Spec-Driven Development with AI Agents

One of the most impactful experiments I have run as a program manager was designing a custom AI agent for spec-driven development. The premise was simple: what if detailed specifications could be translated into working code scaffolds with minimal human intervention?

The Problem

In large enterprise programs, the gap between requirements and implementation is where the most time is wasted. A product manager writes a PRD. An engineer interprets it. Misunderstandings emerge during code review. Rework follows. In my experience, this interpretation gap accounts for 20-30% of unnecessary sprint work.

The Approach

I designed an AI agent workflow that takes detailed specifications — written to a precise template I developed — and generates code scaffolds, test stubs, and acceptance criteria. The agent does not replace the engineer. It gives them a structured starting point that is already aligned with the specification.

The key innovation was the specification template. Most AI coding tools fail because the input is too vague. By standardizing how specifications are written — with explicit data models, API contracts, business rules, and edge cases documented — the AI agent has enough context to produce useful output.

The Results

Requirements-to-code cycle time decreased measurably. Engineers reported that starting from a generated scaffold reduced initial development time by roughly 40%. More importantly, the specification template itself improved requirements quality. When you know an AI agent needs precise inputs, you write better specs.

The PM's Role in AI-Assisted Development

I did not write the agent's code. I designed the workflow, created the specification template, defined success metrics, and managed the rollout. This is exactly the kind of work that technical program managers should be doing: identifying process bottlenecks, designing solutions that leverage AI, and measuring outcomes.

What I Would Do Differently

I would invest more in the feedback loop between generated code and specification quality. When the agent produces poor output, that signal should flow back to improve the specification template. We are building this feedback mechanism now.

The future of software delivery involves AI at every stage. Program managers who can design these workflows will define how teams build software.