Agent code verification and test console

For

Engineering teams using AI coding agents on production repositories

Solves

AI coding agents claim work is complete while features are half-implemented, tests are claimed but never run, and dummy data is substituted, so reviewers cannot trust the output.

Delivers

Reviewer-approved verification report linked to the original request

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce review time spent re-checking agent claims while catching silent failures before merge.

1. Ingest agent conversation history and prior instructions.
2. Check agent actions against the requested goals.
3. Detect half-implemented features and claimed-but-unrun tests.
4. Flag substituted dummy data and stubbed logic.
5. Review full pull requests and diffs for logic errors and unhandled edge cases.
6. Generate frontend and backend test plans from project specifications.
7. Execute tests and identify root causes of failures.
8. Feed failures back to the coding agent until requirements are met.
9. Accept natural language requests without manual test scripting.
10. Produce phased plans describing what to change, why and in which order.
11. Work with the team's existing coding agents.
12. Verify proposed diffs against the plan and flag gaps and regressions.
13. Select only relevant files during planning and iteration.
14. Connect to IDEs and agents through an MCP server.
15. Run from the CLI, in CI or as an agent skill with local models or existing API keys.
16. Capture corrections and named-owner approval before merge.

Everything these tools do, in one app

• Conversation-history verification Reviews an agent's actions in the context of prior instructions and exchanges to ensure alignment with requested goals.Found in Imbue, Vet
• Silent failure detection Identifies issues like half-implemented features, tests that were claimed but never run, and substituted dummy data.Found in Imbue, Vet
• PR and diff review Flags logic errors, unhandled edge cases, and deviations from stated goals across full pull requests.Found in Imbue, Vet
• Flexible deployment Works with local models, uses existing API keys, runs from the CLI, in CI, or as an agent skill.Found in Imbue, Vet
• Open source Source code is available for inspection and customization.Found in Imbue, Vet
• Zero telemetry Ships with zero telemetry by default for privacy-conscious workflows.Found in Imbue, Vet
• MCP Server integration Connects directly with IDEs and AI coding agents to validate and improve code automatically.Found in TestSprite 2.0
• Automatic test plan generation Generates detailed test plans and cases for both frontend and backend components based on project specifications.Found in TestSprite 2.0
• Test execution and analysis Executes tests with failure analysis and root cause identification to enhance debugging efficiency.Found in TestSprite 2.0
• Continuous feedback loop Refines code with AI coding agents until it meets the original requirements.Found in TestSprite 2.0
• Natural language interaction Eliminates the need for manual prompt writing or test scripting.Found in TestSprite 2.0
• Specification-first workflow Produces phased plans describing what to change, why, and in which order.Found in Traycer AI
• Agent-agnostic execution Allows teams to use their existing AI coding agents rather than switching tools.Found in Traycer AI
• Automated verification against plan Checks proposed diffs against the plan, flags gaps, and detects regressions.Found in Traycer AI
• Context-aware file selection Includes only relevant files during planning and iteration.Found in Traycer AI
• In-editor review and team configuration Supports shared rules and prompt templates for collaboration.Found in Traycer AI

What goes in, what comes out

The workflow

1. In
   Start with

   Agent conversation history, repository diffs, specifications and test results

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent conversation history

4. 3

   Repository diffs

5. 4

   Specifications and test results

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved verification report linked to the original request

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 and agent configuration per pilot; final merge and release 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: Repository and agent session intake, Verification workbench, Reviewer sign-off and report. Use a project list for repositories and agent sessions, a large central diff and evidence canvas, and a right-hand panel for the original request, plan, test results and comments. Let users compare claimed work against verified work side by side. Display pending, gaps found and approved states. Provide a shareable report link with findings anchored to the relevant file and line. Make the task-specific outcome reviewer-approved verification report linked to the original request visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository access, agent session versions, reviewer comments, approval states, usage allowances, review 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 agent sessions and permitted specifications. Version control, CI systems, IDE extensions and agent APIs. Start with file exchange and validate destination specifications before promising direct merge. 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: ingest agent conversation history and prior instructions; check agent actions against the requested goals. 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 using AI coding agents on production repositories use it to solve "AI coding agents claim work is complete while features are half-implemented, tests are claimed but never run, and dummy data is substituted, so reviewers cannot trust the output"?
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 diffs per review hour and escaped defects after merge.
4. Measure, then decide. Track verified diffs per review hour and escaped defects 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 and agent configuration; final merge and release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: ingest agent conversation history and prior instructions; check agent actions against the requested goals. Support the third module with operator review: detect half-implemented features and claimed-but-unrun tests. 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 the reviewer-approved verification report linked to the original request. Retain the explicit scope boundary: One repository and agent configuration; final merge and release decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous verification jobs, editable version history, reviewer access and tested export formats. High-fidelity verification requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository and agent configuration; final merge and release 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 verification allowance after repeat demand. Quote complex multi-repository or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved verification report linked to the original request. 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 review time spent re-checking agent claims while catching silent failures before merge. Demonstrate a concrete reviewer-approved verification report linked to the original request using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewer-approved verification report linked to the original request from a small authorized input set, with a transparent calculation of verified diffs per review hour and escaped defects after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams using AI coding agents on production repositories and inspect a recent example of AI coding agents claiming work is complete while features are half-implemented, tests are claimed but never run, and dummy data is substituted.
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 diffs per review hour and escaped defects 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: Verified diffs per review hour and escaped defects 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

Verified diffs per review hour and escaped defects 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 reviewer-approved verification report linked to the original request. 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 verification rules, 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 using AI coding agents on production repositories. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Imbue, Vet, TestSprite 2.0, Traycer AI, manual code review and generic CI checks. Compare this product with the buyer's present method on verified diffs per review hour and escaped defects 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, test execution compute, storage, reviewer hours, client revision rounds and licensed source code access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewer-approved verification report linked to the original request. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license compliance and usage permissions. Engineering leads approve substantive changes and merge scope. One repository and agent configuration; final merge and release 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.