Max — trading systems engineer, Moscow

Your agents ship faster than your team can check them.

I build the layer that decides which agent output is safe to merge — evals, traces, tool limits, and the few places a human still has to look.

run 4f21c  span 3h42m  files 61  tests green
review approved in 4m by 1 human
status merged  unverified

The gap you already feel

Almost every engineer on your team uses coding agents daily. Almost none of them trust the output enough to put it in production without reading it line by line — and nobody has time to read it line by line.

So the work quietly splits in two: output that got checked, and output that got approved. Both look identical in the pull request. You find out which was which during an incident.

This is not a model problem. It's a missing harness — the scaffolding around the model that assembles context, restricts tools, records what happened, and tests the result. Most failed agent rollouts die here, not on model quality.

What I build

evals

Tests for agent behaviour, not just code

A suite that runs on every prompt or workflow change and fails loudly when the agent starts doing something new.

traces

A record of every run you can actually read

Tool calls, injected context, decisions. So a bad merge takes twenty minutes to explain instead of a day.

limits

Explicit boundaries on what agents may touch

Allowlisted tools, default deny, no autonomous writes to anything that costs money or wakes someone up.

gates

The short list of things a human must approve

Small enough that people actually do it, instead of rubber-stamping everything at 4 minutes per review.

Why me

I run Mercury , an algorithmic trading system I built and operate solo. In trading, unvalidated output isn't a code-quality opinion — it's money leaving the account within the hour. Evals, traces and hard limits aren't process there, they're the reason the thing still exists.

I'm bringing that discipline to teams whose agents write more code than anyone can read.

How we can work

2 weeks

Harness audit

I take your real agent runs, find where verification is missing, and hand back a working eval suite plus tracing on one critical workflow. fixed fee — scoped on first look

2–3 mo

Build the layer

Full validation path for agentic changes: evals in CI, trace retention, tool policy, and the review gates. Your team owns it when I leave. monthly retainer — scoped on audit

Start with one failed run

Send me a single agent run that went wrong — the trace, the diff, whatever you have. I'll tell you whether it was a prompt problem, a missing tool permission, or stale context, and what would have caught it. No charge, no call required.

[email protected]

kaido.team — one operator, a fleet of agents, under one flag.

the name