Agentic software delivery control workspace

For

Engineering leads and platform teams shipping code changes under review and compliance rules

Solves

Delivery work is split across separate AI coding, ticketing, review and monitoring tools, so context, approvals and audit evidence are scattered.

Delivers

Reviewed, approved code changes linked to tickets and release evidence

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Consolidate planning, coding, review and release checks into one owned workspace.

1. Generate code and suggestions from repository context.
2. Work inside common editors and IDEs.
3. Handle multiple programming languages.
4. Refactor existing code automatically.
5. Detect errors and suggest fixes.
6. Support real-time shared code sessions.
7. Adapt models to team coding style and project needs.
8. Run multi-step agent workflows for planning, coding and checks.
9. Require human approval before merge or deployment.
10. Map relevant files, history and signals as context.
11. Enforce team rules and service-level settings.
12. Monitor deployments and surface post-release risks.
13. Apply security scans and compliance controls.
14. Run parallel agent sessions for separate tasks.
15. Edit AI-generated changes inline.
16. Show agent progress and status in one panel.
17. Accept commands, natural language, images and links as input.
18. Connect to Linear, Jira and similar boards.
19. Clarify tickets into defined tasks.
20. Open pull requests automatically.
21. Use multiple AI models for solution quality.
22. Build automation sequences visually.
23. Connect third-party data sources and services.
24. Define custom triggers and actions.
25. Scale across project sizes without rework.
26. Assign work to AI agents.
27. Break ideas into stories, sub-tasks and acceptance criteria.
28. Answer project status and bottleneck questions in natural language.
29. Expose an API for custom AI agents.
30. Run in local development, CI pipelines and editors.
31. Show transparent agent activity.
32. Support debugging, SQL and incident response tasks.
33. Manage routine tasks automatically.
34. Adapt workflows to specific business needs.
35. Report performance in real time.
36. Provide a dashboard for navigation and control.
37. Compare the reviewed result with the recorded baseline and value assumptions.
38. Capture corrections and named-owner approval before consequential use.
39. Export a versioned reviewed, approved code changes linked to tickets and release evidence with source references and unresolved questions.

Everything these tools do, in one app

• AI code generation Generates code and suggestions to speed up writing and reduce repetitive work.Found in AI-Dev, Revolte, Warp 2.0 and 2 more
• IDE and editor integration Works inside common development environments so users can stay in their editor.Found in AI-Dev, Exponent
• Multi-language support Handles code in a range of programming languages.Found in AI-Dev
• Automated code refactoring Restructures existing code automatically to improve quality.Found in AI-Dev
• Error detection Finds coding errors and suggests fixes.Found in AI-Dev
• Real-time code collaboration Lets multiple people share and work on code together live.Found in AI-Dev
• Customizable AI models Lets teams adapt the AI to match their coding style and project needs.Found in AI-Dev
• Agentic workflow automation Runs multi-step delivery tasks like planning, coding, and checks with AI agents.Found in Revolte, Warp 2.0, Producta and 2 more
• Human approval gates Requires people to review and approve changes before merge or deployment.Found in Revolte
• Context mapping Pulls relevant files, history, and signals to give the AI useful background.Found in Revolte, Producta
• Policy enforcement Applies team rules and service-level settings to workflows.Found in Revolte
• Post-deployment monitoring Watches deployments and surfaces risks after release.Found in Revolte, Factory, MGX
• Security and compliance controls Provides security scans and compliance alignment for regulated teams.Found in Revolte
• Parallel agent sessions Runs multiple AI agents at once for different tasks.Found in Warp 2.0
• Inline AI code editing Lets users edit AI-generated code changes directly without switching tools.Found in Warp 2.0
• Agent status panel Shows progress and status of all active AI agents in one place.Found in Warp 2.0
• Rich input support Accepts commands, natural language, images, and links as input.Found in Warp 2.0
• Ticket system integration Connects to project management boards like Linear and Jira.Found in Producta
• Ticket clarification Automatically refines tickets so tasks are clearly defined.Found in Producta
• Automated pull requests Creates and submits code changes as pull requests automatically.Found in Producta, Revolte
• Multiple AI model support Uses different advanced AI models to improve solution quality.Found in Producta
• Visual workflow builder Lets users design automation sequences with a visual interface.Found in Factory
• Third-party integrations Connects with popular data sources and external services.Found in Factory, MGX
• Custom triggers and actions Lets users define what starts a workflow and what it does.Found in Factory
• Scalable infrastructure Supports projects of different sizes without rework.Found in Factory
• Task assignment to AI agents Lets teams assign work directly to AI teammates.Found in Shortcut for Agents
• Idea-to-task breakdown Turns high-level ideas into stories, sub-tasks, and acceptance criteria.Found in Shortcut for Agents
• Conversational status queries Answers questions about project status and bottlenecks in natural language.Found in Shortcut for Agents
• Custom AI agent API Allows building custom AI agents for specific team needs.Found in Shortcut for Agents
• Runs across environments Works in local development, CI pipelines, and editors without extra tooling.Found in Exponent
• Transparent agent activity Shows exactly what the AI agent is doing to build trust and control.Found in Exponent
• Broad engineering task support Handles tasks like debugging Docker, writing SQL, and incident response.Found in Exponent
• Automated task management Reduces manual effort by managing routine tasks automatically.Found in MGX
• Customizable workflows Adapts automation sequences to specific business needs.Found in MGX
• Real-time analytics Provides live reporting to monitor performance.Found in MGX
• User-friendly dashboard Offers an easy interface for navigation and control.Found in MGX

What goes in, what comes out

The workflow

1. In
   Start with

   Repository context, tickets, policies and deployment signals

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository context

4. 3

   Tickets

5. 4

   Policies and deployment signals

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, approved code changes linked to tickets and release evidence

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 layout and approved language set; final code review and release decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository and context setup, Agent session board, Review and approval queue, Release and monitoring view. Use a project list, a central agent activity canvas, and a right-hand panel for policies, tickets and evidence. Let users compare proposed diffs side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant change. Make the task-specific outcome reviewed, approved code changes linked to tickets and release evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, usage allowances, change 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, authorized tickets and permitted deployment sources. Cloud code storage, editor import/export and release 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: generate code and suggestions from repository context; run multi-step agent workflows for planning, coding and checks. 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 shipping code changes under review and compliance rules use it to solve "delivery work is split across separate AI coding, ticketing, review and monitoring tools, so context, approvals and audit evidence are scattered"?
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 escaped defects after release.
4. Measure, then decide. Track accepted changes per engineering hour and escaped defects after release; 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 layout and approved language set; final code review and release decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate code and suggestions from repository context; run multi-step agent workflows for planning, coding and checks. Support the third module with operator review: require human approval before merge or deployment. 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, approved code changes linked to tickets and release evidence. Retain the explicit scope boundary: One repository layout and approved language set; final code review and release 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 layout and approved language set; final code review and release 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-repository or regulated compliance work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, approved code changes linked to tickets and release evidence. 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

Consolidate planning, coding, review and release checks into one owned workspace. Demonstrate a concrete reviewed, approved code changes linked to tickets and release evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leads and platform teams shipping code changes under review and compliance rules professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, approved code changes linked to tickets and release evidence from a small authorized input set, with a transparent calculation of accepted changes per engineering hour and escaped defects after release and no promised savings.

The first 30 days

1. Week 1: interview five engineering leads and platform teams shipping code changes under review and compliance rules and inspect a recent example of delivery work split across separate AI coding, ticketing, review and monitoring tools.
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 escaped defects after release, 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 escaped defects after release. 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 escaped defects after release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, approved code changes linked to tickets and release evidence. 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 policies, delivery constraints 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 shipping code changes under review and compliance rules. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI-Dev, Revolte, Warp 2.0, Producta, Factory, Shortcut for Agents, Exponent and MGX, plus manual code review and separate ticketing tools. Compare this product with the buyer's present method on accepted changes per engineering hour and escaped defects after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, model inference, 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, approved code changes linked to tickets and release evidence. 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 release scope. One repository layout and approved language set; final code review and release decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.