps is easier than it sounds. The slide made it clear: "You can operationalise your basic LLM use cases from one environment to the next."

The ideas behind Dev, Pre-Prod, and Prod environments, CI/CD pipelines, manual approvals, and automated tests are all the same. Now you're working with Foundation Models instead of the old ML models, which is different. They're the building blocks you use to build on top of your MLOps skills.

Initial LLMOps: Picking the Right Foundation Model

This is where lots of teams get stuck. How do you pick from the dozens of LLMs that are out there? The lesson gave a framework that could be used right away.

Step 1: Know Your Use Case First

Make sure you know what you need before you choose a type. The things to look at are:

Open source vs. proprietary?
Commercial license compatibility
Model size: Small Language Model (SLM) vs. Large Language Model (LLM)
Speed and latency requirements
Context window size - how much text the model can process at once (measured in tokens)
Quality of the training dataset and how it applies to your area
Is the model fine-tunable with your own data?

Step 2: Navigate the Speed-Precision-Cost Triangle

The truth is that you can't have everything. Raghul showed this with a triangle that showed three objectives that were at odds with each other:

High speed → smaller model → lower precision → lower cost
Higher precision → larger model → lower speed → higher cost

In the case on the slide, three Foundation Models were put side by side. FM1 had the highest accuracy (5/5) but also the highest cost. FM3 was less expensive ($$), but it wasn't as accurate. When price was the most important factor, FM2 was chosen because it had the best mix of accuracy (4/5) and low cost ($). The best choice is always based on which triangle point is most important to you.

Step 3: Build a Prompt Catalog and Evaluate Systematically

Don't just pick a model and hope it works. The recommended process:

Prompt Engineers make good evaluation questions by following organised rules like CORS or Anthropic's instructions.
In a Prompt Catalogue, you can store those prompts. It's kind of like a Feature Store, but for prompts. With version control turned on, DynamoDB works well here.
GenAI Developers shortlist the top 3 Foundation Models based on those prompts
You can do structured evaluations in one of four ways, depending on the facts you have:

Accuracy metrics (when labeled data exists with discrete outputs — e.g., classification)
Similarity metrics like ROUGE or cosine similarity (for open-ended text outputs)
Human in the Loop (HIL): Using tools like Amazon SageMaker Ground Truth, human judges score model outputs by manual against set criteria.
LLM-as-judge: Feed outputs to a trusted, reliable LLM and have it rate the response with a score and explanation

The result is a clean evaluation scorecard, which means that you chose your model based on facts instead of your gut.

Building and Deploying Your LLMOps App

What do you do now that you've picked your LLM? Building the real app around it is the last step:

Frontend: Django, Flask, Streamlit (highly recommended for quick and clean prototypes), or React
Backend / LLM Provider: Amazon Bedrock, SageMaker JumpStart, or HuggingFace - depending on your model choice
Load Balancing and Auto Scaling to handle real-world traffic without hiccups
The same Dev → Pre-Prod → Prod pipeline from MLOps applies - always test your LLM in Pre-Prod before exposing it to end users

The architecture changes based on whether you're delivering at the edge or through a centralised group.

Key Takeaways

After this lesson, a few things really stuck with me:

Building a model is the easy part. Running it consistently in production, testing it, keeping track of its history, and being able to do it again is the real engineering work.
The MLOps maturity model is a journey, not a checklist. You can start at Level 0 if that's where you are now. You get to the higher levels bit by bit.
LLMOps is MLOps with a GenAI lens. You're a lot closer to LLMOps than you think if you already know how MLOps works.
Model selection should be data-driven. You don't have to guess or worry about which LLM to choose because of the prompt catalogue and organised evaluation method.

Conclusion

In the end, Raghul's talk made it clear that having the tools isn't enough; what counts is how you use them. From SageMaker to Bedrock to CodePipelines, AWS gives you a very full set of tools. But even the best tools can't fix a broken process if you don't think about things like testing, tracking, and being able to do the same thing again.

If you're a student just starting to learn machine learning, a developer looking into GenAI, or an engineer building real systems at work, you need to understand this operational layer. This is what sets people who play with AI apart from those who ship AI. The talk at AWS Student Community Day Tirupati taught me that there isn't as much of a gap between the two as most people think. You have to get on that growth curve somewhere and keep going up.

The event also had a number of other great sessions, such as ones about cloud design, hands-on demos, and more. But this one helped me learn how to organise my thoughts in a way that I will use in all future AI projects.

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: From MLOps to LLMOps: A Practical AWS GenAI Operations Guide

Date: November 01, 2025

Also Published On

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LamRAG: AI-Powered Feedback Analysis Using Amazon Bedrock

N Chandra Prakash Reddy — Sun, 29 Mar 2026 15:35:02 +0000

There were a lot of great talks at AWS Student Community Day Tirupati on November 1, 2025. But the one that really jumped out was "LamRAG: From data to constructive insights using Amazon Bedrock" by Rahul Kumar and Gokul Jangam.

It wasn't a normal slide-and-talk presentation. It was a live, step-by-step tour of a real product they made called Feedbackly, which is a platform for managing feedback. They showed how they improved it over time using Amazon Bedrock. The session was set up perfectly, with levels that built on each other. When it was over, I got a whole new way of looking at what generative AI on AWS can do.

Let me break it all down for you.

The Problem: Feedback Chaos and the 10/10 Trap

You know how messy it can get if you've worked on a team that does sprints. Many projects. A lot of project managers. Different schedules for sprints. Different approaches to get feedback from peers. There is no one spot to keep track of it all.

Feedbackly was made to solve just that: a single system for managing projects and getting peer input from team members every sprint. It's like a notebook that everyone in your engineering department can use to keep track of comments from every sprint.

But then there was a new problem that seems quite familiar: everyone started giving 10 out of 10 ratings. Everyone is "great." Everyone "did better than expected." The feedback loses its meaning. Not important. Not useful for real conversations about performance.

Sound familiar? That's where Amazon Bedrock enters the picture.

Why Serverless First?

Before getting into the AI aspects, the speakers made a quick but vital case for creating Feedbackly on a serverless architecture. This is why it made sense:

No Server Management - no patching, no provisioning, no babysitting servers
Pay Only for What You Use - no paying for idle compute between sprints
Automatic Scaling - handles bursts of feedback submissions without manual work
Faster Development - less infra, more features
Built-in Availability - AWS handles the redundancy
Focus on Business Logic - spend time on what actually matters to users

It's like renting a cab instead of buying a car. You don't have to worry about gas, insurance, or maintenance; you just get where you need to go.

Learning in Levels: The Session's Brilliant Structure

The session was set up as a series of five levels, from L1 to L5, with each level adding a new idea on top of the one before it.

L1: Bedrock Playground

The journey started with the Amazon Bedrock Chat Playground — a browser-based interface where you can experiment with multiple foundation models side by side, without writing a single line of code. It's literally a playground.

The presenters used the same feedback-classification prompt on three models at the same time: Llama 3.1 405B Instruct, Claude 3.5 Sonnet, and Command R. They wanted to see how each model reacted to the same input. The results were different for each of them. The terminology, the reasoning, the strictness, and the structure are all important. This is where it gets interesting: you need to choose a model, and the playground allows you compare them before you make a choice.

The model metrics were also interesting: Llama 3.1 had the largest latency (almost 18,000 ms), Command R was the fastest (around 1,591 ms), and Claude 3.5 Sonnet hit a sweet spot (about 4,799 ms) while giving the most structured, reasoned output.

L2: Prompt Engineering - One Word Changes Everything

AI is just as good as the directions you give it.

The presenters went over a good Prompt Template made in Amazon Bedrock's Prompt Management. It had five main parts:

Persona / Role - tell the model who it is
Action - tell it what to do
References - give it positive and negative examples to anchor its judgment
Variables - use placeholders like {{feedback}} for dynamic input
Output Format - ask for structured JSON so your application can actually parse the result

The prompt told the model to sort peer input into three groups: Reliability, Productivity, and Positive Energy. Then, it had to rank each group from 1 to 5. If there isn't enough background for a category, give it a -1.

After that, the most memorable demo of the whole session happened. They gave the identical report twice: "Person X has been productive and done the tasks as expected." What's the difference? One instruction suggested, "Be easy on the ratings." The other remarked, "Be strict with the ratings."

The outcomes were markedly distinct. One word. That's all it needed to change the AI's score. It's a strong reminder that you have to be careful when writing your prompt because it's the most important part of how your AI feature will work. Like you test your code, you should also test your prompts.

The Architecture: Lambda, RDS, and Bedrock Working Together

The basic structure of LamRAG is clean, serverless, and easy to understand:

The User sends a request
AWS Lambda receives it, validates the data, and calls both RDS and Bedrock
Amazon RDS acts as the data store, holding all sprint feedback
Amazon Bedrock takes that data, creates a query, and generates a human-readable summary

Lambda takes care of the orchestration. Bedrock is the smart part. RDS has the truth. Easy to use, works well, and is fully managed - no servers to worry about.

L3: Vector Databases and RAG

"RAG" means "Retrieval-Augmented Generation." In simple terms, you give an AI model access to your personal data instead of only what it already knows from training. This way, its replies are based on your specific situation instead of general internet knowledge.

The lecturers utilised a smart example about fruits to describe how vector databases work. Think about how you would describe an apple not just by its name, but also by its colour (1.0), sweetness (7), sourness (4), crunchiness (8), and shelf life (0.5). That list of numbers is a vector. The vector for an Orange is [0.8, 6, 8, 2, 1.0]. A vector database keeps these embeddings and uses arithmetic to locate items that are comparable to them, not keyword matching.

When you ask for "list some red-colored fruits," the database looks for vectors that are closest to the numbers that represent "red" and "fruit." That's semantic search — and it's far more powerful than a simple text search.

Feedbackly integrated feedback data stored in Amazon S3 to a Bedrock Knowledge Base. This let users to choose how to separate and index documents for quick retrieval by using configurable chunking schemes such as default, fixed-size, hierarchical, semantic, or no chunking.

L4: Agents - Smart, Conversational, and Privacy-Aware

Three months after the first Feedbackly launch, two new problems came up: Admins vs. Users access control and a problem with how feedback was being shown. Not everyone should be allowed to see what other people have said.

The solution? Bedrock Agents.

An agent is like a smart helper that can think, plan, and do things. The speakers built an agent called sls-days-2024-lamrag based on Claude 3 Haiku. The agent has the following settings:

Action Groups - A Lambda function that takes two arguments: the type of inquiry (self or others) and the email address of the person who asked for it.
Knowledge Base - linked to the Feedbackly S3 data, with the order to get data depending on the user's email address
Privacy Logic - The Lambda checks to see if you're asking about yourself or someone else and blocks access right away if it's not allowed.

The live demo was really cool. When an unauthorised email sought to get someone else's comments, the agent said, "You are not authorised to access that information." But when a user asked for their own comments, they got a long, conversational summary: "Sandeep is a very productive, dependable, and positive team member who always gets great results."

That's not only smart AI; it's also responsible AI. Privacy built into the design.

L5: Keeping the Knowledge Base in Sync

A knowledge base is only useful if it is up to date. If new feedback is sent in but the knowledge base isn't updated, the agent keeps answering questions with old information, like a librarian using last year's catalogue.

The presenters talked about this directly with L5, which kept the Bedrock Knowledge Base up to date. The knowledge base needs to re-sync every time fresh feedback is processed and uploaded to the S3 bucket (sls-days-2024-lamrag) so that the agent always has the most up-to-date information. It's a phase that is easy to forget, but it is very important for production systems.

Final Results: The AI Chat Assistant in Action

The end result was a fully functional conversational AI Chat Assistant that was built right into Feedbackly. A member of the team could type a question in plain English and get structured, data-backed answers.

For instance, asking, "What are the average ratings for this worker?" came back:

Positive Energy: 3.9/5 - Very Good
Productivity: 2.5/5 - Below Average
Reliability: 2.7/5 - Below Average
Overall: 3.1/5 - Average

Along with specific strengths (such being good at code reviews, managing time, and mentoring) and areas where they need to improve. No more empty "10/10 across the board" ratings. Instead, there will be real, detailed, AI-backed analysis based on real peer input over time.

Bonus: From Idea to App with Claude Code

The last part of the session was a bonus that honestly blew everyone away. The speakers showed how they used Claude Code, Anthropic's agentic coding tool, to construct the full-stack feedback analyser.

The workflow was deceptively simple:

Create a TASKS.md file — describe the application in plain English (tech stack, features, database setup, everything)
Tell Claude Code to refer the file and build the app
Deploy the app

The TASKS.md file listed all the parts of the stack: For the frontend, we use React, Vite, and Tailwind CSS. For the backend, we use AWS Lambda (Node.js 22). For AI, we use Claude 3.5 Sonnet via Bedrock. For the database, we use PostgreSQL on RDS. For the infrastructure, we use AWS SAM. Claude Code asked a few questions to make sure he understood, and then he made a full implementation plan. This plan included AI functions, security functions, and even privacy functions that replace colleague names with [Colleague] in queries that include more than one user.

This led to the slide that made everyone think the most: "Does this mean we don't need to learn coding anymore?"

Honestly, no, it doesn't. The skill itself is changing. It's more important than ever to know about design, what good code looks like, and how to look at AI-generated output with a critical eye. The startups that the speakers talked about - Cursor, Midjourney, Lovable, and Eleven Labs - were all started by small groups of people that employed AI to help them work faster, not to replace them.

Key Takeaways

This was one of the most useful AI sessions I've ever been to. This is what I'm taking with me:

Prompt engineering is a real, learnable skill. The word "lenient" vs. "strict" makes all the difference. Check your prompts the same way you check your code.
RAG makes AI relevant to your world. Foundation models are not specific. RAG makes them fit your data, your people, and your situation.
Agents add intelligence and access control together. Bedrock Agents can think about who is enquiring before they decide what to respond.
Serverless + Bedrock is a genuinely practical stack. You can send AI features that are ready for production without having to manage a single server.
AI amplifies builders, it doesn't replace them. The actual expertise of this time is knowing what to develop and how to direct the AI.

The Amazon Bedrock Chat Playground is the best place to start if you want to attempt any of this yourself. You don't need any code; simply open your browser and start playing around.

Conclusion

The LamRAG session at AWS Student Community Day Tirupati reminded me that the finest tech speeches don't only teach you ideas; they also show you a real problem, a real solution, and a genuine way to move forward. In short, here's the broad picture:

Generative AI on AWS is approachable - The Bedrock Playground enables anyone start experimenting without having to write any code.
The journey from a simple prompt to a full RAG-powered agent is incremental - You don't have to develop it entirely at once. Start with a small part and add more intelligence as you go.
Privacy and access control aren't an afterthought - Bedrock Agents let you change how the AI reacts right away.
AI-assisted development tools like Claude Code are changing the speed of building - faster than ever from idea to app in use
The best time to start learning Amazon Bedrock is right now - The tools are well-developed, the documentation is good, and the community is developing quickly.

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: LamRAG: AI-Powered Feedback Analysis Using Amazon Bedrock

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

Building a Private GPT with AWS Bedrock: A Deep Dive

N Chandra Prakash Reddy — Tue, 03 Feb 2026 14:18:57 +0000

How It All Started

On November 1, 2025, I attended the AWS Student Community Day in Tirupati, and honestly, one session significantly changed my perspective on developing AI applications. Amin Ali, a Software Engineer at Target Australia and an AWS-certified Solutions Architect, delivered a presentation titled "Generative AI in Action." The session concentrated on how to build your own private GPT using AWS Bedrock.

The reality is that we've all interacted with ChatGPT, haven't we? Have you ever considered how you might develop something similar that operates with your organization's internal files, retains all the information in your own cloud, and avoids transmitting any data to external APIs? This session focused on that topic, and I'm thrilled to share my insights.

Why Build Your Own Private GPT?

You might wonder why you can't just use tools like ChatGPT or Claude directly. That's a good question, and I can explain with an example.

Data Control: Your Information, Your Rules

Imagine you run a hospital and need an AI to help doctors find patient information fast. Would you want to give private medical data to a public AI service? Probably not. By creating your own private GPT, you control your data within your AWS system, so you don't need outside services. Your private information stays safe in your Virtual Private Cloud. You'll also have full records of everything that happens, which helps follow strict rules, and no data is ever shared with anyone else.

Customization: Make It Yours

Public AI models usually learn from information found online. But what if you need answers that are only about your company? A private GPT can give answers based on your own files and knowledge, allowing it to sound like your company. It's like a customer helper who knows everything about your products. It can also connect easily with your company's internal systems, such as employee records, product lists, or instruction guides.

Understanding Generative AI

First, let's be sure we all know what generative AI is. Amin explained it well with a three-step process.

Input Processing: You tell the AI what to do, like giving it words, pictures, or code. Big AI programs like GPT, Claude, or Llama, which have learned from tons of information, handle these instructions.

Foundation Models: Regular machine learning finds patterns in data to guess things, like if an email is spam. But large language models create brand new things from nothing. That's why they are called "generative."

Creative Output: The AI makes new words, pictures, code, and other things that didn't exist before. It's like having a helpful assistant that can create, write code, and solve problems.

Why Generative AI Is Exploding Right Now

Now is a good time to learn about this new technology. Generative AI is popular for these reasons:

The AI Revolution: Many advanced AI models like ChatGPT, Claude, Mistral, and Llama 3 are becoming more common, and they are significantly altering how we work and create.

Enterprise Adoption: AWS is assisting companies in building reliable and secure generative AI systems that can manage a lot of users. Businesses don't have to build everything from scratch anymore.

Industry Impact: Smart automation is transforming industries like healthcare, shopping, schools, and banking. Think about doctors available 24/7 or educational tools that adjust to each student's needs.

Global Scale: All kinds of companies, from small new businesses to large established ones, are using generative AI a lot, and this trend is growing quickly.

Real-World Use Cases

Let's discuss practical applications. This isn't just theory these are real scenarios where private GPTs genuinely make a difference.

Customer Support: AI chatbots provide 24/7 personalized assistance that is as effective as a human helper. Imagine a customer checking your return policy late at night and getting a fast, accurate response that aligns with your actual rules.

Report Generation: Automatically generating business reports, summaries, and documents. Rather than spending a lot of time compiling quarterly reports, the AI collects the data and creates well-organized documents on its own.

Document Q&A: An internal knowledge base that enables quick answers based on company documents. Picture a new employee asking, "What is our vacation policy?" and the AI quickly finding the answer in the employee handbook.

Developer Assistants: Generating code, fixing errors, and writing explanations to speed up the development process. Programmers can ask, "How do I connect to our database?" and receive code examples that work with your system.

The Live Demo: MBU Virtual Assistant

This is where things became truly intriguing. Amin demonstrated an AI assistant he developed for Mohan Babu University.

The interface was smooth simple chat design built with React that appeared both professional and user-friendly. When a question such as "What programs does MBU offer?" was asked, the system would process the query through the chat, scan the uploaded university documents using vector similarity, generate a contextually appropriate response with AWS Bedrock, and deliver the answer in real time.

The demo showcased the chat interface with live-streaming responses and Bedrock integration performing real-time searches on the knowledge base using vector search examples. Powered by Amazon Bedrock, the responses flowed smoothly, creating the impression of conversing with an intelligent assistant.

How the Architecture Works

Let me guide you through the system's structure. Don't worry I'll keep it simple and easy to understand.

The User Journey

Here's the journey of a user's query through the system:

User Interaction: You enter a question using the React-based web interface.
API Gateway: Your request reaches AWS API Gateway, which serves as the main entry point (REST endpoint).
Lambda Function: API Gateway forwards the request to an AWS Lambda function imagine this as the central component that manages all operations.
Bedrock Integration: Lambda calls AWS Bedrock's Knowledge Base to perform a vector search and uses the Large Language Model to create the response.
Response Delivery: The answer then travels back through the same route and appears on your screen.

Architecture Components

Amin broke down the architecture into clear layers using a complete serverless setup:

This serverless configuration means you don't need to manage any servers. AWS handles scaling and security automatically.

Setting Up the Knowledge Base

This is where the real action takes place. How can you train the AI on your particular documents?

Data Preparation Pipeline

The process follows three key steps:

Upload PDFs: Your documents (manuals, policies, guides) are uploaded to an S3 source bucket.
Extract & Chunk: The system pulls out the text and divides it into smart segments for the best vector embeddings.
Generate Embeddings: AWS Bedrock generates vector embeddings that are saved in an S3 vector bucket for similarity searches.

Vector Integration

Does this ring a bell? Imagine vector embeddings as a library catalog. Instead of just matching exact words, vectors understand the meaning behind them.

The Bedrock Knowledge Base connects to an S3 vector bucket with 2024-dimensional embeddings. When you ask a question, the system finds relevant sections of documents using cosine similarity, searches for matching info in real time, and provides answers based on the most important details.

The setup involves connecting to an S3 vector bucket, automatically updating indexes, and using Bedrock directly – all these components work together seamlessly.

Query Handling Flow

Let's trace a question through the whole system.

Step 1 - User Query: You type a question into the React chat interface, just like you would in any messaging app.

Step 2 - Vector Search: Lambda checks the Bedrock Knowledge Base for relevant document sections. The system finds the 3-5 most relevant pieces of information from your document collection.

Step 3 - Context Response: The Large Language Model creates a response based on the retrieved context and sends it back to the user interface. The answer is based on your actual documents, not general internet information.

Best Practices and Optimization

Building the system is one thing, but making it run well is different. Amin shared important tips for improvement.

Prompt Engineering

Writing good prompts helps get better and more useful answers.

Use clear, specific instructions. Instead of saying "tell me about products," say "list the top 3 features of Product X based on our product documentation."
Add context and examples to your prompts.
Improve your prompts based on the responses you get, prompt engineering is a process that requires adjustments.

Performance Optimization

Speed and cost are important in production

Allow real-time responses so users can see answers as they come in, which improves the user experience.
Use tokens wisely, keep prompts short since tokens cost money.
Set up caching for frequently asked questions.

Key Takeaways

After witnessing this in action, three key points caught my attention:

AWS Bedrock: Secure and Flexible: AWS Bedrock provides a secure and flexible foundation for developing private AI solutions. You receive strong security features without needing to build everything yourself.

Production Ready: Generative AI is now ready for business use and has demonstrated real results that justify the investment. This is no longer just experimentation – companies are implementing these systems in their daily operations.

Easy Implementation: Building a private GPT is simpler than ever with managed services. You don't need to be a machine learning expert to get started.

Conclusion

After leaving that session, I realized that AI isn't just about using ChatGPT. It's about building smart systems tailored to your specific needs while keeping your information secure.

The AWS Student Community Day in Tirupati had many great sessions, but this one impressed me because it demonstrated how to turn an idea into a working demo. Whether you're making a company assistant, a customer support bot, or a documentation helper, the architecture Amin presented provides a solid foundation.

In summary, if you're thinking about adding AI features to your apps, AWS Bedrock makes it simpler than you might expect. With managed services, strong security, and flexibility, you can focus on solving your specific problem instead of building the entire AI setup yourself.

I hope this explanation helps you understand how private GPTs work and inspires you to create your own. The future of AI will be private, secure, and tailored to your personal needs.

About the Author

_As an AWS Community Builder, I enjoy sharing the things I've learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don't hesitate to get in touch! 🚀

🔗 Connect with me on LinkedIn_

References

Event: AWS Student Community Day Tirupati

Topic: How AWS Lambda and Fargate Change the Way We Build Apps

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

How AWS Lambda and Fargate Change the Way We Build Apps

N Chandra Prakash Reddy — Thu, 08 Jan 2026 13:02:08 +0000

Attending the AWS Student Community Day in Tirupati at Mohan Babu University was a special experience for me, not just as someone who participated, but also as an AWS Community Builder and someone who enjoys sharing cloud knowledge with beginners and other interested people. The session titled “From Code to Containers: Understanding Serverless Beyond Lambda” by Avinash Dalvi stood out to me right away because it offered something many of us are looking for: clear guidance on when to use Lambda and when to consider other options, especially containers.

Setting the stage: Why “serverless beyond Lambda”?

The session began by discussing the main topic: moving from writing code to using containers, but still keeping the serverless mindset. The key point was that serverless is more than just using functions like Lambda. It’s about thinking in a way that lets you focus on your code, while AWS handles the rest of the work behind the scenes.

The speaker introduced himself as a leader of an AWS User Group Bengaluru and an AWS Community Builder, which made everyone in the room feel confident that the talk would be based on real experiences, not just theory. He focused on helping us understand where Lambda works well, where it might not be the best choice, and how services like AWS Fargate can step in when Lambda can’t handle the job.

The serverless mindset shift

From “I need a server” to “I need to run code when X happens”

One of my favorite parts of the talk was the idea about changing how we think. Usually, people think, “I need a server to run my code.” That way of thinking leads to:

Spending money on server capacity even when traffic is low most of the time
Taking care of the infrastructure, like updating software, fixing security issues, and keeping the system safe
Paying for servers that aren’t being used
Manually adjusting the server size when traffic goes up or down

In short, you end up spending too much time watching over servers instead of working on your app.

The serverless approach changes this by focusing on: “I need to run code when something specific happens.”
That something could be a file being uploaded, an API request, a scheduled task, or an event from another service. Then:

You tell AWS what your needs are, like how long the code runs, how much memory it uses, and how long it can wait for a response.
AWS handles all the setup and management of the hardware and software needed.
You only pay for the time your code actually runs, not for the time it's sitting idle.
When more events happen, the system automatically grows to handle them without you having to do anything.

This change in thinking is what really makes serverless technology powerful, whether you're using Lambda functions or containers in a serverless setup.

Pizza, anyone? Explaining serverless with food

Make it yourself vs order pizza

To make it easier to understand, the speaker used a pizza example. Let’s imagine two choices for dinner:

Option 1: Make it yourself

You buy ingredients
You prepare everything
You cook, serve, and clean up

Option 2: Just order pizza

You specify what you want
Someone else handles the kitchen, oven, and cooking
You pay for the pizza, not for owning a restaurant

Traditional servers, like EC2, are like cooking at home. You take care of the kitchen (server), keep the oven running (patching, updates), and you pay for it all the time, even when you’re not cooking. You have full control, but you also have to take care of everything yourself.

Serverless is like ordering pizza. You just give your order (code), say what you want (toppings, size, base), and AWS handles the rest. You only pay for what you eat, not for keeping the kitchen open all day.

Ordering your serverless pizza

The session continued by using the analogy to explain how to build a serverless application.

Base (Runtime/Language)

Choose Python, Node.js, Java, Go, .NET, Ruby, etc.
Or “bring your own base” using containers when you need something custom.

Toppings (Resources)

RAM from a small amount up to several GB
Temporary storage space
CPU that scales with memory

Size (Code Complexity)

Small: simple functions
Medium: moderate logic
Large: complex applications

When should it run?

Immediate on events (like file uploads)
Scheduled (cron-style jobs)
On-demand via API calls

This helped the students in the room better relate their daily experiences to the decisions made in cloud computing.

Lambda in action (and where it struggles)

A simple Lambda example: image resizer

To make it more clear, there was a slide that showed a Lambda function used to resize images that were uploaded to S3. The code used was:

boto3 to talk to S3
PIL (Python Imaging Library) to open and resize the image
A lambda_handler function that:

Reads the file details from the S3 event
Downloads the image to /tmp
Creates a thumbnail
Saves the processed image back and returns a success status

This kind of situation is exactly where Lambda works best: for small, trigger-based, short-running tasks like image resizing, making thumbnails, simple API support, and light data processing.

When Lambda hits the wall

But Lambda isn’t without its limits, and the speaker was very open about that. There was a slide called “When Lambda hits the wall” that listed situations like:

Uploading a video → Lambda works great
Generating a thumbnail → Lambda is perfect
Transcoding a full video → Lambda fails

Why does it fail here? Because:

Video processing might take 45 minutes
Lambda has a hard timeout limit (15 minutes in the slides)
You may need more control over the environment
Larger dependencies or special tools may be required

So even though serverless technology is strong, you still need to choose the right tool within that serverless environment. That’s where AWS Fargate comes in.

Lambda vs Fargate: same pizza shop, different options

Comparing Lambda and Fargate

One slide showed a table comparing Lambda with Fargate using the same restaurant analogy:

What it is

Lambda: standard menu
Fargate: custom recipe

Base options

Lambda: pre-set runtimes
Fargate: any container image

Customization

Lambda: limited to what the menu supports
Fargate: fully customizable environment

Execution time

Lambda: up to the configured limit (15 minutes in the slide)
Fargate: effectively unlimited for long-running tasks

Code size

Lambda: limited package size
Fargate: no strict limit; your container image can hold more dependencies

Use case

Lambda: quick, short functions
Fargate: long processes, heavy workloads

Cold starts

Lambda: can happen
Fargate: more consistent performance once tasks are running

The bottom line? Lambda is like quickly ordering from a standard menu, while Fargate lets you bring your own recipe and ingredients but still avoids managing the kitchen yourself.

Fargate – bring your own recipe and enjoy the freedom

Another slide described Fargate as “Bring Your Own Recipe”:

You package your app into a container (Docker image)
AWS runs it for you without asking you to manage servers

Then came “Fargate – The Freedom” with three angles:

More control

Any programming language
Any runtime version
Custom OS-level dependencies
Specific tools and libraries you can’t easily run in Lambda

More capacity

Run for hours or days
No 15-minute limit
Much higher memory and CPU options

More use cases

Long-running processes
Legacy applications that expect a traditional environment
Microservices with specific runtime needs
Batch jobs and background processing

This made it clear that Fargate is still “serverless” in the sense that you don’t manage servers, but you get container-level flexibility.

Architecture patterns you can try

Practical patterns from the slides

One of the most helpful slides showed different architecture patterns that students could try at home. Some examples included:

Event-driven API

S3 upload → Lambda → DynamoDB
Great for things like uploading documents and storing metadata.

Scheduled jobs

EventBridge (cron) → Lambda → process data
Perfect for nightly reports, cleanup jobs, or scheduled notifications.

Microservices

API Gateway → Lambda or Fargate → backend services
Useful when building modular, independently deployable services.

Data pipeline

S3 → Lambda (trigger) → Fargate (heavy processing) → S3
A strong pattern when you need to combine quick triggers with long-running tasks.

Webhook handler

GitHub/Stripe → API Gateway → Lambda → action
Common for reacting to external events like payments or code pushes.

These patterns link what students learn in class to actual projects they might work on, like building apps for school, starting their own business, or doing internships.

When NOT to use serverless

Being honest about trade-offs

One slide caught my attention because it said: “Be honest – serverless isn’t always the best choice.” That’s a key point, especially for newbies who are excited about a fresh new idea. The slide listed some situations where serverless might not be the right fit:

When you have steady and high traffic that could be handled more affordably with always-on infrastructure
When you need a lot of control over the environment at a lower level
When tasks run for a long time and go beyond what a serverless function can handle
When you aren’t sure about your needs yet and might complicate things by using too many managed services too early

In the end, serverless is just another tool. The real skill is knowing when to use it and when something else, like containers or even basic EC2 instances, would work better.

Lambda vs Fargate: a simple decision tree

A mental model for choosing

There was also a clear decision tree to help choose between Lambda and Fargate. The simplified way of thinking about it went like this:

Do you need to run code at all?
Will it finish in under 15 minutes?

If no → consider Fargate for long-running processes with a custom environment.

If yes, is it a standard runtime (like Python, Node.js, etc.) with manageable dependencies?

If yes → Lambda is usually the fastest and cheapest option.
If no → again, Fargate or another container-based approach likely fits better.

This kind of straightforward flow is really useful when you're building your first few architectures and aren't sure which service to use.

Key takeaways from the session

The slide summary and my own reflections

The final “Key Takeaways” slide put everything together with points like:

Serverless is like ordering pizza instead of running your own restaurant
Understand Lambda vs Fargate instead of blindly choosing one
Focus on code, not servers
Pay for value, not idle time

For me personally, the biggest takeaway was that “serverless” isn’t just for Lambda. You can think serverless even when using containers, as long as AWS takes care of setting up and scaling your resources, and you focus mainly on your application.

Conclusion

This session at AWS Student Community Day in Tirupati was really helpful because it reminded me that good tech talks don't just list features, they change the way you understand things. The pizza example, the open talk about the limits of Lambda, and the introduction to Fargate as a strong "serverless containers" option made the topics easier to grasp, especially for students who are just starting to learn about these services.

As an AWS Community Builder, events like this keep me motivated because they show how quickly curiosity can turn into actual projects when you get the right examples and ways of thinking. If this topic interests you, try picking a small use case from your own life, like processing images from your app or running a scheduled cleanup, and try building it using Lambda or Fargate. Getting your hands on these tools will teach you more than any presentation ever could.

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS Student Community Day Tirupati

Topic: How AWS Lambda and Fargate Change the Way We Build Apps

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

How Serverless & Community Can Transform Your Career

N Chandra Prakash Reddy — Tue, 06 Jan 2026 13:32:30 +0000

When I walked into the Dasari Auditorium at Mohan Babu University on November 1st, 2025, for AWS Student Community Day Tirupati, I knew I was in for something special. The room was full of energy, with more than 400 students+ and others gathering to learn about cloud technologies. But one session stood out to me, Srushith Repakula’s talk on "How Serverless & Communities Changed My Career, and Can Change Yours Too."

What made this session really special wasn’t just the technical stuff. It was the open conversation about failing, the value of staying curious, and how a mistake that cost ₹4 lakh became the beginning of a great journey that led to becoming an AWS Serverless Hero.

Understanding Serverless: From Cars to Code

To be honest, when you hear the term "serverless," it sounds strange. After all, if anything depends on servers, how can it be server-less?

Here's the thing: Srushith made everything make sense with a clever analogy. Consider how automobiles have changed over time:

Traditional Servers = Buying a Car: Whether you drive it or not, you own it, accept full responsibility for it, maintain it, and pay for it.
VMs/Containers = Renting a Car: You share some responsibility, lease it, and customize it.
Serverless = Ride-Sharing (like Uber or Ola): It is entirely on-demand, requires no maintenance, and you only pay for each journey.

That final alternative is exactly how serverless computing operates. You only pay for the real time your code runs; the cloud provider manages all the infrastructure and dynamically distributes resources. Your funds won't be wasted on idle servers. No hassles with capacity planning.

Why Serverless Matters (Especially for Beginners)

"Why should I care about serverless as a student or early-career developer?" may be on your mind. Srushith outlined four strong arguments:

Learn Faster: Get started right away by writing code and deploying it without setting up servers or infrastructure.
Build Quicker: Within hours, not days, turn your hackathon ideas into functional apps.
Pay Less: You can explore without worrying about huge fees because there are no idle costs.
Scale Smarter: Your app won't break during that viral moment since AWS automatically manages traffic spikes.

The Journey: From Zero to Serverless Hero

My First Lambda: A Beautiful Disaster

The intriguing part of Srushith's story began at this point. Imagine the following scenario: "A chatbot challenge, a new laptop, and zero clue about AWS."

Do you recognize this? Most of us begin there. He didn't fully grasp the fundamental ideas before he started developing something. Next was "The ₹4 Lakh Mistake."

He experimented out of curiosity, but without the right safeguards, he unintentionally established an endless loop that resulted in a huge AWS cost. To be fair, this was an emotional as well as a financial setback. Here's what he discovered the hard way, though:

Understand before you deploy: Before putting your code into production, understand what it does.
Set budgets and CloudWatch alarms: Always establish financial safeguards
Curiosity is great, guardrails are greater: Take risks, but guard against costly errors.
Failure is just tuition you pay for learning: You learn something worthwhile from every error.

From Failure to Community

That ₹4 lakh error might have put a stop to his AWS career. Rather, it sparked something powerful. This is the course of his transformation:

The Panic (2016): Accidentally produced an endless loop, but it sparked sincere interest in fully understanding AWS

The Search (2016): Learned about AWS User Groups while digging deeply into blogs, videos, and documentation to figure out what went wrong.

The Meetup (2017): Met incredible builders at his first AWS meetup, told his story of failure, and connected with others who had experienced similar things.

The Belonging (2018): His community journey really started when he began volunteering, giving speeches, and lending a hand to others.

To put it another way, his failure served as a gateway to the community. And everything altered as a result.

Real-World Impact: Serverless at KonfHub

Srushith spoke on more than just theory. He explained why serverless works for KonfHub, a technical meeting platform built solely on AWS, in his capacity as Head of Engineering:

AI-Powered Networking Suggestions

Problem: Attending conferences by hand is ineffective and causes people to lose out on important contacts.

Solution: Using Amazon Bedrock and AWS Lambda, they developed an AI-powered recommendation system that instantly offers relevant connections.

Imagine having a smart assistant at every conference that knows exactly who you should meet according to your goals, role, and hobbies.

Why KonfHub Chose Serverless

Scale Effortlessly: Manage over 80,000 people at peak registration times with no downtime.
Pay for What You Use: Cost is equal to real consumption, not idle server capacity.
Secure by Design: IAM policies and managed infrastructure lower security overheadThe
Build Fast: Their distinctive selling proposition is developer velocity, which allows them to launch improvements fast without infrastructure delays.

The Evolution: How We Got Here

When you consider the development of computing infrastructure, it becomes easier to comprehend serverless:

Physical Machines: Needed a lot of guessing to plan, stayed on-premises for many years, required big money upfront, took a long time to set up, and didn't offer much new or creative ideas.

Virtual Machines: Made systems more independent of specific hardware, sped up the process of setting up new systems, lowered costs by sharing resources (changing from buying equipment to paying for usage), improved ability to grow, and made it easier to adapt quickly.

Containers: Allowed apps to work on any platform, provided the same environment every time they run, used resources more efficiently, made it quicker to deploy new features, kept processes separate to avoid issues, and started up in just seconds.

Serverless: Automatically adjusts resources as needed, handles system failures automatically, charges based on actual usage, requires no extra maintenance, and lets teams create new ideas without being limited by the underlying setup.

Each step made it easier to manage the system, so developers can concentrate on building good apps instead of worrying about the infrastructure.

Key Takeaways: Your Roadmap Forward

Srushith concluded with three strong principles that can help any cloud enthusiast on their journey:

Curiosity: Where It All Begins

Start small. Try things. Fail. Learn.
Curiosity fuels creativity, not perfection
Don't wait until you "know enough" to start experimenting

Community: The Fuel That Keeps You Going

Find your people and learn together
Share your wins and your mistakes, both teach others
Communities help you learn more and open up new opportunities you never thought possible.

Serverless: The Enabler of Scale

Build fast, fail safely, scale effortlessly
Innovation without the infrastructure overhead
Perfect for students, startups, and teams wanting to move quickly

My Reflections as an AWS Community Builder

Attending this session made me remember why I love AWS and community events so much. As someone who helps build the AWS community, I’ve seen how powerful these experiences can be, not just for learning new skills, but also for gaining confidence, finding mentors, and discovering new career paths you never thought possible.

The AWS Student Community Day in Tirupati had many great sessions, but Srushith’s talk really stood out because it was honest, practical, and hopeful. It showed that failure isn’t the end, it’s just part of the process.

For Aspiring AWS Enthusiasts

If you’re just starting your cloud journey, here’s what I recommend:

Don’t be scared to try things out, just make sure you set limits to stay safe. Join your local AWS User Group, they can help you grow much faster. Start with serverless technology, it makes it easier to get started and lets you focus on solving problems instead of managing complex systems. Share what you learn, even if it seems simple, someone else might be exactly where you were before and need to hear your story.

In the end, your cloud journey is about more than just getting certifications or technical skills. It’s about staying curious, being part of a community, and having the courage to keep going even when things don’t go perfectly.

Conclusion

Srushith's story shows that a big mistake, like spending ₹4 lakh, can turn into a great learning experience. AWS Serverless helps you turn ideas into real projects without worrying about the complicated parts of building infrastructure. The AWS community is ready to help you along the way. So, set up budget alerts, write your first Lambda function, and start your journey today. Your path to the cloud starts with being curious, not perfect.

About the Author

🔗 Connect with me on LinkedIn_

References

Event: AWS Student Community Day Tirupati

Topic: How Serverless & Community Can Transform Your Career

Date: November 01, 2025

Also Published On

AWS Builder Center

Hashnode

Voice Agents with Amazon Nova Sonic

N Chandra Prakash Reddy — Tue, 30 Dec 2025 12:59:02 +0000

I went to the AWS User Group Chennai Meetup on September 27, 2025, and one session really stood out to me. Ilanchezhian Ganesamurthy, who is an AWS Hero, gave a really interesting talk about creating Voice AI Agents using AWS Voice Technology and Amazon Nova Sonic. Since I've always been interested in how AI is making technology more conversational and easier to use, this session provided a lot of useful and practical information.

Let’s be honest, we’ve all had those annoying customer service calls where the AI just doesn’t understand what you’re saying. This session shows how we’re moving away from that and moving toward something more natural and smart.

Understanding Voice AI Agents

Voice agents use the speech and thinking powers of large language models, like the ones behind Alexa or Siri. These agents can talk back and forth in a way that feels real, understanding what you’re saying and even handling interruptions during a conversation.

You might be thinking, why is this important? Picture calling your bank and having a chat that feels like a real conversation. You can stop to ask questions, and the AI listens and adjusts to how you speak. That’s the power of today’s voice agents.

Two Approaches to Building Voice Agents

The traditional approach breaks voice interaction into three distinct steps:

Speech-to-Text (STT): Your voice is turned into text using services like AWS Transcribe, Deepgram, or Azure AI Speech.
Large Language Model (LLM): The text is handled by AI models such as Amazon Bedrock, Google Gemini, or Azure OpenAI to figure out what you mean and create a reply.
Text-to-Speech (TTS): The AI's reply is then changed back into speech using tools like AWS Polly, ElevenLabs, or Azure AI Speech.

The thing is, this method works for a lot of situations, but there's a downside. Each step adds a little delay, so there's a small time gap between when you speak and when you hear the response. In customer service, even a tiny delay can affect how natural the conversation feels.

The setup shown in the session uses Pipecat, which is a framework that helps organize conversational AI parts. It connects users through WebRTC for real-time chats. It also includes Voice Activity Detection to know when someone is talking, Amazon Transcribe for understanding speech, Pipecat Flows to manage the conversation, Amazon Bedrock for thinking through the response, and Amazon Polly to turn text back into speech.

Speech-to-Speech with Amazon Nova Sonic

Amazon Nova Sonic is changing the way things work. Instead of going through three steps, it uses a direct speech-to-speech model that handles the whole conversation without turning speech into text in between.

Does that sound familiar? It's similar to the difference between translating each word of a conversation separately versus truly understanding the whole meaning and responding in a natural way. Nova Sonic does this using a bidirectional streaming API, allowing it to listen and reply at the same time, just like people talk in real life.

The setup is simpler: voice input goes through VAD, then straight to Nova Sonic for processing, with Pipecat handling the conversation’s context and state. This makes the system faster and helps create more natural conversations.

Amazon Nova Sonic: Core Capabilities

Let me break down what makes Nova Sonic special:

Low latency: It allows for live conversations between people where the speech is converted to speech with very little delay.
Multilingual support: It can understand many languages and different ways people speak, making it useful around the world.
Expressive voices: It provides eleven different voice choices in English, Spanish, German, French, and Italian, including both male and female voice options.
Natural dialog handling: What makes it really good is that it can understand and adjust to pauses, stutters, and interruptions in speech while keeping track of the conversation.
Tool integration: It can link to company software and tools, so it can use your business's own information to reply.
Task completion: It helps people answer questions, make bookings, and complete tasks related to their work.

Amazon Nova Model Family

The session also mentioned that Nova Sonic is part of a bigger Amazon Nova family. Let me give you a quick summary:

Nova Micro: Fast, text-only model for lowest latency responses
Nova Lite: Multimodal model that understands text, images, and video, great for quick processing
Nova Pro: Best combination of quality and speed for complex multimodal tasks
Nova Premier: The most capable model for complex tasks and teaching other models on Amazon Bedrock
Nova Canvas: Image generation model
Nova Reel: Video generation model
Nova Sonic: Live speech model for natural voice conversations
Nova Act: Research preview for advanced capabilities

Each model is designed for different purposes, but Nova Sonic focuses on voice interaction.

Real-World Use Cases

To be fair, knowing the technology is one thing, but knowing where to use it is really important. The session showed several real-world uses:

Customer service: First, helping customers with their questions, handling sales requests, or answering insurance-related queries, any situation where you’d usually have a call center agent.
Task automation: Second, making bookings, managing sales processes, and finishing transactions through voice.
Learning and development: Third, helping people improve their skills and doing practice interviews.
Healthcare accessibility: Improving accessibility in medical settings and therapy applications

In short, if your business involves phone interactions or voice-based experiences, voice agents could change how you run your business.

Technical Components

For those who want to know more about the technical part, let me explain some important ideas from the session in a simpler way:

ASR (Automatic Speech Recognition): Also called STT, converts your speech to text
TTS (Text-to-Speech): Also called Speech Synthesis, converts text to spoken words
LLM: The AI brain that understands meaning and generates intelligent responses
VAD (Voice Activity Detection): Detects when humans are speaking in audio, helping the system know when to listen
WebRTC: Real-time communication protocol using UDP for full-duplex, persistent connections, ideal for live voice
WebSocket: TCP-based full-duplex connection designed for streaming audio and video

Knowing these basic parts can help you design better voice-based systems.

Conclusion

Voice AI is changing quickly, and Amazon Nova Sonic is helping make conversations sound more real than ever. It's great for creating customer service chatbots, tools that help people with disabilities, or anyone interested in the future of AI. It's a good idea to try it out if you're working on similar projects.

The AWS User Group Chennai gathering keeps offering useful talks like this one, so if you're nearby, you should consider joining.

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS User Group Chennai Meetup

Topic: Voice Agents with Amazon Nova Sonic

Date: September 27, 2025

Also Published On

AWS Builder Center

Hashnode

Reliable Data with AWS Glue Data Quality

N Chandra Prakash Reddy — Thu, 25 Dec 2025 13:42:53 +0000

On September 27, 2025, I went to the AWS User Group Chennai Meetup and it was really full of great sessions. One of the talks that impressed me the most was by Abinaya, an AWS Community Builder, who spoke about "Ensuring Reliable Data with AWS Glue Data Quality in the Catalog." If you've ever worked on data pipelines, you probably know how frustrating it can be to deal with poor data quality. It's like trying to build a house on unstable ground, no matter how strong your analytics or machine learning models are, bad input means bad output.

Let's be honest: data quality is something everyone understands is important, but it's often ignored or left for later. This session really changed how I see AWS Glue Data Quality. It showed me that data validation can be not only easier but also scalable and more cost-effective.

What is AWS Glue?

Before we talk about data quality, let's briefly explain what AWS Glue is. AWS Glue is a serverless ETL (Extract, Transform, Load) service offered by AWS. Imagine it as a tool that helps you move data from one location to another like from a database to a data lake and also changes the data as it goes.

The session mentioned that Glue is serverless, which means you don't have to manage servers or the underlying infrastructure. You can focus on transforming your data, and AWS takes care of everything else. It includes several useful features like:

A data crawler that automatically finds and understands the structure of your data
A data catalog that works like a central place to store information about your data
ETL jobs that you can run on a schedule or start when certain events happen

Here’s the thing: AWS Glue is great for creating data pipelines, but what if the data moving through those pipelines isn’t consistent, missing pieces, or just incorrect? That’s where AWS Glue Data Quality steps in.

Why Data Quality Matters

The session made a key point: if your data is inconsistent or of poor quality, your insights won't be reliable. Think about running an e-commerce site where your sales data has duplicate orders or missing customer details. Your reports might show higher sales than they actually are or incomplete customer profiles. Has this ever happened to you?

Validating data is essential for building trust in your data lakes. Without proper validation, you're like trying to navigate without seeing where you're going. The speaker said traditional ways of validating data take a lot of time and money. Usually, you'd have to write custom scripts, keep them updated, and run them separately from your ETL processes.

AWS Glue Data Quality changes things by making validation quicker and automatic. Instead of spending days or weeks creating validation systems, you can set up data quality rules right inside your Glue jobs.

Understanding AWS Glue Data Quality

What It Actually Does

AWS Glue Data Quality is based on DeeQu, which is an open-source data quality tool created by Amazon. Here's what makes it unique:

The service offers three main types of features:

Rule: Individual data quality checks you define
Ruleset: A collection of rules grouped together for validation
Tags/Parameters: Metadata you can attach to track costs and organize your rulesets

You may be wondering what kinds of checks you can do. AWS Glue Data Quality allows you to:

Rulesets for validation: Define specific conditions your data must meet
Performance monitoring: Track how your data quality checks are performing over time
Cost tracking in AWS Cost Explorer: See exactly how much you're spending on data quality checks

The Technical Foundation

AWS Glue Data Quality uses DeeQu, an open-source framework that Amazon built and shared with the public. This means you aren't tied to a specific company's tools. If you ever decide to leave AWS Glue, you can still use your data quality rules because they are based on open standards.

Key Insights from the Session

The speaker gave some very useful tips about using AWS Glue Data Quality in a real-world setting:

Runtime and Cost

The time it takes to run the data quality checks goes up as the number of rules increases. That makes sense because more rules mean more checks to do. But the good news is that the cost depends on how much compute power you use, and it stays low, usually between $0.18 and $0.54.

Let me explain that. A DPU is a unit of computing power used by AWS Glue. Even if you have a lot of data quality checks, the cost is still less than a dollar for most tasks. That's much cheaper than building and running your own data validation system.

Tracking and Optimization

Tagging your rulesets helps you keep track of and manage your spending. Tags are like labels for different projects or teams. For example, you can tag a ruleset with "team:marketing" or "project:customer-analytics." This lets you see in AWS Cost Explorer which teams or projects are using the most data quality resources, and helps you manage costs better.

This is where things get really helpful: Glue Data Quality can cut validation time from days to just a few hours. Traditional data quality checks often run as separate jobs after your data is processed. With Glue Data Quality, you can check data quality while it’s still in memory during the data processing step, which is way faster.

Rulesets Categories Explained

The session explained how rulesets are structured. Understanding this helps you think about data quality in a more organized and manageable way:

Individual Rules

A Rule is a single data quality check. For example:

Make sure the "email" column has no empty or missing values.
Check that "order_amount" is always a positive number.
Ensure that "created_date" is not a future date.

Rulesets

A Ruleset is a group of related rules. You can put similar rules together based on how they fit your needs. For example, you might have:

A "Customer Data" ruleset that includes rules for customer information.
An "Order Validation" ruleset that includes rules for order details.
A "Financial Compliance" ruleset that covers rules for following financial regulations.

Tags and Parameters

Tags or Parameters allow you to add extra information to your rulesets. This is really helpful for:

Organizing rulesets by team, department, or project
Tracking costs at a granular level
Implementing governance policies

In short, this three-level structure lets you organize your data quality checks in a way that works best for your company.

Best Practices for Implementation

The session ended with some useful tips on how to use AWS Glue Data Quality effectively. These tips are really helpful if you're thinking about setting up data quality checks.

Start Simple, Then Scale

Start with easy rules and then add more as you go. Don't try to create a perfect system right away. Begin with simple checks like:

Are there any missing values where data should be?
Are the data types correct?
Are all the necessary fields there?

Once these basic checks are working well, you can add more complex rules such as checking if data links correctly between different datasets or comparing data across different sources.

Use Tags for Cost Monitoring

Use tags to keep track of costs. It might seem simple, but it's easy to forget. Tag your rule sets from the beginning so you can see how much you're spending as your system grows. You'll be glad you did when someone asks, "How much are we spending on data quality for the marketing database?"

Enable Caching

Turn on caching to make things faster. AWS Glue Data Quality has a caching feature. If you run multiple checks on the same data, it won't have to read the data again each time. This can help speed things up and save money.

Monitor Actively

Connect with alerts and dashboards to keep an eye on things. AWS Glue Data Quality can send notifications through Amazon EventBridge when there are data quality problems. Set up alerts so your team gets notified right away when something goes wrong. You can also create dashboards in Amazon CloudWatch or another tool to track data quality trends over time.

The Key Advantage

To be fair, the speaker really stressed this point: AWS Glue Data Quality is scalable, reliable, and cost-efficient for validation. It's not just about having data quality checks, it's about having them run automatically as part of your pipeline without making costs go up or needing constant attention.

The session also showed that AWS Glue Data Quality automates the creation of rules, which saves you from doing a lot of manual work. You can even use the built-in machine learning features to automatically suggest rules based on your data. In short, you spend less time writing validation code and more time using your data.

Real-World Scenarios

Let me give you a couple of real-world examples to make this clearer:

Scenario 1: E-commerce Order Pipeline

Imagine you're collecting order data from different places, your website, mobile app, and third-party marketplaces. You could set up a ruleset that checks:

Order IDs are unique
Customer emails are valid format
Order totals match the sum of line items
Payment status is one of the allowed values

If any order doesn't meet these checks, you can set the pipeline to separate the bad records and send an alert to your team, while letting the good records go through.

Scenario 2: Healthcare Data Compliance

For healthcare organizations that handle patient data, data quality isn't just a nice feature, it's a legal requirement. You could use AWS Glue Data Quality to check:

That patient identifiers are present and properly formatted
That dates of birth are within valid ranges
That all required fields for regulatory reporting are filled in
That sensitive data is properly encrypted

The system would automatically create compliance reports showing which records passed the checks and which ones need to be reviewed.

Conclusion

At the end of the day, AWS Glue Data Quality helps change messy and unreliable data into something teams can really trust for using in reports and making decisions. By making sure data checks are an important part of your data pipelines, not something you forget about later, you get quicker results, less money spent, and way fewer problems when you share your reports or dashboards.

For people building data systems on AWS, starting with just a few simple rules and slowly adding more to your data quality plan is a smart way to make your work more dependable and trustworthy every day.

About the Author

🔗 Connect with me on LinkedIn

References

Event: AWS User Group Chennai Meetup

Topic: Reliable Data with AWS Glue Data Quality

Date: September 27, 2025

Also Published On

AWS Builder Center

Hashnode

Building Practical AI Agents with Amazon Bedrock AgentCore

N Chandra Prakash Reddy — Thu, 25 Dec 2025 07:44:43 +0000

Why This Session Instantly Hooked Me

I spent my Saturday at the AWS User Group Chennai meetup, and one session really caught my attention: a detailed look at Amazon Bedrock AgentCore and how it helps in creating real AI agents.

The speaker, Muthukumar Oman, who is the VP – Head of Engineering at Intellect Design Arena and an AWS Community Builder, explained how to take an AI model from a basic demo to a fully working AI agent in a clear and organized way.

There were other good talks that day, but this one stood out because it addressed a question many of us have been thinking about: How can we go beyond simple chatbots and actually build a dependable AI agent that works with our systems?

What Is Amazon Bedrock AgentCore?

Making Sense of AgentCore in Simple Terms

AgentCore acts as the main control center for your AI agents on AWS.

It helps you:

Deploy and operate agents securely at scale
Ensure trust and reliability when agents call tools and APIs
Use built-in tools like a code interpreter and browser
Stay framework- and model-agnostic, so you can bring your favorite stack
Test and monitor agents in a structured way

Imagine it this way: if a regular LLM is like a smart intern, AgentCore is like the IT, security, and support team that helps that intern use different apps, keeps track of their work, and makes sure everything stays secure.

Where AgentCore Fits in the AI Stack

One of the slides showed the full AI structure on AWS: applications at the top, followed by AI and agent development tools and services, then Amazon Bedrock (which includes models, features, and AgentCore), and finally the underlying infrastructure such as Amazon SageMaker and AI compute resources like Trainium, Inferentia, and GPUs.

In other words:

Infrastructure = raw compute and ML tooling
Bedrock = models and agent building blocks
AgentCore = runtime, memory, gateway, observability, and identity for agents
Applications = what your users actually interact with (like support bots, internal copilots, etc.)

Core Building Blocks of AgentCore

AgentCore Runtime – The Engine Behind the Agent

The AgentCore Runtime slide explained what happens when your agent starts working.

Key points that stood out:

Framework agnostic – you’re not locked into a specific agent framework
Model flexibility – you can plug in different models
Protocol support, extended execution time, and enhanced payload handling
Session isolation, built-in authentication, and agent-specific observability
Unified set of agent-specific capabilities

There was also a diagram showing how your agent or tool code, like a Python framework, is structured.

Packaged as a container
Pushed to ECR
Exposed via an AgentCore endpoint
Connected to a model and the Bedrock AgentCore runtime

Imagine deploying a microservice: you package your code into a container, send it out, and AgentCore connects it to models and tools.

Memory – Short-Term vs Long-Term

This part really caught my attention. The speaker divided AgentCore Memory into different parts.

Short-term memory

Immediate context
In-session knowledge accumulation

Long-term memory

User preferences
Semantic facts
Summary

In the architecture view, short-term memory stored things like chat messages and session details, while long-term memory kept semantic data, user preferences, and summaries.

Another slide showed how long-term memory functions:

Short-term memory = raw storage
Long-term memory = vector storage
A memory extraction module finds relevant information based on events and strategies, combines it, and then creates an embedded version that can be searched.

Imagine your agent is like a person:

Short-term memory is about the conversation you're currently having.
Long-term memory is about what I’ve learned about you from previous chats and over time.

For a banking or e-commerce assistant, this could mean remembering:

Your preferred language
The kind of products you usually buy
Important facts like “this user prefers digital invoices”

Built-In Tools: Code Interpreter and Browser

Code Interpreter – Let the Agent Safely Run Code

The Code Interpreter slides explained how an agent can safely run code within a sandbox environment.

The architecture was roughly:

User sends a query to the agent
Agent invokes the LLM
LLM selects the Code Interpreter tool and creates a session
Code runs inside a sandboxed environment with a file system and shell
Telemetry flows into observability
Results are returned to the user

The Code Interpreter capabilities listed included:

Secure sandbox execution
Multi-language support
Scalable data processing
Enhanced problem-solving
Structured data formats
Ability to handle complex workflows

Imagine giving your agent a temporary, secure laptop where it can execute scripts, handle CSV files, or process data, while you keep a close watch on everything.

Browser Tool – Let the Agent Navigate the Web or Apps

Another built-in tool is the Browser Tool.

The flow looked like this:

User sends a query (e.g., “Buy shoes on Amazon”)
Agent invokes the LLM
LLM chooses the browser tool
Commands like “click left at (x, y)” are generated
A library (e.g., browser automation) translates these into real actions
The browser executes them and sends screenshots/results back to the agent

The Browser Tool capabilities mentioned:

Resource and session management
Rendering live view using AWS DCV web client
Observability and session replay

In simple terms: your agent can actually interact with a user interface, not just describe it. This is really important when dealing with older systems inside a company that might not have APIs available.

Gateway, Identity, and Observability – Production-Ready Concerns

AgentCore Gateway – One Door for All Tools

The AgentCore Gateway shows how agents connect to tools and APIs in a single, unified way.

Key ideas from the slides:

Simplified tool development and integration
Unified tools access and semantic tool selection
Security guard and serverless infrastructure
Tool types: OpenAPI specs, Lambda functions, Smithy models

Architecturally, the gateway:

Sits between agents and APIs/tools
Handles inbound authentication (via tokens)
Routes to different targets: Smithy, OpenAPI, AWS Lambda
Integrates with Identity for credentials and CloudWatch for observability

If you've ever connected an LLM to many APIs by hand, you know how frustrating that can be. The gateway acts like a main router and enforces rules for using tools.

AgentCore Identity – Who Is This Agent, Really?

Identity is managed through AgentCore Identity, which focuses on:

Centralized agent identity management
Credentials storage
OAuth 2.0
Identity and access controls
SDK integration
Request verification security

It’s like IAM, but better suited for agents and their tools: agents don’t just randomly call APIs; they do so with proper authentication, limited access credentials, and verified requests.

AgentCore Observability – Seeing What Your Agent Is Doing

Observability was another big emphasis:

OTEL-compatible
Runtime metrics
Memory metrics
Gateway metrics
Tools metrics
Sessions, traces, spans

In short, you don’t have to guess what’s happening. You can track how an agent handled a user request, which tools it used, how long each step took, and where things went wrong.

Strands Agents vs Bedrock Agents vs AgentCore

One slide compared Strands Agents, Bedrock Agents, and AgentCore based on several different factors.

So, if you’re:

Experimenting quickly → Strands may be fine.
Shipping something fast → Bedrock Agents are convenient.
Building enterprise-grade, highly customized agents → AgentCore gives you more control while still leaning on AWS-managed pieces.

How All of This Comes Together for Real-World Apps

From “Toy Chatbot” to Production Agent

The speaker used several diagrams showing how an app communicates with AgentCore Runtime, which then interacts with:

Models
Memory
Gateway
Identity
Observability

In real situations, this allows you to create use cases like:

A customer support agent that keeps track of past conversations and user preferences.
A financial assistant that uses browser tools to access internal systems and retrieves data safely.
A developer assistant that runs code using the code interpreter and records all actions for review.

Why This Matters for Builders Like Us

If you're creating a startup product or working in a team within a large company, the usual challenges are similar:

“How do I handle sessions and memory in a reliable way?”
“How can I link agents to different tools without causing serious security issues?”
“How do I figure out what went wrong when something doesn’t work as expected?”

AgentCore helps solve these problems by:

Structured runtimes and memory
Gateway and identity for secure tool access
Deep observability for traces and metrics

In the end, it takes AI agents from being a makeshift side project to something that operations, security, and compliance teams can really trust and use.

Conclusion

Amazon Bedrock AgentCore showed me that creating strong AI agents isn't just about making another chatbot. It's more about getting the basics right, like memory, tools, security, and the ability to track what's happening. When runtime, gateway, identity, and built-in tools all work together, they form a solid base. This helps move from quick weekend projects to real, reliable AI experiences that teams can trust and grow.

About the Author

As an AWS Community Builder*, I enjoy sharing the things I've learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don't hesitate to get in touch!* 🚀

🔗 Connect with me on LinkedIn

References

Event: AWS User Group Chennai Meetup

Topic: Building Practical AI Agents with Amazon Bedrock AgentCore

Date: September 27, 2025

Also Published On

AWS Builder Center

Hashnode