AI Agent
AI Agent is an instance of agentic AI software, a concrete system built around a language model that can perceive inputs, decide which actions to take, execute those actions through tools, and use the results to drive toward a specified objective; An AI agent consists of a planning model (usually an LLM), a tool registry (functions the model can call), a memory layer (short-term context plus optional long-term storage), and an execution environment; When designing agents, builders should define the smallest permission footprint that allows the agent to complete its task
AI Agent is an instance of agentic AI software, a concrete system built around a language model that can perceive inputs, decide which actions to take, execute those actions through tools, and use the results to drive toward a specified objective. Agents may be single-model or multi-model, short-lived or persistent, and they operate across domains from software engineering to data analysis to customer support automation.
How it works
An AI agent consists of a planning model (usually an LLM), a tool registry (functions the model can call), a memory layer (short-term context plus optional long-term storage), and an execution environment. The model receives a task description and available tools, generates a plan or the next action, the environment executes it, and results are fed back for the next reasoning step.
Key facts
- Single-agent vs. multi-agent: Multi-agent systems have specialized sub-agents that coordinate, often supervised by an orchestrator agent.
- Persistence: Stateless agents reset between runs; stateful agents maintain memory across sessions.
- Task scope: Agents are most effective for well-bounded tasks with clear success criteria and deterministic tool interfaces.
- Security: Agents with write access to files, databases, or external APIs require strict permission scoping and audit logging.
For builders
When designing agents, builders should define the smallest permission footprint that allows the agent to complete its task. Wrapping tool calls in idempotent operations and confirmation steps before destructive actions significantly reduces production incidents. Observability tooling that captures the full agent trajectory, including every tool call and model decision, is non-negotiable for debugging and compliance.
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
