Source-linked coding agent operations console

For

Engineering teams and technical leads running AI coding agents on their own codebases

Solves

Coding agents are scattered across several rented tools, so runs, fixes and approvals are hard to trace, reproduce or keep inside the team's own environment.

Delivers

A source-linked agent run record with reviewed diffs and merge requests

Built in

about 4 weeks of creation time, MVP in 5 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

Reduce manual edit-run-fix work while keeping every agent action reviewable and inside the team's own environment.

1. Run a coding agent that writes, tests and debugs code.
2. Execute locally on the user's machine against local files.
3. Run cloud-hosted agents for continuous background work.
4. Run several agents in parallel, each on a chosen model.
5. Scan and index the codebase into a symbol map and AST index.
6. Detect test failures and attempt fixes with stack trace primacy and write-scope locking.
7. Detect runtime errors and apply fixes to shorten the edit-run-fix loop.
8. Make changes and open merge requests across several repositories in one session.
9. Schedule recurring agent runs.
10. Drive a browser to click, capture screenshots and run flow tests.
11. Perform cross-app actions such as typing, clicking and switching applications.
12. Read console and network logs during runs to locate issues.
13. Generate and edit documents and spreadsheets in Office suites.
14. Commit, push and pull from inside the terminal.
15. Generate files and scaffolding from a stated goal.
16. Refactor individual files on request.
17. Keep turn memory compact by discarding raw tool output and passing a struct.
18. Represent MCP tools as local markdown skills and lazy-load schemas.
19. Float an always-on-top agent window above other applications.
20. Transcribe voice input on-device so audio never leaves the machine.
21. Work with existing CLI agents or a built-in agent.
22. Build custom agents with common frameworks.
23. Connect GitHub, Slack, Jira and Gmail for end-to-end automation.
24. Deploy locally with Docker or on remote servers.
25. Give agents a Vulkan 3D runtime for rendering, terrain and physics work.
26. Compare the reviewed result with the recorded baseline and value assumptions.
27. Capture corrections and named-owner approval before consequential use.
28. Export a versioned source-linked agent run record with reviewed diffs and merge requests, source references and unresolved questions.

Everything these tools do, in one app

• AI coding agent An AI agent that performs software development tasks such as writing, testing, and debugging code.Found in Airuncode, Keen Code, Codex 3.0 by OpenAI and 4 more
• Local-first execution Runs the agent directly on the user's machine and works on local files.Found in Airuncode, Keen Code, Backgrind and 1 more
• Cloud-hosted agents Runs agents in the cloud without local installation, allowing continuous background work.Found in Agen, Codex 3.0 by OpenAI
• Multi-agent parallel execution Runs several coding agents at the same time, each able to use a different model.Found in Airuncode, Backgrind
• Codebase scanning and indexing Scans the codebase to build a global symbol map and AST index that agents use to understand code structure.Found in Airuncode
• Self-healing test failures Detects test failures and attempts automated fixes, with stack trace primacy and write-permission scope locking.Found in Airuncode, NOVA, Agen
• Automated error remediation Detects runtime errors or tracebacks and applies fixes automatically to shorten the edit-run-fix loop.Found in NOVA, Airuncode, Agen
• Multi-repository support Lets a single agent session make changes and open merge requests across several repositories.Found in Agen
• Scheduled agent runs Runs agents on a schedule for recurring or background tasks.Found in Agen
• Browser automation Simulates clicks, captures screenshots, and runs flow tests in a browser.Found in Codex 3.0 by OpenAI, SWE-Kit
• Cross-app computer control Performs actions like typing, clicking, and switching between applications.Found in Codex 3.0 by OpenAI
• Realtime debugging logs Uses console and network logs to identify and fix issues during runs.Found in Codex 3.0 by OpenAI
• File generation for office suites Generates and edits documents and spreadsheets in Microsoft Office and Google Drive.Found in Codex 3.0 by OpenAI
• In-terminal Git operations Commit, push, and pull without leaving the development environment.Found in NOVA
• Build from goal Describe what you want and the tool generates the necessary files and scaffolding.Found in NOVA
• Refactoring on demand Request refactors for individual files.Found in NOVA
• Context-efficient turn memory Discards raw tool inputs/outputs after each turn and passes a compact struct to keep context lean across multi-turn sessions.Found in Keen Code
• Lazy-loaded MCP skills Represents MCP tools as local markdown Skills and lazy-loads JSON schemas only when requested.Found in Keen Code
• Floating overlay window An always-on-top window that floats the agent above any desktop application, including fullscreen games.Found in Backgrind
• Voice input on-device Transcribes voice input locally with whisper.cpp so audio never leaves the machine.Found in Backgrind
• Bring your own agent Works with existing CLI agents like Claude Code, Cursor, or Codex, or provides a built-in agent.Found in Backgrind
• Framework-agnostic agent building Works with frameworks such as LangChain, LlamaIndex, CrewAi, and Autogen to build custom agents.Found in SWE-Kit
• Third-party integrations Connects with platforms like GitHub, Slack, Jira, and Gmail for end-to-end automation.Found in SWE-Kit
• Flexible deployment Can be deployed locally using Docker or on remote servers.Found in SWE-Kit
• Vulkan 3D runtime A native Vulkan 3D runtime that gives agents access to rendering, terrain, and physics systems for game development.Found in Airuncode

What goes in, what comes out

The workflow

1. In
   Start with

   Repository access, build and test commands, runtime logs, browser flows and approval rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository access

4. 3

   Build and test commands

5. 4

   Runtime logs

6. 5

   Browser flows and approval rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   A source-linked agent run record with reviewed diffs and merge requests

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the 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 approved repository set and build environment; final code review and merge 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: Agent run setup, Live run console, Review and merge queue. Use a repository and task list, a large central run timeline with diffs and logs, and a right-hand panel for permissions, model choice and approvals. Let users compare agent branches side by side. Display queued, running, needs review, approved and failed states. Provide a client or stakeholder preview link with comments anchored to the relevant diff. Make the task-specific outcome a source-linked agent run record with reviewed diffs and merge requests visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, run history, reviewer comments, approval states, model and tool 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

Team-owned repositories, CI systems and issue trackers. Cloud code hosting, chat and mail platforms, and document suites. Start with file exchange and validate destination specifications before promising direct publishing. 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

   5 days

   One buyer segment, one recurring use case; first modules: run a coding agent that writes, tests and debugs code; scan and index the codebase into a symbol map and AST index. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 teams and technical leads running AI coding agents on their own codebases use it to solve "coding agents are scattered across several rented tools, so runs, fixes and approvals are hard to trace, reproduce or keep inside the team's own environment"?
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 agent changes per engineering hour and rework after merge.
4. Measure, then decide. Track accepted agent changes per engineering 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 approved repository set and build environment; final code review and merge decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: run a coding agent that writes, tests and debugs code; scan and index the codebase into a symbol map and AST index. Support the remaining modules with operator review: detect test failures and attempt fixes with stack trace primacy and write-scope locking; detect runtime errors and apply fixes to shorten the edit-run-fix loop; make changes and open merge requests across several repositories in one session. 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 a source-linked agent run record with reviewed diffs and merge requests. Retain the explicit scope boundary: One approved repository set and build environment; final code review and merge 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 approved repository set and build environment; final code review and merge 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 game-development work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked agent run record with reviewed diffs and merge requests. 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 manual edit-run-fix work while keeping every agent action reviewable and inside the team's own environment. Demonstrate a concrete source-linked agent run record with reviewed diffs and merge requests using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample source-linked agent run record with reviewed diffs and merge requests from a small authorized input set, with a transparent calculation of accepted agent changes per engineering hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and technical leads running AI coding agents on their own codebases and inspect a recent example of coding agents scattered across several rented tools, so runs, fixes and approvals are hard to trace, reproduce or keep inside the team's own environment.
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 agent changes per engineering 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 agent changes per engineering 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 agent changes per engineering 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 a source-linked agent run record with reviewed diffs and merge requests. 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 run configurations, repository 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 teams and technical leads running AI coding agents on their own codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Airuncode, Keen Code, Codex 3.0 by OpenAI, NOVA, Agen, Backgrind and SWE-Kit, plus in-house scripts and manual review. Compare this product with the buyer's present method on accepted agent changes per engineering 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 and compute usage, 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 a source-linked agent run record with reviewed diffs and merge requests. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and merge scope. One approved repository set and build environment; final code review and merge 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.