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name: three-layer-agent-stack description: Use when building AI-powered products or agents, when raw model intelligence isn't enough to solve user problems, or when designing the architecture for agentic workflows

The Three-Layer Agent Stack

Overview

A framework for building effective AI agents by synchronizing innovation across three distinct layers: Model, API, and Harness. Success requires tight integration—not treating the model as a black box.

Core principle: Features like "compaction" (long-running tasks) require simultaneous changes across all three layers.

The Stack

Copy┌─────────────────────────────────────────────────────────────────┐
│  LAYER 3: HARNESS / PRODUCT LAYER                               │
│  ─────────────────────────────────────────────────────────────  │
│  The environment that executes actions and provides context     │
│  • VS Code / IDE integration                                    │
│  • Terminal / Shell access                                      │
│  • Sandbox / Secure execution environment                       │
├─────────────────────────────────────────────────────────────────┤
│  LAYER 2: API LAYER                                             │
│  ─────────────────────────────────────────────────────────────  │
│  Interface handling state, context windows, and orchestration   │
│  • Context management / Compaction                              │
│  • State handoff between sessions                               │
│  • Tool routing and formatting                                  │
├─────────────────────────────────────────────────────────────────┤
│  LAYER 1: MODEL LAYER                                           │
│  ─────────────────────────────────────────────────────────────  │
│  Foundation model providing reasoning and intelligence          │
│  • Code generation / Reasoning                                  │
│  • Summarization for compaction                                 │
│  • Environment-specific training                                │
└─────────────────────────────────────────────────────────────────┘

Key Principles

Common Mistakes

• Model-only optimization: Changing model without adapting harness
• Generic API assumptions: Assuming generic API supports agentic behaviors
• No feedback loop: Harness doesn't feed back to model training

Real-World Example

Implementing "Compaction" to allow Codex to run 24 hours:

• Model: Must understand summarization
• API: Must handle the context handoff
• Harness: Must prepare and format the payload

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Source: Alexander Embiricos (OpenAI Codex) via Lenny's Podcast