The Agent Architecture Stack

A comprehensive mental model showing how AI agents are built from foundational components to production-ready systems

LAYER 6
📊

Evaluation & Monitoring

Feedback Loop for Continuous Improvement
Systematic measurement and tracking of agent performance across task completion, quality, tool usage, and system metrics. Enables iterative enhancement through data-driven insights.
🎯 Task Completion Metrics
✨ Quality Assessment
🔧 Tool Interaction Analysis
⚡ System Performance (latency, tokens)
🤖 LLM-as-Judge Evaluation
📈 Trajectory Tracing
↓ Measured outputs inform improvements ↓
LAYER 5
🌐

External Integrations

Real-World Data & Action Capabilities
Connect agents to external systems for dynamic data access and action execution. Includes APIs, databases, search engines, and emerging standardized protocols.
🔍 Web Search (SerpAPI, Tavily, Bing)
🗄️ SQL Databases (PostgreSQL, MySQL)
📚 Vector Databases (Chroma, Pinecone)
🔌 REST APIs & Webhooks
🔐 OAuth & API Key Authentication
🔗 Model Context Protocol (MCP)
↓ External data feeds agent decisions ↓
LAYER 4
🧠

Memory Systems

Context Continuity Across Time
Create the illusion of memory by managing context across interactions. Enables coherent multi-turn conversations and personalized experiences over time.
💬 Short-Term Memory (session-level)
📖 Long-Term Memory (cross-session)
🔄 Full Conversation History
🪟 Sliding Window Strategy
📝 Summarization Techniques
💾 Vector Storage for Retrieval
↓ Memory informs current execution ↓
LAYER 3
🔄

State Management

Tracking Progress During Execution
Manage ephemeral execution context that exists during a single task. State machines provide predictable, testable workflows with clear transition logic.
📋 Original User Query
⚙️ System Instructions
💬 Message History Buffer
🛠️ Tool Calls (pending/completed)
📊 Intermediate Results
🔀 Conditional Transitions
↓ State coordinates execution flow ↓
LAYER 2
🔧

Function Calling & Tool Integration

The "Glue" Between Reasoning and Action
Extend agent capabilities through programmatic interfaces. Function calling enables models to recognize when tools are needed, format structured requests, and execute external actions.
📐 Math & Computation Functions
🔍 Search & Retrieval Tools
💻 Code Execution (Python)
📊 Data Transformation Tools
🎯 Structured Output Schemas
✅ Pydantic Validation
↓ Tools execute based on LLM decisions ↓
LAYER 1
🤖

LLM Core (Foundation)

Stateless Reasoning Engine
The foundational large language model that provides reasoning, language understanding, and generation capabilities. By default, it's stateless—treating each prompt independently without memory or context.
🧩 Natural Language Understanding
💭 Reasoning & Planning
✍️ Text Generation
🎯 Instruction Following
🔍 Pattern Recognition
📊 Structured Output Generation

🎯 Key Architectural Insights

Foundation is Stateless
The LLM core has no inherent memory or state. All higher layers exist to add these capabilities.
Tools Transform Agents
Function calling (Layer 2) is the critical "glue" that turns passive language models into active problem-solvers.
State ≠ Memory
State (Layer 3) is ephemeral execution context. Memory (Layer 4) persists across tasks and sessions.
External Integration Enables Reality
Layer 5 connects reasoning to real-world systems—APIs, databases, search—making agents practical.
Evaluation Drives Improvement
Layer 6 closes the feedback loop, revealing where agents succeed or fail and enabling iterative enhancement.
Each Layer Depends on Below
You can't have memory without state, or external integrations without tools. Build progressively from Layer 1 up.