Anatomy of the AI agent

The building blocks The core components of an AI agent and when the agent loop is worth the overhead.
The ReAct pattern The ReAct loop turns a stateless model into an agent that reasons, acts, and observes.
Planning & Task Decomposition Agents turn broad goals into concrete plans, subtasks, and executable steps.
Tools & Function Calling Agents expose functions to models, validate arguments, and keep tool use reliable.
Tool Servers & MCP External tool servers and MCP let agents discover and call tools across process boundaries.
Memory & Context Engineering Agents manage conversation history, retrieval, and context-window budgets across turns.
RAG as an Agent Pattern Retrieval-augmented generation becomes an agent pattern for grounded, iterative information use.
The System Prompt & Context Engineering System prompts and assembled context shape reliable agent behavior at runtime.
Workflow Orchestration Prompt chains, routing, parallelization, workers, and evaluators structure agent workflows.
Coding Agents Coding agents use files, tests, terminals, and deterministic feedback to produce working software.
Computer-Use Agents Agents operate graphical software through screenshots, clicks, keystrokes, and visual feedback.
Multi-Agent Systems Agent systems split work across specialized agents with separate roles and tools.
Sequential & Parallel Patterns Deterministic multi-agent pipelines sequence, fan out, vote, and merge agent work.
Coordinator & Hierarchical Patterns Coordinator agents dynamically delegate work to specialists and synthesize their results.
Loop, Review-and-Critique, and Iterative Refinement Generate-critique-revise loops improve agent output through explicit review and feedback.
The Swarm Pattern Peer agents collaborate, critique, and hand off work without a central coordinator.
Guardrails and Safety Runtime checks constrain agent inputs, outputs, and side effects beyond prompt instructions.
Error Handling and Recovery Resilient agents classify failures, retry safely, fall back, checkpoint, and recover.
Model Selection and Routing Agents choose models per step to balance latency, cost, capability, and confidence.
Human-in-the-Loop Agents pause for human review, approval, correction, and escalation on risky actions.
Agent-to-Agent Communication Agents discover, message, stream, and delegate work to other agents across system boundaries.
Observability and Testing Traces, metrics, evaluations, and regression tests make agent behavior visible and measurable.
Long-Running and Durable Agents Durable agents persist progress, suspend, resume, and survive long-running workflows.
Multimodal Agents Agents reason across text, images, audio, video, and structured media inside one loop.