Now, no feature
is left behind.
agI3R is the governed delivery lifecycle for AI coding agents — it governs Claude Code, Codex, and Grok across GitHub and Azure DevOps. Agents draft — specs, code, schemas, designs — and humans decide, at a gate, on the record, every time.
An AI agent will happily change code nobody asked it to touch, and report success either way.
Work starts before requirements are written down, so “done” is whatever the demo survived.
When compliance asks who approved the change an AI wrote, a Slack thread is not an answer.
The I3R lifecycle
Four stages, each with its own human gate. Status is projected from an immutable event stream — never directly written, by anyone.
An AI interviewer asks you structured questions until requirement coverage passes — then writes the spec you approve.
The coding agent works in an isolated sandbox against the approved spec. Its output becomes a proposal, not a commit.
Humans review the diff, comment inline, request changes. The agent revises. Every round is an immutable event.
Approved proposals are applied, pushed, and opened as pull requests. The final merge is always yours.
The console is the paper trail
Interviews that write specs. Boards projected from events. Diff review with independent verification of what the agent actually did.
The board is a projection
Cards move because events happened — never the other way around. And a drag doesn’t edit a status field; it opens a gate.
Four guarantees, by construction
No agent output reaches your repo, your designs, or a committed plan without a person clicking Approve — under rules your policy defines.
Boards, reviews, and audit views are projections of one immutable stream. There is no hidden state to argue about.
Events are hash-chained. Deleting or rewriting history is detectable — your audit trail is verifiable, not just stored.
What the agent says it did is verified against what actually changed. Disagreement blocks the proposal — it is never papered over.
Any model. Same gates. A budget that holds.
Implement tasks run on Claude Code, OpenAI Codex, or xAI Grok — routed for cost, cross-checked for truth, and stopped by your budget, not your luck.
A registry routes each task class to the most cost-effective model, with fallback chains that absorb a provider outage. Changing a route needs no redeploy.
Every model call rolls up into a live spend view — and batch mode plus cache-friendly prompts cut the bill before governance ever has to.
Budget alerts at 80% and 100% — and with enforcement on, over-budget blocks new agent work, attributed as a budget stop in the audit trail.
A model that can't prove what it changed can't ship. Managed or bring-your-own keys, with on-prem lanes for code that must not leave.
Runs where your data lives — including fully on-prem
The coding agent already works where your repositories are. Now the whole stack can stay inside your network: agent execution, the knowledge base, and the vector search index can all run on your own servers. Nothing about your code or documentation has to leave your walls.
- ✓Your code never leavesAgents run on infrastructure you control; the platform sees reviewed changes, not a copy of your codebase.
- ✓On-prem vector searchThe documentation index and semantic search run on a vector store you host — your knowledge stays your knowledge.
- ✓Sovereign by deploymentAir-gapped and regulated environments supported: point the platform at your own hosts, keys, and models.
- ✓Attributed & tamper-evidentEvery AI action is tied to a licensed person and recorded so it cannot be quietly altered — audit on your terms.
Real products, brought up to AI speed under control
A multi-tenant CRM and a performance-management platform — each onboarded in minutes and moved through a governed lifecycle where every AI change is reviewed and recorded.
Ship with agents. Stay in control.
Sign in with your work account to run your first Ideate → Implement → Iterate → Release loop — or talk to us about your team.