Agentic AI: planning, reasoning, and acting with Autonomy

What is an Agentic AI#

Agentic AI refers to systems that don’t just answer questions, but can plan, decide, and act on their own.

They use LLMs + Tools + Memory to handle Multi-Step Tasks without being told exactly what to do at each step.

For example:#

• You say “Book me a trip to Delhi this weekend under Rs.5000.”

  • An Agentic AI might:

    • Search for flights & buses.
    • Check Hotels.
    • Compare Costs.
    • Make bookings via APIs.
    • Email you the plan.

  • Now, this is way beyond just generating text - this is actual Decision + Action.

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Characterstics of Agentic AI:#

1. Goal-Oriented Planning

   • It starts with a goal, then breaks it into smaller steps and executes.

2. Multi-Step Reasoning

   • It doesn’t stop at one reply - it iteratively reasons until the goal is met.

3. Autonomous Decision-Making

   • It chooses the best path or the tool on its own, based on the whatever context it has in that situation.

4. Uses Memory, Tools and Knowledge

   • Remembers Past Events (long term memory or conversational memory), uses APIs/Scripts (tools), and refers to the docs or rules (Knowledge Base).

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What makes Agentic AI different from Basic LLMs?#

• Normal LLMs (Chat GPT, Gemini, Perplexity etc.) are Passive:
  • They just respond to a prompt.
• Agentic Systems are Active:
  • They observe, think, plan, act, and even revise their actions.

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Basic Agentic Structure#

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Agentic AI VS AI Workflow#

Term	What it means
AI workflow	Pre-defined steps using AI (e.g. RAG pipeline)
Agent	Dynamic reasoning & acting system that adapts
ReAct Agent	Reason + Act: Think, Decide, take Action
Control logic	Like if/else to guide actions
Tools	External Functions/APIs
Memory	Past info/context retained
Planner	Creates a step-wise plan

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LLMs in this context#

• LLMs (like GPT, Gemini, Claude) are the Core Brains of the system.
• But on their own, they can’t remember, act or plan.
• We wrap them in Agents to extend their ability.

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Agentic AI Example#

1. AI Coding Assistant
   • Can Understand bug report, edits code, runs tests, commits.
2. Travel Agent
   • As we have already seen the example above; There are a gazillion use-cases of Agentic AI in various domains.
3. A few more examples would be an HR Onboarding Bot, An Agent that posts for you on LinkedIn, the list goes on~

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Let’s recap#

• LLM = Brain
• Agent = Brain + Body(Tools/Memory/Planner)
• Agentic AI = Autonomous Worker that thinks and achieves any goal given to it
• Multi-Turn Planning = Not just simple answers, but strategic answers driven by context
• ReAct Loop = Reason -> Act -> Observe -> Repeat.

Important: before finishing up this blog, I’d like you to read this amazing blog by Anthropic on building agentic AI.