DEV Community: Darren Broderick

Why Generative AI Isn’t Enough, Enter Agentic Systems

Darren Broderick — Thu, 16 Apr 2026 20:47:28 +0000

Generative AI vs Agentic AI

From Content Creation to Autonomous Action

As we all move on from AWS DeepRacer (well I try to) and look towards what has replaced it, the "AWS AI League", we find that it's a big step from model ML designing through the console to AI engineering.

AI is evolving fast, but not all AI is created equal. Two terms you’ll hear more and more are Generative AI and Agentic AI. While they’re often grouped together, they represent very different capabilities and very different futures.

What is Generative AI?

Generative AI refers to systems designed to create content. This includes text, images, code, audio, and more. These models are trained on vast datasets and learn patterns that allow them to generate new, "human like" outputs.

Think of Generative AI as a highly capable assistant that responds to prompts such as;

Asking it to write a project proposal, it creates one
Generating code to a requirements document, done, order up!
Instant summaries to books, papers, design patterns, instant, you'll never need to read again!

However, generative AI is typically reactive. It waits for input, produces output, and basically just stops there. It doesn’t independently decide what to do next or take action beyond the initial ask.

What is Agentic AI?

Agentic AI takes things a step further. Instead of simply generating content, it can plan, decide, and act towards a goal/contextual thinking, ideas, goals or scenarios.

An agentic system can:

Break down a high-level objective into smaller tasks
Execute those tasks across multiple steps
Use tools (APIs, databases, systems)
Adapt based on results or new information

In short, Agentic AI behaves more like an autonomous worker than a tool.

For example:

Instead of just writing a report, an agentic system could gather data, analyze trends, generate the report, and email it to stakeholders.
Rather than suggesting code, it could build, test, debug, and deploy an application.

Key Differences

Generative AI
Role: Content creator

Behaviour: Reactive
Workflow: Single-step outputs

Autonomy: Low
Tool use: Limited

Agentic AI
Role: Goal-driven actor
Behavior: Proactive
Workflow: Multi-step processes
Autonomy: High
Tool use: Extensive

Why Does This Matter?

The transition from Generative to Agentic AI marks a fundamental shift from assisting humans to executing work on their behalf.

But can have big implications.

Productivity gains: Tasks that once required coordination across people and tools can be automated end-to-end.
A new software paradigm: Instead of apps, we may interact with systems that do things for us.
Workforce transformation: Roles may shift from execution to oversight and strategy.

Challenges to Overcome

Agentic AI also introduces new complexities:

Reliability: Multi-step systems can fail in unpredictable ways and may not be easy to find root causes if the design is not understood
Control: Ensuring actions align with user intent
Safety: Preventing harmful or unintended outcomes
Evaluation: Harder to measure success than simple outputs, test and record

These challenges mean that while agentic systems are powerful, they require careful design and monitoring.

The Future path that leads to Convergence, (hopefully)

It’s important to note that Agentic AI is not separate from Gen AI but builds on top of it. Generative models provide the intelligence, while agentic frameworks provide the structure for action.

The future likely isn’t one or the other, but a combination:

Use Gen AI for thinking and creating
Use Agentic AI for planning and doing

Final Thought

If Gen AI gave machines a voice, Agentic AI gives them initiative. And that shift from responding to acting may be one of the most important transitions in the next era of tech.

Hopefully these are the concepts we can explore more in a gamified way, perhaps through the AI league!

AWS New Voices

Darren Broderick — Thu, 27 Feb 2025 15:10:44 +0000

What is AWS New Voices?
The New Voices Program is a fresh initiative designed to identify promising speakers from the AWS Community Builders network and refine their public speaking skills. With guidance from experienced community speakers who volunteer as coaches, participants receive hands-on training to elevate their presentation abilities.

In a zoom setup with multiple breakout groups for discussions, around 100 individuals practice their ideas and methods for developing a talk or workshop they plan to present in the future.

It's a desired class and run by enthusiastic, fun and "jazzy" hands members of new voices and aims to give people who attend 4/5 sessions a badge of recognition for their efforts.

An example problem
*How would you explain "the cloud" to someone who just teleported from 1625? *

Breathe
We start with a 4-7-8 breathing exercise were we inhale for 4 seconds, hold for 7, exhale for 8. Getting everybody comfortable in the new space.
Improv Time
Public speaking can be seen as an impossible challenge to most, but a method called "Yes, and..." keeps your audience engaged and can act as a good starting structure to your talk.
You can aim for a certain audience but don't assume
When explaining the cloud to someone new they might have no idea what angle you are coming from while others might get bored of an overly simple explanation. Finding the ground between educational and engaging is something only practice and feedback can improve.
So what is your audience thinking?

Who are you?
Why should I care?
How will this help me?

Addressing these 3 questions as quickly as possible is key to success. Understand everyone's time is the most important thing to them. Communicating that you're going to solve a key problem you have personally faced will add a lot of credability right at the start of a 10 min lightning talk or a full 40 min session with Q&As.

Practice and get feedback

We did a quick ‘show-and-tell’ session, picking a random object within arm’s reach. Speaking about what you know gives early confidence in your abilities, so that with proper planning and research you'll find able to speak to any topic your passionate about!

AWS Deepracer DRFC MinIO Fixes

Darren Broderick — Tue, 25 Feb 2025 15:31:46 +0000

In this guide, we address common issues and resolutions related to the dr-upload-custom-files operation when working with DeepRacer DRFC repo, Docker and MinIO.

Repo Link: https://github.com/aws-deepracer-community/deepracer-for-cloud

Whether you encounter swarm manager errors, container conflicts, or bucket-related issues, this document provides troubleshooting steps to ensure a smooth process. Additionally, key verification commands are included to assist with system diagnostics and resolution.

Error when dr-upload-custom-files
"Error response from daemon: This node is not a swarm manager. Use "docker swarm init" or "docker swarm join" to connect this node to swarm and try again."

Check
docker ps -a

Steps to fix
docker run -d -p 9000:9000 --name minio minio/minio:RELEASE.2022-10-24T18-35-07Z s

If you're unlucky enough (as I was) to get the following messgae
Try again "dr-upload-custom-files" Then try either 1 or both of the below scenarios.

Scenario A: If you get the below error
docker: Error response from daemon: Conflict. The container name "/minio" is already in use by container "75734681ee2c1ec634a7f6f928e8db93d077bdb289d6435af9e9b7a3bc113ff4". You have to remove (or rename) that container to be able to reuse that name.

Run following
docker ps -a
docker start minio
docker start s3_minio.1.vwyypz7jhdj9d16h0db2kzwh1

Scenario B: If you get the below error
Uploading files to s3://bucket/custom_files/
fatal error: An error occurred (NoSuchBucket) when calling the ListObjectsV2 operation: The specified bucket does not exist

TBD - Could have to migrate to a current minio

Extra Info / Verification
Check system.env (DR_MINIO_IMAGE **in system.env, make sure it’s set to:
**RELEASE.2022–10–24T18–35–07Z

docker service ls (Make sure you see s3_minio)
docker stack ls ("Should return "s3, services 1")

docker logs "container id from docker ps -a"
dr-local-aws s3 ls