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

┌─────────────────────────────────────────────────────────────────┐
│  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

Principle	Description
Full-Stack Iteration	Changes often need Model + API + Harness together
Harness Specificity	Models perform best when trained for specific environments
Feedback Loops	Product usage (Harness) must inform model training
Safety Sandboxing	Harness provides secure environment for code execution

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