For teams

It starts as a verification harness. It becomes correctness intelligence.

For a team, the value compounds: every run creates data about how your team builds, verifies, revises, and accepts AI-generated changes.

The stakes

Cost is now the constraint.

By early 2026, AI spend had become a leadership problem. OpenAI's Sam Altman called it "a huge issue" — now one of the most common complaints from its enterprise customers — with companies reporting they had spent the year's budget by the end of Q1, and Uber burning through its annual agentic-AI budget within months.

The response has been a flight to cheaper models. By March 2026, open-weight Chinese models led by MiniMax's M2.5 and Moonshot's Kimi K2.5 were roughly 61% of the tokens running through OpenRouter's most-used models — and coding had grown to more than half of all tokens on the platform.

But a cheaper model only saves money if it stays correct on the changes that matter. Which model — and which checks — actually hold up, and which quietly let errors through, is exactly what your team's verification history can tell you.

Learn from your own data

Learn from the verification data your team creates.

For an individual developer, Adversarial TDD provides an independent check before trusting AI-generated code.

For a team, the value compounds because every run creates data about how your team builds, verifies, revises, and accepts AI-generated changes.

Adversarial TDD helps your team learn from your own data:

Your team learns

From the data generated by

Your team learnsWhere AI-generated changes most often need correction.

From the data generated byFindings, revisions, failed checks, and escalations.

Your team learnsWhich requirements tend to be underspecified.

From the data generated byRepeated clarification requests and unresolved uncertainty.

Your team learnsWhich verification checks find real issues.

From the data generated byAccepted findings, rejected findings, and downstream outcomes.

Your team learnsWhich model pairings expose different blind spots.

From the data generated byIncumbent / challenger results across repeated runs.

Your team learnsWhich engineering decisions keep recurring.

From the data generated byHuman approvals, rejections, waivers, and tradeoff decisions.

Your team learnsWhich issues still escape review.

From the data generated byProduction issues linked back to prior runs and gate configurations.

This is the difference between running a verification pass and building a correctness memory.

The first run helps you decide whether one change is safe to trust.

Repeated runs help your team learn where its AI-generated code tends to fail, which checks are worth running, and which decisions should inform future work.

Correctness intelligence

Adversarial TDD helps your team learn which verification work is worth doing.

Adversarial TDD does not merely record that a check happened. It records what the check found, whether the finding mattered, what changed, and what your team decided.

That makes it possible to answer practical questions from your own engineering data:

Which model / anti-pattern configurations are most cost-effective?
Which inexpensive models can replace costly variants without compromising quality?
Where do our AI-generated changes most often go wrong?
Which checks catch issues our normal review misses?
Which checks mostly produce noise?
Which assumptions keep coming back across features?
Which human decisions later proved risky?

The goal is not to run more agents.

The goal is to help you learn, from your own verification history, which forms of independent scrutiny actually reduce your risk of shipping wrong code.

Adversarial TDD starts as an independent verification harness. It becomes correctness intelligence.

Waitlist

Join the MVP waitlist.

Adversarial TDD is an early MVP for developers using AI-generated code on high-consequence changes.

Join the waitlist →