Agentic AI
Agentic AI describes AI architectures in which a model acts as an autonomous decision-making engine that perceives its environment, formulates a plan, executes actions using tools, observes outcomes, and iterates until a goal is achieved; An agentic loop typically follows an observe-think-act cycle; Building reliable agentic systems requires explicit error handling, bounded tool permissions, and deterministic fallback paths for when the agent gets stuck
Agentic AI describes AI architectures in which a model acts as an autonomous decision-making engine that perceives its environment, formulates a plan, executes actions using tools, observes outcomes, and iterates until a goal is achieved. The key distinction from standard LLM chat is that the model drives a multi-step process rather than responding to a single isolated query.
How it works
An agentic loop typically follows an observe-think-act cycle. The model receives task context and available tools, reasons about the next action, invokes a tool, receives the result, updates its plan, and continues until it determines the task is complete or encounters a blocking condition. Memory systems maintain state across steps; guardrails interrupt the loop when safety or scope conditions are violated.
Key facts
- Key capabilities required: Tool use (function calling), memory, planning, and self-evaluation.
- Common frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, and the Anthropic Agents SDK are widely used.
- Failure modes: Agents can enter infinite loops, hallucinate tool outputs, or take unintended real-world actions.
- Human-in-the-loop: Production agents typically include checkpoints where a human approves consequential actions.
For builders
Building reliable agentic systems requires explicit error handling, bounded tool permissions, and deterministic fallback paths for when the agent gets stuck. Logging every tool call and model decision in an observability system is critical for debugging multi-step failures. Start with narrow, well-defined tasks where success criteria are measurable before expanding agent autonomy.
Sources
- Yao, S., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629. arxiv.org
- Madaan, A., et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback. arXiv:2303.17651. arxiv.org
- Anthropic. Model Context Protocol specification. modelcontextprotocol.io
- Anthropic. Tool use with Claude. docs.anthropic.com
- OpenAI. Function calling guide. platform.openai.com
