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Use case · engineering

Ship AI systems in twelve weeks that survive an audit.

Coding agents make an individual engineer faster. They don't make a programme faster, because programmes are slowed by unsigned specs, missing tests, integration surprises and a release process nobody trusts. This is agents applied to the whole lifecycle, with human gates where regulated enterprises need them.

The problem

Why this queue costs what it costs.

Speed at the keyboard isn't the constraint

Teams adopt coding assistants and are surprised when delivery dates don't move. The bottleneck sits in specification, review, integration and release confidence.

Regulated delivery has extra gates for good reason

In healthcare and financial services, an unexplained change in a production decision system is a finding. Velocity that can't evidence itself is a liability.

How it works

What the agent does, and where the human stays.

Step 01

Sign the spec first

Intent becomes a blueprint, data contracts, acceptance criteria and an initial eval suite — including failure and abuse cases. The sponsor signs; nothing is built from an unsigned spec.

Step 02

Build in bounded scopes

Build agents write code and tests and open pull requests with explanations, in scopes small enough for a human to review properly.

Step 03

Gate every change

Behaviour, safety and cost evals run in CI. A change that regresses any of the three blocks.

Step 04

Roll out in stages

Shadow, then approve, then bounded autonomy — each expansion signed by the risk owner and logged.

Guardrails

The constraints that make it deployable.

These are not aspirations. They are enforced in the build, checked by the eval suite and visible in the audit trail.

01No direct pushes to protected branches; engineers review and merge everything.
02Secrets never appear in prompts or logs; dependency and licence scanning on every PR.
03Eval gates cannot be bypassed without a named approver and a recorded reason.
04Cost per transaction is a test — doubling it fails the gate even when accuracy passes.
05Every human signature logged and readable straight into the governance pack.
Measurement

What the sponsor sees every month.

The baseline is agreed with Finance before we start. These are the lines on the console — and what our outcome fee is read from.

Days from signed spec to first shadow run

Cycle time, with review time separated

Defect escape rate per release

Cost per merged pull request

Rework after build

Agents involved
Voyager Spec WriterVoyager Build AgentVoyager Eval GatekeeperVoyager Codebase AssessorArgos Token Sentinel
See each agent's inputs and guardrails →
Typical timeline

12 weeks to first production outcome

Blueprint and eval scaffolding by week 2, shadow mode by week 8, approve mode with a measured result by week 12 — then autonomy expands on evidence.

How AIM sequences it →
Let's talk

Tell us the number you need to move.

A 45-minute working session with an operator who has run the kind of work you are describing. You will get an honest read on where your programme stands and what it would take to move it.