Agentic coding task delivery workspace

For

Engineering teams and technical leads delegating coding tasks to AI agents

Solves

Coding agents run in scattered tools, so plans, edits, tests, pull requests and cost records are hard to review, steer and audit.

Delivers

Reviewed, tested code changes linked to pull requests

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 code review and release authority with the team.

1. Plan coding tasks from repository and issue context.
2. Generate new code and context-aware edits.
3. Run multiple coding tasks or agents concurrently.
4. Connect to GitHub repositories and branches.
5. Open or update pull requests automatically.
6. Run each task in an isolated sandbox or worktree.
7. Track and steer running tasks in real time.
8. Provide browser and mobile access.
9. Support terminal-native operation.
10. Require plan approval before editing.
11. Pull tasks from issue trackers.
12. Support local-first execution on the team's machines.
13. Review diffs, edit files and run commands in one interface.
14. Organize and filter multiple agent sessions.
15. Pipe agent actions into CI/CD pipelines.
16. Connect external editors via a standard protocol.
17. Maintain long multi-turn coding sessions.
18. Accept dictated prompts.
19. Track token and dollar usage per session.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before merge.
22. Export a versioned reviewed, tested code changes linked to pull requests with source references and unresolved questions.

Everything these tools do, in one app

• AI coding agent An AI assistant that plans and performs coding tasks on your behalf.Found in Claude Code on the web, happycapy, GitHub Copilot Workspace and 7 more
• Code generation and edits Generates new code and makes context-aware edits to existing files.Found in Claude Code on the web, happycapy, GitHub Copilot Workspace and 6 more
• Parallel task execution Runs multiple coding tasks or agents concurrently.Found in Claude Code on the web, happycapy, Claude Code Desktop App Redesigned and 1 more
• GitHub integration Connects to GitHub repositories to work on code and changes.Found in Claude Code on the web, GitHub Copilot Workspace, Parallax and 1 more
• Automatic pull requests Creates or updates pull requests for review automatically.Found in Claude Code on the web, Parallax
• Isolated execution environments Runs each task in a contained sandbox or worktree with restricted access.Found in Claude Code on the web, happycapy, Parallax
• Real-time progress tracking Lets you monitor and steer tasks while they run.Found in Claude Code on the web, Agent Bar
• Browser and mobile access Use the tool from a web browser or mobile device.Found in Claude Code on the web, happycapy, Lovelace
• Terminal-native workflow Operates directly from the command line.Found in Cline CLI 2.0, kimiflare
• Plan-first approval Generates a change plan and waits for your approval before editing.Found in Parallax, GitHub Copilot Workspace
• Issue tracker integration Pulls tasks from issue trackers like Linear or GitHub Issues.Found in Parallax
• Local-first execution Runs code changes and agent behavior on your own machine.Found in Parallax
• Integrated editor and terminal Review diffs, edit files, and run commands in one interface.Found in Claude Code Desktop App Redesigned, GitHub Copilot Workspace
• Session management Organizes and filters multiple agent sessions in one place.Found in Claude Code Desktop App Redesigned, Claude Code on the web
• Headless CI/CD mode Pipes agent actions into automated build and deployment pipelines.Found in Cline CLI 2.0
• Editor protocol integration Connects external editors and tools via a standard protocol.Found in Cline CLI 2.0
• Large-context conversations Maintains long multi-turn coding sessions with a large context window.Found in kimiflare
• Voice input Dictate prompts instead of typing them.Found in Agent Bar
• Cost tracking Tracks token and dollar usage per session.Found in Agent Bar

What goes in, what comes out

The workflow

1. In
   Start with

   Repository access, issue-tracker tasks, coding conventions and review rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository access

4. 3

   Issue-tracker tasks

5. 4

   Coding conventions and review rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, tested code changes linked to pull 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 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 host and one CI provider; final code review, security checks 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: Task intake and plan approval, Agent run and diff review, Pull request and delivery. Use a session list for repositories and tasks, a large central diff and terminal view, and a right-hand panel for plan, tests, cost and comments. Let users compare agent runs side by side. Display planned, running, changes requested and merged states. Provide a review link with comments anchored to the relevant file and line. Make the task-specific outcome reviewed, tested code changes linked to pull requests visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository permissions, session history, review comments, approval states, usage allowances, agent run limits, export history and a rights record for supplied code. 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, authorized issue trackers and permitted CI/CD pipelines. Cloud code storage, editor protocol connections 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: plan coding tasks from repository and issue context; generate new code and context-aware edits. 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 teams and technical leads delegating coding tasks to AI agents use it to solve "coding agents run in scattered tools, so plans, edits, tests, pull requests and cost records are hard to review, steer and audit"?
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 pull requests per engineering hour and corrections after merge.
4. Measure, then decide. Track accepted pull requests 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 host and one CI provider; final code review, security checks and release decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: plan coding tasks from repository and issue context; generate new code and context-aware edits. Support the third module with operator review: run each task in an isolated sandbox or worktree. 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, tested code changes linked to pull requests. Retain the explicit scope boundary: One repository host and one CI provider; final code review, security checks 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 host and one CI provider; final code review, security checks 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 500-2,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or specialist migration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, tested code changes linked to pull 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 coordination overhead while keeping code review and release authority with the team. Demonstrate a concrete reviewed, tested code changes linked to pull 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 delegating coding tasks to AI 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, tested code changes linked to pull requests from a small authorized input set, with a transparent calculation of accepted pull requests per engineering hour and corrections after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and technical leads delegating coding tasks to AI agents and inspect a recent example of coding agents run in scattered tools, so plans, edits, tests, pull requests and cost records are hard to review, steer and audit.
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 pull requests 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 pull requests 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 pull requests 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 reviewed, tested code changes linked to pull 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 coding conventions, review examples and delivery constraints, 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 delegating coding tasks to AI agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Claude Code on the web, happycapy, GitHub Copilot Workspace, VibeShift MCP, Parallax, Claude Code Desktop App Redesigned, Cline CLI 2.0, kimiflare, Lovelace and Agent Bar. Compare this product with the buyer's present method on accepted pull requests 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 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 reviewed, tested code changes linked to pull requests. 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 host and one CI provider; final code review, security checks 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.