Inside an AI Agent
Step through an agent loop: task analysis, tool selection, execution, observation, and response.
Architecture flow
A conceptual single-agent execution path. Each tool result returns to the orchestrator before it generates an answer.
Concept
An AI agent is an application loop that uses a model to decide what should happen next. The surrounding software owns the tools, permissions, state, execution and stopping criteria.
Engineering trade-offs
Tool calls introduce latency, failures and security boundaries. A reliable design uses narrow tool permissions, explicit schemas, limits on repeated actions and validation before reporting success.
Try the walkthrough
Use the step controls below to see a deterministic illustrative request move through planning, tool selection, tool execution, observation and response. This is not a live LLM or a running production agent.
Agent execution trace
Receive the task
Example: “Find the deployment status for service A and summarize it.” The application validates and records the request.
request = { service: "service-a", intent: "deployment-status" }Sample status data is fictional. No model, API, or infrastructure tools are executed.