Every AI-written change, accounted for.
IAS turns intent into governed action. For enterprise engineering, it runs Claude Code, Codex CLI, and Gemini CLI under one harness, inside your own repos. Before a run starts, you can see the context it loads, the checks that gate it, and where human approval sits.
Coding agents reached your repos
before your controls did.
Engineering teams adopted agents one pilot at a time. Each run loads whatever context the operator typed, passes whatever review happened to be there, and leaves whatever record someone thought to keep. You are accountable for the output without a single control you can point to.
of enterprises remain in agent pilots, short of scaled rollout
of AI agent initiatives fail before reaching production
EU AI Act high-risk obligations take effect. DORA already applies.
Four gaps an examiner finds
before you do.
None of these are hypotheticals. They are what agent adoption looks like when the controls were built for human-only commits.
No evidence trail
An agent rewrote the auth module last Tuesday. There is no record of the context it read, the checks that ran, or who approved the change.
Approvals by accident
Human sign-off happens wherever someone thought to look, not where policy requires it. Two repos give two different answers to the same audit question.
Context nobody can inspect
What the agent knew lives in one engineer’s prompt history. You cannot show an examiner what it was given, or prove what it was not.
Standards that live in people
Your most careful engineer runs agents well. That is a habit, not a control, and it does not transfer to the next hundred operators.
You can't hand-review
everything your agents write.
And you shouldn't have to.
IAS moves the review into the run itself: context set before the agent starts, automated checks on every step, approval exactly where your policy puts it.
From intent to a change you can defend.
Walk through it: state the intent, watch IAS refine it and attach the right context, then approve the plan before any code changes.
* Simulated workflow for demonstration purposes
One checkpoint every run passes through.
IAS sits between your coding agents and your repositories. Each run picks up its context, checks, and approval points on the way in, and leaves its evidence on the way out.
Ungoverned Agents
Claude Code, Codex CLI, and Gemini CLI as they arrive: no shared context, no shared checks, no record of what they did.
IAS
Control Plane
Defensible Changes
Code that ships with its context, checks, and approvals on record. The evidence exists before anyone asks for it.
The parts that do the governing.
Each one is inspectable: what a run loaded, what gated it, and what it left behind.
The Harness
The standards and rules a run needs travel as context packs; validators and approval boundaries load with them, the same way every time.
IAS Control Plane
Which runs are live, which controls applied, and where the exceptions sit, across every repo.
Context Lake
Architecture decisions, constraints, and domain language, versioned and attached by name to every run that used them.
Set the controls now,
or retrofit them later.
Agents are already inside.
Your engineers run coding agents today, sanctioned or not. The only open question is whether the controls arrive before the audit does.
Habits harden fast.
Once every team has its own agent workflow, a common standard becomes a migration project. Put the harness in place now and new adoption inherits it.
Put IAS in front of
your risk team.
Start with self-service pricing or schedule a conversation for an enterprise rollout. We will walk through a real run end to end: the context it loaded, the checks that gated it, and the evidence it left.