Source-linked coding agent operations console

For

Engineering teams running coding agents and multi-step automation on their own codebases

Solves

Coding work is split across several rented model subscriptions, so agent runs, long-context analysis, security checks and search sit in separate tools with no shared review trail.

Delivers

Source-linked, reviewer-approved agent runs and patches

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Consolidate rented model subscriptions into one owned console for coding agents, multi-step automation and large-context software engineering.

1. Break complex tasks into ordered steps and carry them out over longer runs.
2. Generate and edit code for software engineering tasks.
3. Run autonomous agents that execute longer chains of actions and tool calls.
4. Process very large codebases and extended sessions in one context.
5. Run multiple agents in parallel and coordinate them on one task.
6. Interpret images and visual input alongside text.
7. Check generated code and reasoning to catch simple mistakes automatically.
8. Keep context coherent across steps and separate sessions.
9. Offer commands and effort settings to steer code review and reasoning depth.
10. Find security flaws in code and apply automated patches.
11. Run inside company infrastructure to keep sensitive data private.
12. Work across many languages for global teams.
13. Let teams download and customize the model under a license.
14. Provide an API so developers can integrate the console into their own apps.
15. Find relevant information in large datasets beyond simple keyword matching.
16. Handle text, documents and structured data in one tool.
17. Update search indexes immediately as new information arrives.
18. Narrow search results with user-defined filters.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before merge or deployment.
21. Export a versioned, source-linked agent run and patch set with source references and unresolved questions.

Everything these tools do, in one app

• Multi-step planning Breaks complex tasks into ordered steps and carries them out over longer runs.Found in Gemini 3.7 Flash, Google Gemini 3.8 Flash and Cyber, Muse Spark 1.1 by Meta AI and 3 more
• Code generation Writes and edits code for software engineering tasks.Found in Gemini 3.7 Flash, Google Gemini 3.8 Flash and Cyber, Muse Spark 1.1 by Meta AI and 4 more
• Agentic workflow support Runs autonomous agents that execute longer chains of actions and tool calls.Found in Gemini 3.7 Flash, Google Gemini 3.8 Flash and Cyber, Muse Spark 1.1 by Meta AI and 6 more
• Long context window Processes very large documents, codebases, or extended conversations in one session.Found in Muse Spark 1.1 by Meta AI, Step 3.5 Flash, Claude Opus 4.6 and 2 more
• Multi-agent orchestration Lets multiple agents run in parallel and coordinate on a task.Found in Muse Spark 1.1 by Meta AI, Claude Opus 4.6
• Multimodal understanding Interprets images and visual input alongside text.Found in Muse Spark 1.1 by Meta AI, Claude Opus 4.7
• Output verification Checks generated code and reasoning to catch simple mistakes automatically.Found in Claude Opus 4.7
• Session memory Keeps context coherent across multiple steps or separate sessions.Found in Claude Opus 4.7
• Developer controls Offers commands and effort settings to steer code review and reasoning depth.Found in Claude Opus 4.7
• Vulnerability detection Finds security flaws in code and applies automated patches.Found in Google Gemini 3.8 Flash and Cyber, Gemini 3.6 Flash Family
• On-premise deployment Runs inside company infrastructure to keep sensitive data private.Found in Command A
• Multilingual support Works across many languages for global teams.Found in Command A
• Open weights Lets teams download and customize the model under a license.Found in Step 3.5 Flash, Command A, Kimi K2
• API access Provides an API so developers can integrate the model into their own apps.Found in Google Gemini 3.8 Flash and Cyber, Muse Spark 1.1 by Meta AI, Step 3.5 Flash and 1 more
• Context-aware search Finds relevant information in large datasets beyond simple keyword matching.Found in DeepSeek-V3-0324
• Multiple data formats Handles text, documents, and structured data in one tool.Found in DeepSeek-V3-0324
• Real-time indexing Updates search indexes immediately as new information arrives.Found in DeepSeek-V3-0324
• Customizable filters Narrows search results with user-defined filters.Found in DeepSeek-V3-0324

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, documents, tickets and tool outputs

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Documents

5. 4

   Tickets and tool outputs

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked, reviewer-approved agent runs and patches

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured plans and generate candidate code, patches and search results for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints, sandboxing and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository scope and approved tool set; final code review, security sign-off and deployment decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Run setup and repository scope, Editable agent run and diff preview, Review and release. Use a run list for projects, a large central canvas for plans, diffs and evidence, and a right-hand panel for sources, tool calls, constraints and comments. Let users compare agent runs and model settings side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant file, line or step. Make the task-specific outcome source-linked, reviewer-approved agent runs and patches visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, tool permissions, 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, CI pipelines and permitted documentation sources. Cloud and on-premise storage, code-host 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

   5 days

   One buyer segment, one recurring use case; first modules: break complex tasks into ordered steps; generate and edit code; run autonomous agents with tool calls. 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 running coding agents and multi-step automation on their own codebases use it to solve "coding work is split across several rented model subscriptions, so agent runs, long-context analysis, security checks and search sit in separate tools with no shared review trail"?
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 runs per engineering hour and corrections after merge.
4. Measure, then decide. Track accepted agent runs per engineering hour and corrections 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 scope and approved tool set; final code review, security sign-off and deployment decisions remain human. Implement one approved input format, a bounded representative case set and the first three task modules: break complex tasks into ordered steps; generate and edit code; run autonomous agents with tool calls. Support the remaining modules with operator review: output verification, vulnerability detection, long context, multi-agent orchestration, session memory, developer controls, multimodal input, on-premise deployment, multilingual support, open weights, API access, context-aware search, multiple data formats, real-time indexing and customizable filters. 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 source-linked, reviewer-approved agent runs and patches. Retain the explicit scope boundary: One repository scope and approved tool set; final code review, security sign-off and deployment 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 scope and approved tool set; final code review, security sign-off and deployment 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 scope. Offer a monthly production allowance after repeat demand. Quote complex multi-agent, on-premise or security-review work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked, reviewer-approved agent run and patch set. 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 rented model subscriptions into one owned console for coding agents, multi-step automation and large-context software engineering. Demonstrate a concrete source-linked, reviewer-approved agent run and patch set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams running coding agents and multi-step automation 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, reviewer-approved agent run and patch set from a small authorized input set, with a transparent calculation of accepted agent runs per engineering hour and corrections after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running coding agents and multi-step automation on their own codebases and inspect a recent example of coding work split across several rented model subscriptions.
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 runs per engineering hour and corrections 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 runs per engineering hour and corrections 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 runs per engineering hour and corrections 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 source-linked, reviewer-approved agent runs and patches. 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 running coding agents and multi-step automation on their own codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Gemini 3.7 Flash, Google Gemini 3.8 Flash and Cyber, DeepSeek-V3-0324, Muse Spark 1.1 by Meta AI, Step 3.5 Flash, Claude Opus 4.6, Command A, Kimi K2, Claude Opus 4.7 and Gemini 3.6 Flash Family, used today as separate rented subscriptions. Compare this product with the buyer's present method on accepted agent runs per engineering hour and corrections 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 attempts, sandbox and compute time, 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 source-linked, reviewer-approved agent runs and patches. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code provenance, license terms, attribution and usage permissions. Named engineers approve substantive changes, security patches and deployment scope. One repository scope and approved tool set; final code review, security sign-off and deployment decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.