Source-linked coding agent context and orchestration console

For

Engineering teams running AI coding agents across repositories, issue trackers and documentation

Solves

Coding agents lack project context and their work is not verified, so engineers re-state conventions and re-check results by hand.

Delivers

Reviewed, source-linked task context and verified agent results

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

Reduce repeated context briefing and manual agent checking while keeping engineers in control.

1. Aggregate code, issues and documentation into one searchable source.
2. Fetch relevant context from Jira, GitHub, Notion and Slack.
3. Ingest project context and linked documentation from tasks automatically.
4. Link related code files, past similar issues and relevant documents per ticket.
5. Attach relevant docs to tasks for terminal retrieval.
6. Construct prompts for coding agents from fetched context.
7. Support concise requests such as resolving an issue by ID.
8. Orchestrate multiple coding agents using existing subscriptions.
9. Verify that agents complete tasks correctly.
10. Apply CRISPE prompt structure with constraints and formatting rules.
11. Parse messy ticket comments for repo names and acceptance criteria.
12. Write back documentation updates as agents work.
13. Build requested features such as task trackers from prompts.
14. Run workflows, side chat, crons, inline previews and remote access as plugins.
15. Respect existing permissions and record agent actions in audit logs.
16. Provide connectors to scope which projects and spaces are processed.
17. Run cross-platform in a browser via server mode.
18. Allow local cloning and customization under an open license.

Everything these tools do, in one app

• Context aggregation Collects code, issues, and documentation into one searchable source for AI tools.Found in Stash MCP Server
• Context fetching Automatically gathers relevant information from tools like Jira, GitHub, Notion, and Slack.Found in coolplugz
• Automatic context ingestion Consumes project context and linked documentation directly from tasks to reduce re-stating conventions.Found in Knowns CLI
• Issue-aware context Links related code files, past similar issues, and relevant documents for each ticket.Found in Stash MCP Server
• Task linking Attaches relevant docs to tasks for straightforward context retrieval from the terminal.Found in Knowns CLI
• Automated prompt writing Constructs prompts for Claude Code based on fetched context so you don't spell out every instruction.Found in coolplugz
• Simple command flow Enables concise requests to an assistant, like resolving an issue by ID, with minimal prompting.Found in Stash MCP Server
• Agent orchestration Manages multiple coding agents like Claude Code, Codex, OpenCode, and Cursor using existing subscriptions.Found in bb
• Task verification Checks that the coding agent actually completes tasks correctly rather than assuming success.Found in coolplugz
• CRISPE prompt structuring Applies a predefined structure to prompts with constraints and formatting rules.Found in coolplugz
• Custom models for Jira parsing Uses Hugging Face models to extract repo names and acceptance criteria from messy ticket comments.Found in coolplugz
• Write-back capability Allows the AI to update or create documentation as it learns while working.Found in Knowns CLI
• Prompt-driven self-modification Lets you ask for features like a task tracker, and the tool builds the UI and creates skills for agents.Found in bb
• Plugin architecture Implements workflows, side chat, crons, inline previews, and remote access as plugins.Found in bb
• Access controls and audit logs Respects existing permissions in connected systems and records agent actions for transparency.Found in Stash MCP Server
• Integration helpers Provides connectors for common development platforms to scope which projects and spaces are processed.Found in Stash MCP Server
• Server mode Runs cross-platform in a browser via npx bb-app@latest.Found in bb
• Open source and MIT licensed Allows cloning and running the software locally with full customization.Found in bb

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted code, issues, documentation and agent output

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted code

4. 3

   Issues

5. 4

   Documentation and agent output

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked task context and verified agent results

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 fixed repository set and connected workspace; final code review and merge decisions remain with engineers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connection and scope, Task context and agent run, Verification and audit. Use a project list with connection status, a central task view showing fetched context, generated prompt and agent output, and a right-hand panel for sources, permissions and review state. Let users compare agent runs side by side. Display draft, changes requested and approved states. Provide an audit view with each agent action linked to its source. Make the task-specific outcome reviewed, source-linked task context and verified agent results visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, connection scopes, agent run history, review states, usage allowances, task limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, audit logs and explicit approval for external actions.

Integrations and data access

Customer-owned repositories, issue trackers, documentation spaces and agent subscriptions. Cloud code storage, tracker import/export and CI destinations. Start with file exchange and validate destination specifications before promising direct merge or 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: aggregate code, issues and documentation into one searchable source; fetch relevant context from Jira, GitHub, Notion and Slack. 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 AI coding agents across repositories, issue trackers and documentation use it to solve "coding agents lack project context and their work is not verified, so engineers re-state conventions and re-check results by hand"?
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: Verified agent task completions per engineer hour and rework after agent handoff.
4. Measure, then decide. Track verified agent task completions per engineer hour and rework after agent handoff; 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 fixed repository set and connected workspace; final code review and merge decisions remain with engineers. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate code, issues and documentation into one searchable source; fetch relevant context from Jira, GitHub, Notion and Slack. Support the third module with operator review: construct prompts for coding agents from fetched context. 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, source-linked task context and verified agent results. Retain the explicit scope boundary: One fixed repository set and connected workspace; final code review and merge decisions remain with engineers.

What the build depends on. Repository access 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 fixed repository set and connected workspace; final code review and merge decisions remain with engineers.

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 and workspace package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or enterprise integration separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked task context and verified agent results. 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 repeated context briefing and manual agent checking while keeping engineers in control. Demonstrate concrete reviewed, source-linked task context and verified agent results using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewed, source-linked task context and verified agent results from a small authorized input set, with a transparent calculation of verified agent task completions per engineer hour and rework after agent handoff and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running AI coding agents and inspect a recent example of coding agents lacking project context and unverified work.
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 verified agent task completions per engineer hour and rework after agent handoff, 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: Verified agent task completions per engineer hour and rework after agent handoff. 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

Verified agent task completions per engineer hour and rework after agent handoff; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked task context and verified agent results. 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 prompt structures, connection scopes 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 AI coding agents across repositories, issue trackers and documentation. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

coolplugz, Stash MCP Server, Knowns CLI and bb, plus manual copy-paste between trackers, repositories and agent terminals. Compare this product with the buyer's present method on verified agent task completions per engineer hour and rework after agent handoff. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, agent subscription 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 reviewed, source-linked task context and verified agent results. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code accuracy and usage permissions. Engineers approve substantive changes and merge scope. One fixed repository set and connected workspace; final code review and merge decisions remain with engineers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.