Hi everyone! I'm Salma, a student and software engineer preparing for full-time roles. While applying for jobs and preparing for interviews, I realized something big:
"Just knowing how to code is no longer enough."
Today’s tech world is changing fast. We see AI everywhere, and one term you’ll hear again and again is Agentic AI. Some people know what it is, many don’t. But if you’re a student or professional looking for a job, understanding Agentic AI gives you a huge advantage.
Let’s Imagine you're building your own travel assistant app.
Traditional AI (like ChatGPT):
You: "Book a flight to Delhi."
AI: "Sure. Please tell me the date, airline, timing, etc."
Agentic AI:
You: "I need to be in Delhi next week for a conference."
AI:
- Checks your calendar for free days
- Suggests flight options
- Books your ticket
- Adds it to your calendar
- Sends you a reminder and even books your cab
This isn’t just AI that responds. It’s AI that acts on its own.
What is Agentic AI?
Agentic AI is artificial intelligence that sets goals, makes decisions, takes action, and learns all on its own.
It doesn’t wait for your prompt. It’s like hiring a junior employee who knows what to do next.
Traditional AI vs Agentic AI
Traditional AI works based on prompts. You give it instructions, it gives an output.
Agentic AI works based on goals. You give it a goal, and it figures out how to reach it.
Difference Between Traditional AI and Agentic AI
| Feature | Traditional AI | Agentic AI |
|---|---|---|
| Needs prompts | Yes | No |
| Can act on goals | No | Yes |
| Decision-making | Basic logic | Complex reasoning |
| Example | Chatbot | Calendar + Travel Manager |
Lifecycle of Agentic AI
- Perceive – Collects data (emails, APIs, sensors)
- Reason – Understands the task and plans next steps
- Act – Executes using APIs and tools
- Learn – Evaluates and improves its performance
- Collaborate – Works with humans or other agents
How Agentic AI Solves Customer Support Issues
- Perceive: Reads an angry customer email
- Reason: Understands it’s about a delayed shipment
- Act: Sends an apology and discount coupon
- Learn: Tracks response from customer
- Collaborate: Notifies human agent if unresolved
Types of Agentic AI
Single-Agent System : One agent handles everything.
Example: Budget manager bot that tracks, predicts, and alerts.
Multi-Agent System : Several agents with different responsibilities.
Example: Email agent one reads, another replies, another logs.
Goal-Oriented Agent : Given a goal, it plans and acts.
Example: “Grow Instagram to 5K followers.”
Reactive Agent : Reacts quickly but doesn’t plan ahead.
Example: Auto-braking system in cars.
Deliberative Agent : Thinks and reasons before acting.
Example: Schedules meetings based on mood, urgency, and history.
How to Build Agentic AI
To build an Agentic AI system, you begin with a frontend that accepts input from users. The request is handled by a backend which forwards the data to a language model (LLM) such as GPT-4 or Claude. The LLM reasons about the task and initiates actions. These actions may include calling APIs or updating systems. Context or memory is stored using vector databases. Results and state changes are saved in a storage system like PostgreSQL.
How Agentic AI Can Automate Resume Screening
- Recruiter uploads resumes on the web interface
- Backend forwards data to the LLM
- LLM ranks the candidates based on fit
- Memory layer remembers past hiring preferences
- Action layer sends top resumes to HR
- PostgreSQL stores rankings and history
Components Used
- Frontend: HTML, React
- Backend: Python (Flask, FastAPI)
- LLM: GPT-4, Claude, LLaMA
- Memory: FAISS, Pinecone
- Actions: APIs, Zapier, CRMs
- Storage: PostgreSQL, Redis
How LangChain + Agentic AI Works
This diagram shows how an Agentic AI system works when you build it using LangChain:
User Input : The user gives a request.
Example: “Remind me about my meeting and send a message if I’m late.”
Reasoning / Planning : The system now goes into thinking mode. It uses a smart model (like GPT-4 or Claude) to figure out what to do next.
Action : Based on the plan, it performs the actual work:
- Checks your calendar
- Sends messages
- Searches the web
Uses Tools : To complete tasks, the AI uses different tools:
- Web Search to gather new information
- API Calls to apps like your calendar or email
- Databases, Zapier, or CRMs to interact with systems
Memory / Storage : After doing the task, it stores what happened for future reference so it can learn and improve next time.
Back to User or Move to Next Task : It either
- Updates the user about the result
- Or starts working on the next goal
This full loop User → Plan → Act → Tools → Back to User is what makes Agentic AI powerful. It’s not just replying like a chatbot. It’s doing real work for you like a smart digital assistant.
Advantages of Agentic AI
- Proactive and autonomous
- Learns and adapts over time
- Integrates with tools and systems
- Can collaborate with other agents or humans
- Reduces repetitive human work
Drawbacks of Agentic AI
- Risk of incorrect actions due to bad data
- Hard to debug errors in multi-step logic
- Requires safeguards and human override
- Complexity in design and testing
What If Agentic AI Fails?
Failures can occur. Here's how to make systems robust:
- Task Queues: Split large tasks into traceable chunks
- Session Tokens: Avoid confusion between user sessions
- Prompt Templates: Keep communication consistent
- Escalation Paths: Alert humans when automation fails
Failure Example:
If a meeting booking fails due to calendar API error:
- Retry booking
- On failure again, send alert to user and log the error
Where Is Agentic AI Used Today?
- Salesforce – AI customer support agents
- Hippocratic AI – Medical virtual assistants
- Ema AI – Business workflow automation
- Juna – Factory control agents
- Jasper + HubSpot – AI-powered marketing
AI Agents vs Agentic AI
AI Agent : Acts only after manual user input.
Example: Gmail Smart Reply you click it, it sends.
Difference Between AI Agents and Agentic AI
| Feature | AI Agents | Agentic AI |
|---|---|---|
| User initiated | Yes | No |
| Goal planning | No | Yes |
| Multi-step task | No | Yes |
| Learning ability | No | Yes |
Evolution of AI : AI has progressed in 3 major stages:
Predictive AI : Forecasting the future
Example: Credit scoring, fraud detection
Generative AI : Creating new content
Example: ChatGPT, DALL·E, MidJourney
Agentic AI : Thinking, planning, acting
Example: AI assistant managing tasks and meetings
Final Thoughts
Agentic AI is not just a buzzword. It’s a career game-changer.
If you're a student or developer:
- Learn the lifecycle of agentic systems
- Build a real mini-project (e.g. with LangChain)
- Write about it or share your GitHub
- Talk about it in interviews
✍️ Written by Shaik Salma Aga
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