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Three-Layer Stack for Effective AI Agents

by Alexander Imbirikos on December 14, 2025

Building AI Coding Agents That Accelerate Human Work

The future of AI coding agents isn't just about writing codeā€”it's about becoming a true software engineering teammate that participates across the entire development lifecycle. Alexander Imbirikos, product lead for Codex at OpenAI, shares insights on how AI coding agents are evolving and the principles that make them effective.

The Three-Layer Stack of Effective AI Agents

  • An effective AI agent requires three integrated layers working together:
    • A smart reasoning model that knows how to do specific tasks well
    • An API layer that understands agent concepts and provides appropriate endpoints
    • A harness layer that prepares payloads and manages the environment
  • Building all three in parallel with a tightly integrated product and research team enables rapid iteration and experimentation
  • Features like "compaction" (allowing models to run for extended periods) require coordination across all three layers

Principles for Building Effective AI Coding Assistants

From Pairing to Proactivity

  • Current state: "A really smart intern that refuses to read Slack and doesn't check Datadog unless you ask it to"
  • Evolution path:
    • Start with interactive pairing (easier adoption)
    • Progress to delegation of specific tasks
    • Eventually reach proactive assistance where the agent identifies what needs to be done

The Productivity Bottleneck

  • The current limiting factor for AI productivity is human typing speed and validation time
  • "If you think of how many times people could actually get benefit from a really intelligent entity, it's thousands of times per day"
  • The goal is to create agents that are "helpful by default" without requiring explicit prompts

Building Trust Through Progressive Capability

  • Start with side-by-side work to build trust and configure the environment
  • As users work with the agent, they naturally configure it for their specific needs
  • Eventually transition to delegating larger tasks once trust is established

The Path to Autonomous Coding Agents

Validation as the Key Unlock

  • The biggest bottleneck isn't writing code but reviewing and validating AI-generated code
  • Focus on building tools that help validate work and build confidence in AI-written code
  • "Reviewing agent-written code is a place that today is less fun" - solving this is a priority

Contextual Awareness

  • Agents need to understand the context they're working in and team preferences
  • The most effective agents can access and understand:
    • The codebase structure and patterns
    • Team guidelines and preferences
    • Existing tools and systems

Code as the Universal Interface

  • "The best way for models to use computers is simply to write code"
  • Code is composable and interoperable
  • Even non-coding tasks can be more effectively accomplished through code
  • This suggests coding agents may become the foundation for all AI agents

Practical Implementation Advice

  • Give Codex your hardest problems, not trivial tasks
  • Try things in parallel to build trust (understand codebase, formulate plans, execute small tasks)
  • For complex projects, work with the agent to create a plan.md file with verifiable steps
  • Configure the agent to validate its own work when possible
  • Focus on making humans feel maximally accelerated rather than replaced

The Future of Work with AI Coding Agents

  • Boundaries between roles will blur as AI compresses the "talent stack"
  • The value of deep customer understanding increases as building becomes easier
  • The most successful implementations will be those where AI feels like a natural extension of human capability
  • "If you're gonna build a super assistant, it has to be able to do things"
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