Ship AI code fast.
Make it reliable.
Your team moved fast with AI-assisted coding — until it started drifting. Flaky output, silent regressions, tests that pass but shouldn’t. We audit the harness around your AI and engineer it for stability at scale.
The Drift
Fast became fragile.
AI didn’t make your codebase worse overnight. It drifted — one unreviewed suggestion, one skipped test, one “looks right” at a time.
Flaky output
The same prompt ships different code twice. Nobody can reproduce the bug.
Silent regressions
An AI-generated change “fixes” one thing and quietly breaks three others — with no test that would have caught it.
Tests that rot
The AI writes tests for its own code that assert whatever it just produced. Green stops meaning anything.
2am incidents
Production breaks in ways no human wrote and no human fully understands.
Eroded trust
Reviewers rubber-stamp or re-do everything. The green check stopped meaning “safe.”
Slowing velocity
The speed you bought with AI is being repaid in rework, hotfixes, and hesitation.
The Harness
It’s not the model. It’s the harness.
A capable model with a weak harness is a fast way to ship unreliable software — whether your team prompts a copilot, generates whole modules, or runs autonomous agents. The harness is everything around the model that turns probabilistic output into a dependable system, starting with a test harness that actually exercises AI-generated code. We audit six dimensions of it.
Guardrails & validation
Is AI output checked — types, contracts, schemas — before it can merge or ship?
Test harness for AI code
Do your tests genuinely exercise AI-generated code — or did the AI write tests that just confirm whatever it produced?
Context & generation design
Are the prompts, context, and agent loops that produce your code engineered — or improvised?
CI/CD reliability gates
What stops a bad AI change from reaching production automatically?
Observability & incidents
When AI-driven behavior breaks, can you see it and trace it fast?
Rollback & determinism
Can you reproduce, pin, and safely roll back non-deterministic behavior?
The Audit
The AI Harness Audit
A focused, founder-led review of how your team builds with AI — and where reliability leaks out. Free, no obligation.
Map
We walk your stack, workflow, and AI tooling across all six harness dimensions.
Find
We pinpoint the gaps where drift, regressions, and incidents actually originate.
Roadmap
You get a prioritized reliability roadmap — highest-leverage fixes first.
~14 days from kickoff to a roadmap you own and can act on with or without us.
Book the audit →Sample Finding
What a finding looks like
34 of 41 AI-generated modules had tests written by the same AI in the same pass — asserting whatever the code already did, not what it should do. CI was green on every release while two silent regressions shipped to production. The test harness was confirming the AI, not checking it.
Recommendation
Recommended: behavior-first tests and golden-output evals on the 41 modules, plus a CI gate that blocks merges on coverage or eval drift. Est. 1 week, removes the top incident source.
Services
Start free. Go as deep as you need.
Audit
Free~14 days
We map your harness across six dimensions, find the gaps, and hand you a prioritized reliability roadmap.
Book the audit →Harden
Fixed scope2–6 weeks
We implement the highest-leverage guardrails, tests, evals, and CI gates straight from your roadmap.
Talk scope →Embed
OngoingMonthly
A reliability partner on your team — continuous harness improvement and on-call for AI reliability.
Discuss →Why Apidrift
We treat reliability as an engineering discipline, not a vibe.
Most teams bolt AI onto a workflow built for humans and hope the tests hold. They don’t. Reliability with AI is a design problem — guardrails, evals, context, and gates engineered on purpose. That’s the only thing we do, and we do it with the calm of people who’ve cleaned up the 2am incidents.
Questions
Objections, handled.
Isn’t this just linting and tests?
Those are pieces. The harness is the whole system that makes a probabilistic model behave predictably — validation, evals, context design, gates, observability, and rollback. We audit all six, not just the easy two.
How do you test code the AI generated?
We build a test harness that exercises the actual behavior the code should have — not the behavior the AI assumed. Behavior-first tests, golden-output evals, property and contract checks, and CI gates that fail when an AI-generated change drifts. The point is tests that can disagree with the AI, instead of rubber-stamping it.
Will this slow my team down?
The opposite. Reliability gaps are what slow you down — rework, incidents, and lost trust in your own pipeline. A good harness lets you keep moving fast, safely.
We already have CI. Isn’t that enough?
CI that mostly runs tests the AI wrote to pass itself gives false confidence. We check whether your gates actually catch AI-specific failure modes — and most don’t.
Is the audit really free?
Yes. It’s how we start a relationship. If the roadmap is valuable and you want help executing it, that’s where paid work begins — entirely your call, no obligation.
How much of my team’s time does it take?
A couple of conversations and read access. We do the heavy lifting; you get a roadmap in about two weeks.
Find the gaps before your users do.
Five free harness audits a month. Claim one while it’s open.
Book your free harness audit →