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Anatomy of an Agent

Learn the core components that make up an AI agent and how they work together

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Practical Application

See how real production systems architect their agentsβ€”from ChatGPT to GitHub Copilot. Learn what works at scale.

Production Agent Architectures

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ChatGPT Plugins

OpenAI's agent system

Component Choices:
  • β€’ Reasoning: GPT-4 (best quality)
  • β€’ Memory: Conversation history (short-term)
  • β€’ Tools: 70+ plugins (web search, calc, Wolfram)
  • β€’ Loop: Modified ReAct with parallel tool calls
Why These Choices:
  • β€’ Quality matters more than cost (consumer product)
  • β€’ No long-term memory (privacy concerns)
  • β€’ Many tools = versatility (general assistant)
  • β€’ Parallel calls = faster multi-step tasks
Performance: 2-5s per response, $0.05-0.20 per conversation
πŸ‘¨β€πŸ’»

GitHub Copilot

Microsoft's coding assistant

Component Choices:
  • β€’ Reasoning: Codex (code-specialized model)
  • β€’ Memory: Editor context + project files
  • β€’ Tools: File read/write, LSP, terminal
  • β€’ Loop: Plan-Execute (predictable coding tasks)
Why These Choices:
  • β€’ Specialized model = better code quality
  • β€’ Project context = relevant suggestions
  • β€’ Few targeted tools (no web search needed)
  • β€’ Plan-Execute fits structured coding workflows
Performance: 100-500ms per suggestion, optimized for latency
πŸ’Ό

Intercom Fin

Customer support agent

Component Choices:
  • β€’ Reasoning: GPT-4 fine-tuned on support data
  • β€’ Memory: Long-term (vector DB of past tickets)
  • β€’ Tools: Ticket search, KB lookup, escalation
  • β€’ Loop: ReAct with human-in-loop for escalations
Why These Choices:
  • β€’ Fine-tuning = consistent brand voice
  • β€’ Past tickets = faster resolution (learned fixes)
  • β€’ Narrow tool set = reliable, domain-specific
  • β€’ Human escalation = safety net for edge cases
Performance: 3-8s per ticket, 70% autonomous resolution rate

Common Patterns Across Production Systems

βœ…Always Include

  • Max iterations: Every production agent has hard limits (10-30)
  • Timeouts: Kill runaway tasks (30-120s typical)
  • Error handling: Graceful degradation, not crashes
  • Logging: Every LLM call, tool execution logged
  • Cost tracking: Monitor spend per user/task

⚑Performance Optimizations

  • Caching: Cache tool results (weather valid for 30min)
  • Parallel calls: Execute independent tools simultaneously
  • Model tiers: GPT-4 for planning, GPT-3.5 for execution
  • Streaming: Stream responses for perceived speed
  • Prefetching: Load likely-needed context early

οΏ½Safety Measures

  • Sandboxing: Code execution in isolated containers
  • Rate limiting: Max N API calls per minute
  • Input validation: Schema checking before tool calls
  • Human approval: Require confirmation for destructive actions
  • Audit trails: Full logs for compliance/debugging

πŸ“ŠMonitoring Metrics

  • Success rate: % of tasks completed successfully
  • Latency: P50, P95, P99 response times
  • Cost per task: Total LLM + tool costs
  • Tool usage: Which tools called most often
  • Error types: Categorize and track failure modes

Decision Framework: Choosing Components

Answer these questions to architect your agent:

1. What's your quality vs cost vs speed priority?
  • β€’ Quality first: GPT-4, long-term memory, Reflexion loop
  • β€’ Speed first: GPT-3.5/Claude, no memory, simple ReAct
  • β€’ Cost first: Llama 3 (self-hosted), short-term only, Plan-Execute
2. Are tasks predictable or exploratory?
  • β€’ Predictable: Plan-Execute (booking flights, data processing)
  • β€’ Exploratory: ReAct (research, debugging, creative tasks)
  • β€’ Error-prone: Reflexion (API integrations, web scraping)
3. Do you need context from past interactions?
  • β€’ No: No memory (stateless Q&A, calculations)
  • β€’ Within session: Short-term memory (multi-turn conversations)
  • β€’ Cross-session: Long-term memory (personalized assistants, support)
4. How many tools does the agent need?
  • β€’ 1-5 tools: Specialized agent (code assistant, booking bot)
  • β€’ 5-15 tools: General assistant (ChatGPT, virtual assistant)
  • β€’ 15+ tools: Split into multiple specialized agents

🎯Getting Started: Recommended Stack

For 80% of use cases, start with this proven combination:

Reasoning: GPT-4 (plan) + GPT-3.5 (execute) for cost efficiency
Memory: Short-term conversation context (add long-term later if needed)
Tools: 3-7 focused tools for your domain
Loop: ReAct (flexible, debuggable, works for most cases)
Guardrails: Max 20 iterations, $5 budget, 60s timeout

✨ This stack handles customer support, code assistance, research, and most business workflows.