Anatomy of an Agent
Learn the core components that make up an AI agent and how they work together
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0 / 5 completedKey Takeaways
Understanding agent anatomy transforms you from an agent user to an agent architect. Here's what matters most.
🎯Core Concepts Recap
Every agent has: Reasoning Engine (the brain), Memory Systems (context storage), Tool Interface (world interaction), and Control Loop (orchestration pattern).
Component choices depend on your use case. Quality-first? Use GPT-4 + long-term memory. Speed-first? GPT-3.5 + no memory. Error-prone tasks? Reflexion loop. Predictable workflows? Plan-Execute.
Production agents must have hard limits: max iterations (10-30), budget caps ($5 typical), timeouts (60s), and human-in-loop for risky actions. Without these, agents become expensive disasters.
Begin with: GPT-4 + short-term memory + 3-5 tools + ReAct loop. This handles 80% of use cases. Add long-term memory only if users need cross-session context. Add more tools only when current set is insufficient.
Every choice has implications: GPT-4 costs 30x more than GPT-3.5 but has 20% better accuracy. Long-term memory adds 500ms latency. More tools = slower decision making. Reflexion uses 3x more LLM calls than ReAct.
Component Selection Cheat Sheet
| Component | When to Use | When to Avoid |
|---|---|---|
| GPT-4 | Complex reasoning, high stakes, quality > cost | Budget constraints, simple tasks, high volume |
| Long-term Memory | Personalization, learning from past, customer support | Stateless tasks, privacy concerns, latency-critical |
| Many Tools (10+) | General assistants, versatility needed, ChatGPT-like | Specialized domains, reliability critical, focused tasks |
| ReAct Loop | Most use cases, exploratory tasks, need adaptability | Predictable workflows, cost optimization needed |
| Plan-Execute | Predictable tasks (booking, data processing), efficiency | Exploratory work, high failure rates, need replanning |
| Reflexion | Error-prone tasks (API calls, scraping), learning needed | Budget-limited, simple tasks, predictable success |
Production Lessons from Real Systems
✅What Works
- • Hybrid model strategy: GPT-4 for planning, GPT-3.5 for execution
- • Parallel tool execution: 2-3x faster for independent operations
- • Caching tool results: Weather valid for 30min, reduces costs
- • Streaming responses: Improves perceived speed, better UX
- • Few specialized tools: 3-7 focused tools beat 50 generic ones
❌Common Mistakes
- • No iteration limits: Agents run forever, rack up $100+ bills
- • Too many tools: 50+ tools confuses LLM, degrades quality
- • No error handling: One API failure crashes entire agent
- • Skipping observability: Can't debug failures without logs
- • Using only GPT-4: 10x cost for marginal gains on simple tasks
Quick Start Guide
🚀 Recommended Starting Configuration
Best balance of quality and cost. Switch to GPT-3.5 for simple execution steps.
Start here. Add long-term memory (vector DB) only if users need cross-session personalization.
Focus on what your agent actually needs. More tools = slower decisions and more errors.
Default choice. Flexible, debuggable, works for 80% of use cases.
CRITICAL. Without these, agents become expensive and unpredictable.
✨ This configuration handles customer support, research tasks, code assistance, and most business workflows.
Optimize individual components only after measuring performance bottlenecks.
📚Further Learning
- →LangChain Docs: Comprehensive guide to agent components and implementation patterns
- →OpenAI Function Calling: Best practices for reliable tool use
- →ReAct Paper (2022): Original reasoning + acting pattern that started it all
- →LangSmith: Production observability tool for debugging agent failures
🎓 Module Complete!
You now understand the four core components of every AI agent, how to choose the right architecture for your use case, and what patterns work in production. Ready to see these concepts in action? Continue to the next module.