What is Agentic AI
Agentic AI - AI systems that autonomously plan and execute multi-step tasks
AI systems that can autonomously decompose goals into subtasks, plan execution strategies, use tools, and iteratively refine their approach based on intermediate results. Agentic AI enables complex multimodal workflows that require reasoning, tool use, and adaptive decision-making.
How It Works
Agentic AI systems use large language models as reasoning engines that perceive their environment, plan sequences of actions, execute those actions using tools (search, code execution, API calls), observe results, and decide next steps. Unlike single-turn question answering, agents maintain state across multiple turns, recover from errors, and adapt their strategy based on intermediate outcomes to achieve complex goals.
Technical Details
Agent architectures include ReAct (reasoning + acting), Plan-and-Execute (separate planning and execution), and multi-agent systems (specialized agents collaborating). Tool integration uses function calling or structured output APIs. Memory systems include short-term (conversation context), working memory (scratchpad), and long-term (vector store retrieval). Frameworks include LangGraph, AutoGen, and CrewAI for building agent systems.
Best Practices
- Define clear tool interfaces with descriptions that help the agent understand when and how to use each tool
- Implement guardrails and safety checks on agent actions, especially for write operations
- Use structured output to constrain agent responses into parseable action formats
- Build in human-in-the-loop checkpoints for high-stakes decisions or irreversible actions
Common Pitfalls
- Giving agents too much autonomy without safety guardrails on tool execution
- Not handling agent loops where the model repeats the same failed action indefinitely
- Building overly complex agent architectures when a simpler pipeline would suffice
- Ignoring cost and latency, as agent loops can make many LLM calls for a single user request
Advanced Tips
- Build multimodal agents that can search across text, images, audio, and video using different tools
- Implement reflection steps where the agent evaluates its own progress and adjusts strategy
- Use multi-agent architectures with specialized agents for retrieval, analysis, and generation
- Apply agentic RAG where the agent iteratively retrieves, evaluates, and refines its search over multimodal data