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Raunak ALI
Raunak ALI

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Agentic AI Explained: A Transition from Gen AI to Agentic AI

#genai #agent #langchain #llm

Agentic AI Explained: How Smart Agents Turn Language Models Into Real-World Problem Solvers

1. From Prompting to Agents

Think of Generative AI today like a really smart intern: you ask it a question (prompt), it gives you an answer. That’s prompting very simple and powerful.

But here’s the catch: LLMs don’t “know” everything. They generate based on their training data and the words you give them. So, if you ask an LLM “What’s the current stock price of Tesla?” it can’t fetch that for you. It’ll guess.

That’s where Agents come in.

  • Prompting = “Ask and answer” model.
  • Agents = Models + Tools + Memory + Autonomy.

In short: while prompts are static queries, agents are dynamic systems that decide what steps to take, which tools to use, and how to carry out multi-step goals.

2. What Is Agentic AI?

Agentic AI = LLMs upgraded into orchestrators.
Instead of just generating text, they:

  1. Access tools (APIs, databases, calculators, search engines).
  2. Use memory (short-term for conversation flow, long-term for knowledge retention).
  3. Plan and reason (break a task into smaller steps, execute in sequence).
  4. Self-reflect (evaluate outputs, retry or improve).
  • A simple analogy:
  • Gen AI = You asking ChatGPT for an essay draft.
  • Agentic AI = A virtual assistant that researches, cites sources, cross-checks facts, and then drafts the essay all without you having to prompt it for each step.

3. Examples in Action

  • LangChain + LangGraph → Frameworks to build multi-step reasoning agents. LangGraph adds state machines so agents can decide “what to do next.”
  • AutoGPT → A famous early experiment where you just give a high-level goal (“research electric cars and make a report”), and the agent loops through tasks autonomously.
  • Real-world → Customer service bots that not only answer FAQs but also pull data from your CRM, check stock inventory, and send an email confirmation.

4. Architecture Breakdown

Here’s how most Agentic AI systems are structured:

  • LLM Core → Handles reasoning and natural language. (Gemini, GPT-4, Claude, LLaMA-based models)
  • Toolset → Plug-ins like web search, SQL database connectors, calculators.
  • Controller/Orchestrator → Decides when to call which tool.

  • Memory Layer:

    • Short-term memory (conversation so far).
    • Long-term memory (knowledge storage, embeddings, vector databases like Pinecone, Weaviate).

  • Feedback/Evaluation Loop → The agent checks whether outputs make sense, retries if needed.

Think of it like a team:

  • LLM = the brain
  • Tools = the hands
  • Memory = the notebook
  • Orchestrator = the project manager

5. Strengths & Opportunities

  • Autonomy → Agents can run for hours or days on tasks.
  • Efficiency → Offload repetitive workflows (data entry, monitoring, research).
  • Scalability → Businesses can replace complex manual processes with AI pipelines.
  • Customization → Agents can be fine-tuned to specific industries (finance, healthcare, e-commerce).

6. Limitations & Warnings

Like any shiny new tech, Agentic AI isn’t flawless:

  • Hallucinations: Agents may “act confidently wrong.” (e.g., citing non-existent papers).
  • Cost Explosion: Running loops or external calls repeatedly can burn API credits.
  • Control Risk: Fully autonomous systems can spiral if not sandboxed.
  • Latency: Multi-step reasoning = longer response times.
  • Security: Allowing agents tool access (e.g., your email or database) can open doors for misuse.

Example: Early versions of AutoGPT would happily Google random things for hours, running up costs, with no guarantee of useful output.

7. Where Things Are Headed

Agentic AI is shaping the next wave of applications:

  • AI Researchers → Systems that design and test hypotheses.
  • AI DevOps → Agents fixing code, testing, and deploying autonomously.
  • Enterprise Assistants → Not just answering queries but executing workflows end-to-end.

Frameworks like LangGraph, CrewAI, and Microsoft’s AutoGen are already bridging this gap. The vision? Moving from single-shot Q&A to AI teammates that collaborate with humans on real tasks.

8. Key Design Patterns & Implementation Strategies

When moving from single-shot Gen AI to Agentic AI systems, engineers often rely on well-tested design patterns. These patterns help balance autonomy with control, ensuring agents don’t just “guess” but actually execute structured workflows.

A. Task Decomposition

Agents break down complex goals into smaller, actionable steps.

💡 Example:
Instead of asking an agent directly: “Write a business plan for an EV startup”, the agent may decompose it into:

  1. Research EV market trends.
  2. Analyze competitors.
  3. Estimate costs and revenue.
  4. Draft business plan sections.

🔹 In LangChain, this often uses a “planner → executor” pattern.

# Pseudo-pattern: Decompose into subtasks
goal = "Write a business plan for an EV startup"
subtasks = agent.plan(goal)  # e.g., planner LLM decides steps
for task in subtasks:
    agent.execute(task)
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B. Tool Selection Heuristics

Agents must know which tool to use at the right time (e.g., calculator, database, search engine).

  • Heuristic-based: Simple if-else logic.
  • Model-driven: LLM itself decides via prompt engineering. 
if "calculate" in user_query:
    use(calculator_tool)
elif "search" in user_query:
    use(google_search_tool)
else:
    use(LLM)
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This avoids unnecessary tool calls and keeps costs down.

C. Memory Management

Memory is what makes agents “context-aware.”

  • Short-term memory → Keeps track of conversation/session (like a chatbot remembering what you just said).
  • Long-term memory → Uses vector databases (like Pinecone, Weaviate, FAISS) to store embeddings for retrieval later. 
# Example: retrieve memory from vector store
query = "What did the customer say about pricing last week?"
context = vector_db.similarity_search(query, k=3)
response = llm.generate(context + query)
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D. State Management

Agents don’t just move linearly instead they often branch, loop, or run in parallel.

  • State tracking ensures the agent knows where it is in the workflow.
  • Frameworks like LangGraph use a state machine approach (nodes = steps, edges = transitions).

📌 Example:

This keeps the workflow predictable and debuggable.

Together, these patterns ie decomposition, tool heuristics, memory, and state management are what elevate LLMs from simple “word predictors” into reliable autonomous systems.

Simple takeaway:

  • Gen AI is great for one-off answers.
  • Agentic AI is about orchestrating steps, tools, and memory for autonomous workflows.

This transition is not just technical ,it’s a paradigm shift in how businesses, students, and professionals will work with AI.

Want to learn in depth on how to Talk to LLMs?
Discover Prompt Engineering from basic to Expert level
Understand how to unlock the power of Prompt Engineering to get the best out of LLM Models
Read:-Prompt Engineering Made Simple: Real-World Techniques, Mistakes to Avoid, and Hands-On for Everyone

Want to Build Real AI Apps, Not Just Test Prompts?
Discover why LangChain and LangGraph are the go-to frameworks for anyone looking to turn LLMs into powerful, tool-using apps—and even autonomous agents.
This chapter breaks down what makes them essential, when to use each, their strengths and limitations, plus practical prerequisites for getting started. Perfect for beginners and future builders!

Got questions or ideas?Drop a comment below — I’d love to hear your thoughts.
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