Governed multi-agent coding delivery workspace

For

Engineering leads and platform teams running AI coding agents on real repositories

Solves

AI coding agents act on unclear requirements, drift from agreed decisions and produce changes that are hard to audit or merge.

Delivers

Reviewed, mergeable code changes with visible diffs and recorded rationale

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce rework and review load while keeping developers in control of consequential code changes.

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.
4. Carry out confirmed coding tasks mechanically.
5. Keep the developer involved at defined checkpoints.
6. Coordinate multiple agents in parallel with isolated worktrees and collision-free sessions.
7. Show exactly what changed and why in auditable diffs.
8. Run built-in AI models with a path to add more.
9. Run verify and research subagents for extra checks and code understanding.
10. Allow sessions to run autonomously under limits.
11. Integrate with VS Code for in-editor checks.
12. Fork and merge agent outputs.
13. Comprehend project structure, dependencies, patterns and history.
14. Make multi-file edits from natural language with visible modifications.
15. Accept plain-language specifications and delegate execution.
16. Retain long-term memory of preferences, past solutions and engineering knowledge.
17. Analyze code locally and use cloud only for advanced features.

Everything these tools do, in one app

• Clarifying questions Asks targeted questions to clarify requirements before and during coding.Found in Compyle, Verdent Deck
• Planning artifacts Produces structured plans and artifacts before writing code.Found in Compyle, Verdent Deck
• Validation during implementation Checks changes against agreed decisions and stops when something deviates.Found in Compyle
• Mechanical execution Carries out coding tasks once direction is confirmed.Found in Compyle
• Developer involvement Keeps the developer involved throughout the process.Found in Compyle
• Parallel agent orchestration Coordinates multiple AI agents running in parallel with isolated worktrees and collision-free sessions.Found in Verdent Deck
• Code review and diffs Shows exactly what changed and why, making diffs easy to audit.Found in Verdent Deck
• Integrated model support Includes built-in AI models (e.g., Claude Sonnet 4.5) with plans for more.Found in Verdent Deck
• Verify and Research subagents Provides additional checks and deeper code understanding.Found in Verdent Deck
• Autorun option Allows sessions to run autonomously.Found in Verdent Deck
• VS Code integration Integrates with VS Code for in-editor checks.Found in Verdent Deck
• Fork and merge outputs Tools to fork and merge agent outputs.Found in Verdent Deck
• Project understanding Comprehends entire project structure, dependencies, patterns, and historical changes.Found in Qoder
• Natural language multi-file edits Directs AI to make multi-file changes via chat with clear visibility of modifications.Found in Qoder
• Quest Mode Allows writing specifications in plain language and delegating execution to the AI agent autonomously.Found in Qoder
• Long-term memory Learns personal coding preferences, remembers historical solutions, and retains engineering knowledge.Found in Qoder
• Local-first processing Maximizes privacy with local code analysis, using cloud only for advanced features.Found in Qoder

What goes in, what comes out

The workflow

1. In
   Start with

   Repository access, requirement notes, agreed decisions and coding conventions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository access

4. 3

   Requirement notes

5. 4

   Agreed decisions and coding conventions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, mergeable code changes with visible diffs and recorded rationale

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, repository versions, review comments, approval states, usage allowances, session limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: ask targeted clarifying questions before and during coding; produce structured plans and artifacts before writing code. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering leads and platform teams running AI coding agents on real repositories use it to solve "AI coding agents act on unclear requirements, drift from agreed decisions and produce changes that are hard to audit or merge"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted changes per developer hour and rework after merge.
4. Measure, then decide. Track accepted changes per developer hour and rework after merge; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, mergeable code changes with visible diffs and recorded rationale. Retain the explicit scope boundary: One repository and one agreed branch policy; final merge and release decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository and one agreed branch policy; final merge and release decisions remain with the engineering team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or specialist migration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, mergeable code changes with visible diffs and recorded rationale. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce rework and review load while keeping developers in control of consequential code changes. Demonstrate a concrete reviewed, mergeable code changes with visible diffs and recorded rationale using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leads and platform teams running AI coding agents on real repositories professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, mergeable code changes with visible diffs and recorded rationale from a small authorized input set, with a transparent calculation of accepted changes per developer hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering leads and platform teams running AI coding agents on real repositories and inspect a recent example of AI coding agents act on unclear requirements, drift from agreed decisions and produce changes that are hard to audit or merge.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted changes per developer hour and rework after merge, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted changes per developer hour and rework after merge. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted changes per developer hour and rework after merge; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, mergeable code changes with visible diffs and recorded rationale. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved conventions, decision records and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering leads and platform teams running AI coding agents on real repositories. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Compyle, Verdent Deck and Qoder, plus manual coding and generic code assistants. Compare this product with the buyer's present method on accepted changes per developer hour and rework after merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, repository indexing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, mergeable code changes with visible diffs and recorded rationale. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.