AI agent work, as a controlled delivery process.
Agent Orchestrator turns work items, repos, pull requests, and review feedback into governed AI-agent workflows with human oversight and visible writeback into the tools your teams already use.
Work items · repositories · model runtimes · governed writeback
AI coding tools are powerful. Unmanaged AI usage is a process problem.
A developer pastes context into a local agent, generates changes, and pushes. The organization never sees why it ran, what it used, what it changed, or what still needs approval.
- 1Manual context pasteno link to work item
- 2Local agent runcredentials on device
- 3Push to branchno record of model used
- 4PR opensteam has no visibility into AI work
- 1ai-enabledSource work itemADO User Story
- 2orchestratedAgent Job createdlinked to source
- 3runningBounded phasesrefine -> implement -> review
- 4auditedWriteback + PRvisible in ADO + portal
Source work item, to Agent Job, to writeback. Visible at every step.
A source work item exists
Every automation starts from a real business-facing item, initially an Azure DevOps User Story. Nothing in the platform happens without one.
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Operate AI work like any other part of delivery.
Setup overview
readySetup overview
One place to see the full integration: workspace, work item store, repositories, model access, credentials, routing, and ingestion. Diagnostics tell you what's ready and what isn't.
Guided setup turns disconnected systems into a ready operational workspace.
Setup is grouped into plain-language stages. Each step produces an inspectable artifact: a credential reference, routing rule, readiness check, or process setup result.
Connect
Workspace, work item store project, repositories.
- Create workspace
- Setup shell
- Work item store provider
- Connect project
- Connect repositories
Configure model access
Your credentials. Your model providers. Stored as references.
- Choose access approach
- OpenAI / Codex / Claude Code
- Default per setup
Route and ingest
Map source work to repositories and choose scheduled or event-driven ingestion.
- Routing rules
- Polling and webhooks
- Provider setup applied
Approve and go live
Bootstrap analysis, readiness checks, then operational overview.
- Repository bootstrap
- Readiness analyzed
- Setup overview
Connect the systems your teams already use, validate access, and launch with an inspectable readiness record.
Trust without compliance wallpaper.
Four operational pillars. Each one maps to a real surface in the product, not a slogan.
Customer-owned credentials
You bring your model provider keys. The platform stores references and scopes them to setups, not raw secrets pasted into prompts.
Bounded phases
Refinement, implementation, review, review-fix, writeback. Each phase has a known trigger, inputs, attempts, artifacts, and writeback target.
Visible attempts
Every run, retry, and failure is recorded with provider, inputs, outputs, and writeback result. The audit trail is the product.
Human controls
Authorized users can retry, cancel, rerun, poll, or repair a phase. Decision points are exposed; nothing important is hidden behind a chat.
Connect the delivery systems and model runtimes your teams already use.
Explicit boundaries across identity, credentials, data, and execution.
Kubernetes deployment
Containerized services and Kubernetes-ready configuration for controlled platform deployment.
Service isolation
Separate control-plane, provider, runtime, persistence, and portal service boundaries.
Customer-controlled models
Bring model credentials or connect compatible endpoints without embedding secrets in prompts.
Scoped access
Tenant, workspace, setup, and role boundaries constrain configuration and operational actions.
- Customer-owned credentials, scoped to setups
- Workspace and setup boundaries enforced in product
- Explicit model and provider configuration
- Auditable attempts, outputs, PRs, comments, writebacks
- Human review paths and authorized control actions
- Polling and authenticated webhook ingestion with replay protection
Bring AI work into the same process you already trust.
Connect your work-item systems, repositories, and model runtimes. Keep every automated step visible and governed.