Governed multi-agent coding delivery workspace

Reduce rework and review load while keeping developers in control of consequential code changes. For engineering leads and platform teams running AI coding agents on real repositories, convert repository access, requirement notes, agreed decisions and coding conventions into reviewed, mergeable code changes with visible diffs and recorded rationale. The benefit is a testable hypothesis, measured through accepted changes per developer hour and rework after merge; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect repository access, requirement notes, agreed decisions and coding conventions, then follow this sequence: 1. Ask targeted clarifying questions before and during coding. 2. Produce structured plans and artifacts before writing code. 3. Check changes against agreed decisions and stop on deviation. Resolve uncertain cases with qualified reviewers, approve reviewed, mergeable code changes with visible diffs and recorded rationale, and measure accepted changes per developer hour and rework after merge against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and one agreed branch policy; final merge and release decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering leads approve substantive changes and merge scope. One repository and one agreed branch policy; final merge and release decisions remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One repository and one agreed branch policy; final merge and release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: ask targeted clarifying questions before and during coding; produce structured plans and artifacts before writing code. Support the third module with operator review: check changes against agreed decisions and stop on deviation. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned repositories, issue trackers and CI systems. Cloud code storage, design-file import/export and deployment destinations. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Requirement clarification and plan, Parallel agent sessions and worktrees, Review and merge. Use a repository and session list, a central diff and plan canvas, and a right-hand panel for decisions, questions and agent status. Let users compare agent branches side by side. Display draft, changes requested, verified and merged states. Provide a review link with comments anchored to the relevant diff hunk. Make the task-specific outcome reviewed, mergeable code changes with visible diffs and recorded rationale visible beside its evidence, review state and value baseline.