The paradox: velocity isn't delivery

Agents are genuinely impressive at producing code. The problem is that software delivery isn't a typing contest. The hard parts live in alignment: choosing the right behaviour, integrating it safely, maintaining it, and proving it does what stakeholders expect.

The uncomfortable truth is that AI can shift effort out of writing and into verification. If you don't change how you control scope, you can end up with more code and less confidence.

The problem: agent drift

Agent drift is the growing gap between intended scope and delivered changes, caused by autonomous decisions compounding across a codebase.

Drift isn't moral failure or "bad prompting". It's an expected outcome of autonomy without constraints. Agents are trained to be helpful and complete tasks. They don't inherently know what your organisation considers "in scope" versus "nice to have".

1) The autonomy paradox

Autonomous agents are powerful precisely because they make decisions: file choices, abstractions, refactors, dependency upgrades, test strategies. Each decision is reasonable in isolation. But small deviations compound, and by the time humans review, the code reflects an approach the team never explicitly agreed to.

2) Why traditional PR review fails (by itself)

PR review is necessary, but it's a late-stage alignment mechanism. Reviewers see a diff. They often don't see the chain of intent: what the requirement was, what was out-of-scope, what trade-offs were accepted, and what evidence proves behaviour. When an agent produces a large PR, review becomes a forensic exercise.

3) The cost of drift

Drift shows up as rework cycles: clarifying scope after code exists, unwinding over-engineering, adding tests late, and patching edge cases under pressure. AI was meant to save time. But verification overhead can consume the gains if you don't redesign the delivery loop.

The insight: scope as the source of truth

The missing link isn't better AI. It's better alignment infrastructure.

Scope documents aren't new. What's new is making scope machine-readable and continuously validated. The scope becomes a contract the agent can be held accountable to—and the human team can use to approve deviations intentionally.

Old model vs new model

The mental model shift is simple:

• Old: Write scope → hand to agent → review output
• New: Write scope → agent works within scope → continuous alignment verification → humans approve deviations

What we do internally (and what you can copy without new tooling)

We've built an internal scope-first tool we use alongside coding agents on client work and our own products. We're not naming it here because it's not a public product—this post is about the pattern.

The workflow is two phases: generate scope, then close gaps. You can approximate it with a disciplined template and a few checks, even if you don't have tooling.

Architecture deep dive (conceptual)

The core architectural idea is to separate the human alignment layer from the execution layer. In our internal setup, we treat scope as a first-class artifact and run analysis/execution in controlled environments with tight credentials and an audit trail.

The bigger picture

The industry is moving from "AI writes code" to "AI works within constraints". Scope-first delivery isn't new—it's how good teams already operate. What AI changes is the need to continuously validate alignment at scale, because generation is now cheap and fast.

Trust requires verification. Verification requires scope. Teams that ship fastest will be the ones who can trust what their agents produce because alignment is built into the workflow.

Conclusion

AI coding agents are here to stay. The gap between generation and delivery doesn't have to be.

If your team is already using coding agents, the question isn't whether they can write the code—it's whether you can verify it matches the scope you intended, with evidence you can trust.