Multi-agent software delivery workspace

For

Engineering leads and product teams coordinating several AI coding agents on one codebase

Solves

Multiple AI coding agents work in isolation, so plans, code, tests and reviews are scattered and hard to verify before merge.

Delivers

Human-approved, tested changes linked to a merge decision

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce coordination overhead while keeping changes reviewable and mergeable.

1. Coordinate multiple AI agents with different roles on one software task.
2. Assign specific roles or models to different agents.
3. Keep shared project state and context accessible to agents and humans.
4. Pass work between agents through structured handoffs.
5. Generate test plans and diff-based reviews before merge.
6. Let human reviewers give feedback and suggestions to agents.
7. Store session state and data locally by default.
8. Keep source code open for inspection and modification.
9. Allow use without an account or subscription.
10. Carry useful context from one project to another when enabled.
11. Organize projects into workspaces and reusable configuration packages.
12. Cover build, deploy, operate and maintain stages.
13. Schedule tasks and mix outputs from multiple models with configurable priorities.
14. Connect to Git for version control and change tracking.
15. Generate software-related images through a dedicated art designer agent.
16. Keep the core implementation small to reduce overhead.
17. Define agent behavior in code rather than complex JSON.
18. Run agent-generated code in a sandbox.
19. Access models and tools from the Hugging Face Hub.
20. Support multiple large language model providers.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned human-approved, tested changes linked to a merge decision with source references and unresolved questions.

Everything these tools do, in one app

• Multi-agent orchestration Coordinates multiple AI agents with different roles to work together on software tasks.Found in Tonkotsu, Crew44, Gas City 1.0 and 1 more
• Role-based agent assignment Lets you assign specific roles or responsibilities to different agents or models.Found in Tonkotsu, Crew44, ChatDev
• Shared project context Keeps project state and context accessible to all agents and humans involved.Found in Tonkotsu, Crew44
• Structured handoffs Passes work between agents in an organized way so they can build on each other's output.Found in Tonkotsu, Crew44
• Built-in verification Provides test plans and diff-based reviews to check changes before they are merged.Found in Tonkotsu
• Human review interaction Allows human reviewers to give feedback and suggestions to the agents.Found in Tonkotsu, ChatDev
• Local-first storage Stores session state and data on your own machine by default.Found in Crew44
• Open source Source code is available for inspection, modification, and community contributions.Found in Crew44, Gas City 1.0, ChatDev and 1 more
• No account required Can be used without creating an account or subscription.Found in Crew44
• Cross-project memory Optionally carries useful context from one project to another.Found in Crew44
• Workspace and packaging Organizes projects into workspaces and reusable configuration packages.Found in Gas City 1.0
• Lifecycle support Covers building, deploying, operating, and maintaining software produced by agent workflows.Found in Gas City 1.0
• Task scheduling and mixing Schedules tasks and mixes outputs from multiple models with configurable priorities.Found in Gas City 1.0
• Git integration Connects to Git for version control and tracking changes.Found in ChatDev
• Art generation Generates software-related images through a dedicated art designer agent.Found in ChatDev
• Minimal codebase Keeps the core implementation small to reduce overhead and simplify understanding.Found in SmolAgents
• Code-first configuration Uses code rather than complex JSON files to define agent behavior.Found in SmolAgents
• Sandboxed execution Runs agent-generated code in a secure sandbox to prevent unsafe operations.Found in SmolAgents
• Hugging Face Hub integration Provides easy access to models and tools from the Hugging Face Hub.Found in SmolAgents
• Multiple LLM providers Supports using different large language model providers for flexibility.Found in SmolAgents

What goes in, what comes out

The workflow

1. In
   Start with

   Shared repository, task backlog, role definitions and review rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect a shared repository

4. 3

   Task backlog

5. 4

   Role definitions and review rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Human-approved, tested changes linked to a merge decision

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 supported language stack; final architecture, security and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace and task board, Agent run and diff review, Merge and release record. Use a project list for workspaces, a central run view with per-agent steps, and a right-hand panel for context, roles and review comments. Let users compare agent outputs side by side. Display planned, running, changes requested and approved states. Provide a reviewer view with comments anchored to the relevant diff line. Make the task-specific outcome human-approved, tested changes linked to a merge decision visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent role versions, reviewer comments, approval states, usage allowances, run 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 pipelines. Cloud code storage, Git hosting 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: coordinate multiple AI agents with different roles on one software task; assign specific roles or models to different agents. 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 product teams coordinating several AI coding agents on one codebase use it to solve "multiple AI coding agents work in isolation, so plans, code, tests and reviews are scattered and hard to verify before 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 engineering hour and defects found after merge.
4. Measure, then decide. Track accepted changes per engineering hour and defects found 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 supported language stack; final architecture, security and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: coordinate multiple AI agents with different roles on one software task; assign specific roles or models to different agents. Support the third module with operator review: keep shared project state and context accessible to agents and humans. 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 human-approved, tested changes linked to a merge decision. Retain the explicit scope boundary: One repository and one supported language stack; final architecture, security and merge decisions remain human.

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 supported language stack; final architecture, security and merge decisions remain human.

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-repo or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded human-approved, tested changes linked to a merge decision. 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 coordination overhead while keeping changes reviewable and mergeable. Demonstrate a concrete human-approved, tested changes linked to a merge decision using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leads and product teams coordinating several AI coding agents on one codebase professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample human-approved, tested changes linked to a merge decision from a small authorized input set, with a transparent calculation of accepted changes per engineering hour and defects found after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering leads and product teams coordinating several AI coding agents on one codebase and inspect a recent example of multiple AI coding agents work in isolation, so plans, code, tests and reviews are scattered and hard to verify before 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 engineering hour and defects found 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 engineering hour and defects found 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 engineering hour and defects found 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 human-approved, tested changes linked to a merge decision. 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 agent roles, review rules and merge 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 product teams coordinating several AI coding agents on one codebase. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Tonkotsu, Crew44, Gas City 1.0, ChatDev and SmolAgents, plus manual coordination in chat and issue trackers. Compare this product with the buyer's present method on accepted changes per engineering hour and defects found 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 calls, sandbox compute, 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 human-approved, tested changes linked to a merge decision. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license compliance and usage permissions. Engineering leads approve substantive changes and merge scope. One repository and one supported language stack; final architecture, security and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.