Anatomy of the AI agent

Agent Foundations
The building blocks The core components of an AI agent and when the agent loop is worth the overhead.
The ReAct pattern The loop that turns a stateless model into an agent that reasons, acts, and observes.
Planning & Task Decomposition Turning broad goals into concrete plans, subtasks, and executable steps.
Tools & Function Calling Exposing functions to models, validate arguments, and keeping tools use reliable.
Tool Servers & MCP Discovering and calling tools across process boundaries using external tool servers and MCP.
Memory & Context Engineering Conversation history management, retrieval, and context-window budgets across turns.
RAG as an Agent Pattern Retrieval-augmented as agent pattern for grounded, iterative information use.
The System Prompt & Context Engineering System prompts and assembled context as reliable agent behavior at runtime.
Workflow Orchestration Prompt chains, routing, parallelization, workers, and evaluators.
Coding Agents Files, tests, terminals, and deterministic feedback.
Computer-Use Agents Operating graphical software through screenshots, clicks, keystrokes, and visual feedback.
Multi-Agent Systems
Multi-Agent Systems 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 Dynamic work delegation to specialists and results synthesis.
Loop, Review-and-Critique, and Iterative Refinement Improving agent output through explicit review and feedback.
The Swarm Pattern Peer agents collaborating, critique, and hand off work without a central coordinator.
Cross-Cutting Concerns
Guardrails and Safety Runtime checks constraints and side effects beyond prompt instructions.
Error Handling and Recovery Failures classification, retry safely, fall back, checkpoint, and recover.
Model Selection and Routing Balancing latency, cost, capability, and confidence.
Human-in-the-Loop Review, approval, correction, and escalation on risky actions.
Agent-to-Agent Communication Discover, message, stream, and delegate work to other agents across system boundaries.
Observability and Testing Traces, metrics, evaluations, and regression tests as a measure of agents behavior.
Long-Running and Durable Agents Persisting progress, suspend, resume, and survival of long-running workflows.
Advanced Patterns
Multimodal Agents Agent reasoning across text, images, audio, video, and structured media inside one loop.
Agentic Search & Deep Research Iterative search, triangulating sources, and synthesizing cited answers.
Fine-Tuning & Distillation for Agents Fine-tuning and distillation of agent behavior for faster, cheaper, and more consistent results.
Production Concerns
Cost Optimization Compounding agent costs across loops.
Security Model reasoning as untrusted process containing damage with layered controls around actions.
Agent UX & Transparency Trustworthy agent interfaces.
Benchmarking & Evaluation Design Evidence about quality, regressions, and real-world readiness.