Where does your automation break?

Every team running unattended AI sits somewhere on this ladder. Most of the interesting failures happen in the gaps between rungs — the places where automation outgrows the practices that were fine at the last stage.

Manual Automated IaC Self-Service AI-Assisted Self-Healing

This system runs at the top two rungs. Every finding below was caught in its own infrastructure.

What breaks between rungs

These are real bugs, from this system's own automation, mapped to the transition where each one lives. The methodology that caught them is the review, offered as a service.

Automated → IaC — the gap between "it runs" and "it is defined"

Two production service definitions that existed only on the running machine and in no repository — unbacked up, and not reproducible by the installer that was supposed to create them
A deploy script that wrote config to one directory while the runtime read from another — three months of deploys went to a path nothing checked

IaC → Self-Service — the gap between "it is defined" and "it can be changed safely"

A scheduled task that committed to whatever git branch happened to be active — including feature branches belonging to the operator
A staging command that could silently sweep credentials and unrelated work into public commits

AI-Assisted → Self-Healing — the gap between "AI helps" and "AI runs unsupervised"

A generator that outpaced its reviewer by 40 batches in a single weekend, producing pure unreviewed debt
A financial calculator that accepted infinity, NaN, and zero as valid inputs — discovered before any reached a live order
A position-sizing rule that counted filled orders but not resting ones, allowing a spending cap to be silently exceeded
A circuit breaker that, once tripped, would file an identical escalation ticket every thirty minutes forever — with no concept that the problem it kept reporting had already been fixed
A safety counter that tallied failures from before the rules it was testing had changed, so revising those rules would have halted the pipeline permanently at exactly the moment it most needed to run
Seven scheduled jobs that failed at once for two days, because the watchdog was a scheduled job too — it shared the failure domain with the fleet it monitored

Every finding above was real, was fixed, and is logged in the repository that generates this page.

The review

Start here

Free scope check. Tell us where you think you sit on the ladder. You get back which rung you are actually on, what breaks at the next transition, and an honest answer if the answer is that you do not need this.

Get a free scope check →

$49

Public repo hardening. 10-check scope on public code. Operational bugs your tests do not cover: dirty state, missing kill switches, credential exposure, retry storms, scope creep across runs.

Request a review →

$99

Structural review. Describe your workflow in plain language. The system decomposes it into testable components and finds structural bugs — untyped gates, missing boundaries, implicit contracts — without seeing your code.

Request a review →

$199

Full hardening. Both scopes on private code. Operational and structural review, prioritized findings, remediation guidance, optional fix PR.

Request a review →

Questions a buyer would ask

What is this, exactly?

A fixed-scope reliability review of unattended AI automation. It looks for the failure modes that show up only when a workflow runs without a person watching it: dirty state carried between runs, missing kill switches, credential exposure, retry storms, and scope creep across executions.

What does the review actually check?

Operational checks (state hygiene, kill switches, credential handling, retry behavior, run boundaries) and, at the higher tiers, structural checks (untyped gates, missing boundaries, implicit contracts between components). The exact checklist depends on the tier — see the scope notes on each price above.

How much does it cost?

The scope check is free. After that: $49 for public-repo operational hardening, $99 for a structural review from a plain-language description of your workflow, or $199 for both scopes on private code with remediation guidance and an optional fix PR.

Who is this for?

Anyone running unattended AI automation — scheduled agents, autonomous pipelines, or scripts nobody watches in real time — who is about to move to the next rung and wants to know what breaks when they do.

How do I know the methodology works?

It was not designed in the abstract. Every finding on this page was caught in this system's own infrastructure, running at the top two rungs of the ladder, with real money at stake. Each is logged in the repository that generates this page. The example review shows two of them in full detail.