checkout-api · d7a31e01 / Baseline
Sixty learns normal behavior
Four queries, 182ms p95, and 24 rows returned becomes the release baseline.
Database calls4per checkout·p95 response182msnormal release
Production intelligence for AI-native teams
Your agents ship all day. Who watches production?
When code generation accelerates, production understanding becomes the bottleneck. Sixty catches the regressions that tests and review cannot see, names the deploy, and sends the evidence back to your coding agent.
From generated change to verified fix
playing
01
A developer asks the coding agent for a small refactor. The agent reads the repository, changes the code, and runs the test suite.
132×34sergio@macbook ~/code/checkout-api main % codex
7 days free60s to install0 dashboards0 user identities
The typical failure
The diff looks reasonable. The behavior is not.
An agent moves a fetch into a reusable component. Tests pass. Review sees clean code. With real carts and real data, one request now makes a database call for every item.
+ items.map(async item =>+ await db.price.find(item.id))same code≠same behavior
checkout-apiDatabase calls4 → 37p95 response182ms → 1.8s
Every individual query is fast. Only the count is wrong. That is why conventional error tracking—and the pull request—stay green.
What Sixty does after every deploy
Find the regression. Give your agent what it needs to debug and fix it.
This is the actual workflow: Sixty learns a baseline, compares the next release, finds the first deploy where behavior changed, and sends a compact evidence package to your coding agent over MCP.
What gets measured
The failures that look fine in a pull request.
No synthetic quality score. Sixty watches the production behavior that generated code can actually change.
Performance drift
Latency, database calls, rows, external requests, and AI token use—compared across releases.
Silent failures
Empty results, rejected requests, and output shapes that change without throwing an exception.
Change attribution
The first release where behavior moved, with the operation and code location responsible.
The quality loop your agents are missing
Every generated change gets a production answer.
Your agent already knows how to edit code. Sixty supplies what it cannot know from the repository: how the last edit behaved with real traffic and real data. That answer becomes the starting context for the next fix.
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Keep the speed. Add the production feedback loop.
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