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Agentic AI
AI systems that are autonomous agents: they can plan, reason, take actions, and use tools.
Builds on LLMs + RL concepts.
Can execute multi-step tasks with minimal human guidance.
Before Agentic AI
- Traditional AI -> task-specific models.
- LLMs -> good at generating text but mostly passive responders.
Transformation with Agentic AI
- Adds agency: memory, planning, acting.
- Can chain multiple AI capabilities (search + reasoning + action).
Pros
- Automates workflows end-to-end.
- Adaptable across domains.
- Learns from feedback loops.
Cons
- Hard to control (hallucinations, unsafe actions).
- High computational cost.
- Reliability and governance still open challenges.
Use Cases
- AI agents booking travel (search -> compare -> purchase).
- Customer support bots that escalate only when needed.
- Business process automation (invoice handling, data entry).
| Aspect | AI Assistant (Chatbot/LLM) | Agentic AI (Autonomous Agent) |
|---|---|---|
| Nature | Reactive → answers questions | Proactive → plans and executes tasks |
| Memory | Limited to current session | Has memory across interactions |
| Actions | Generates text/code only | Uses tools, APIs, external systems |
| Planning | One-shot response | Multi-step reasoning and decision-making |
| Adaptability | Needs explicit user prompts | Self-adjusts based on goals and feedback |
| Example Use Case | “What’s the weather in NYC?” → gives forecast | “Plan my weekend trip to NYC” → books flight, hotel, creates itinerary |
| Industry Example | Customer support FAQ bot | AI agent that handles returns, refunds, and escalations automatically |