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 completedCore Concepts
Deep dive into each component—how they work, what trade-offs exist, and how to choose the right architecture for your use case.
The Four-Layer Agent Architecture
Modern agents are built in layers, from low-level execution to high-level reasoning. Understanding these layers helps you debug failures and optimize performance.
🧠 Reasoning Engine: The Brain
What It Does
The reasoning engine is the LLM that processes inputs, generates thoughts, and decides what to do next. It's the "intelligence" of the agent.
Example Flow:
→ Input: "What's the weather in Tokyo?"
→ Thought: "I need current weather data, not just general knowledge"
→ Decision: Use get_weather tool with location="Tokyo"
→ Action: Call tool and wait for result
Model Choice
- • GPT-4: Best reasoning, expensive ($0.03/1K)
- • Claude 3.5: Fast, balanced ($0.015/1K)
- • GPT-3.5: Cheap, less reliable ($0.001/1K)
- • Open-source: Llama 3, Mixtral (self-hosted)
Prompt Engineering
- • System prompt defines agent behavior
- • Few-shot examples improve accuracy
- • Structured outputs (JSON) reduce errors
- • Temperature controls creativity (0.0-1.0)
⚡ Performance Trade-offs
Larger models: Better reasoning but slower (2-5s per call)
Smaller models: Faster (200-500ms) but more errors
Best practice: Use GPT-4 for planning, GPT-3.5 for execution
Component Integration: How They Work Together
A single agent task involves all four components working in concert:
1. User Input
"Book a flight to Paris next Friday"
2. Memory Retrieval
Agent recalls: "User prefers morning flights, economy class"
3. Reasoning
LLM thinks: "Need to search flights → compare prices → book best option"
4. Tool Call
Execute: search_flights(dest="Paris", date="2025-11-22")
5. Observation
Tool returns: 15 flights found, prices $450-$890
6. Control Loop
ReAct loop decides: continue to compare prices
7. Memory Update
Store: "Searched flights on 2025-11-15, found 15 options"
8. Final Response
"Found a $450 morning flight departing 8am. Would you like to book it?"
💡Key Insights
- →No single "best" architecture: Choose components based on your use case
- →Start simple: ReAct loop + GPT-4 + basic tools → works for 80% of use cases
- →Add complexity only when needed: Don't add memory if tasks are stateless
- →Guardrails are non-negotiable: Production agents must have hard limits