Interactive AI agent architecture diagram with seven components in concentric rings. Core orchestration at center, cognitive layer (Planning, Reflection) inner ring, I/O boundary (Perception, Tools, Memory, Response) outer ring. Activate any component to learn what it does and why it exists.
Interactive Reference
AI Agent Architecture
How autonomous AI agents perceive, plan, act, and learn — an interactive map of the components, patterns, and production realities of modern agent systems.
Agent Architecture Diagram
Illustrative flow — real systems may route data directly or asynchronously
Execution Trace
An illustrative example of an agent executing a real task. Step through to see how each architectural component activates — or select a node on the map to jump to its step.
8 steps
Select any step or map node to highlight its component. Select a node repeatedly to cycle through its steps.
Agent Design Patterns
ReAct
Think→Act→Observe↻ loop
Interleaves reasoning and acting in a tight loop — think, do, observe, repeat. The simplest effective pattern and the right starting point for most tasks.
Simple to moderate tasks with clear tool needs
Plan-and-Execute
Plan→Step 1→Step 2→Step N→Done
Creates a full plan upfront, then executes steps sequentially. Allows global optimization and dependency management before any action.
Complex workflows with 5+ interdependent steps
Reflexion
Attempt→Evaluate→Revise↻ loop
Adds explicit self-evaluation after each attempt. The agent critiques its own output and uses that feedback to improve on the next iteration.
Language Agent Tree Search — applies Monte Carlo Tree Search to explore multiple reasoning paths in parallel, scoring and pruning branches.
Large solution spaces needing systematic exploration
Multi-Agent
Coord→Agent A→Agent B→Synthesize
Specialized agents collaborate, each with distinct roles, tools, and expertise. A coordinator routes tasks and synthesizes results.
Diverse expertise or parallel sub-task execution
Choosing a Pattern
Start with ReAct — it's the simplest effective pattern. Move to Plan-and-Execute when tasks have 5+ interdependent steps. Add Reflexion when output quality matters more than speed. Use LATS for problems with large solution spaces. Go Multi-Agent when no single agent has all required expertise. In practice, production systems often combine patterns — planning at the top level with ReAct loops inside each step.
Frameworks & Production Reference
Orchestration Frameworks
LangGraphStateful, multi-actor agent graphs with persistence and human-in-the-loop
CrewAIRole-based multi-agent orchestration with structured collaboration
The gap between demo agents and production agents. These are the concerns that most tutorials skip — and where most real-world agent projects fail.
Guardrails & Safety
Input/output validation, content filtering, scope boundaries to prevent agents from taking harmful or unintended actions. Includes rate limiting, spend caps, and human approval gates.
Context Window Management
The fundamental constraint. Agents must compress, summarize, and strategically select what enters the limited context. RAG exists specifically to work around this bottleneck.
Cost & Token Economics
Every reasoning step, tool call, and reflection loop costs tokens. Production agents need budgets, caching strategies, and model routing (expensive for hard tasks, cheap for easy ones).
Observability & Debugging
Agent failures are opaque without tracing. Production systems need step-level logging, token tracking, decision audit trails, and the ability to replay failed runs.
Evaluation & Benchmarking
How do you know your agent actually works? Requires task-specific benchmarks, success rate tracking, regression testing, and human evaluation on edge cases.
Prompt & Interface Design
The hidden foundation. System prompts, few-shot examples, output formatting instructions, and tool descriptions are the actual interface between your code and the model's capabilities.
Ask the Agent
Ask questions about AI agent architecture — components, patterns, frameworks, or production deployment. This agent is ring-fenced to only discuss agent architecture topics.