AI lab by Hupyy · Developer tool infrastructure
Verify AI-generated code before it ships.
FeelGoot verifies AI-generated code before teams accept it with evidence reports, CI completion gates, intent drift detection, and fake-green test detection.
Product
Let the agent propose. Make the system verify.
FeelGoot creates a separate evidence boundary around coding agents, pull requests, CI, code review, and agent evaluation workflows.
Verify the intent
Compare the ticket, prompt, acceptance criteria, constraints, and non-goals against the actual diff.
Score the evidence
Separate strong tests from shallow checks, skipped paths, over-mocking, and implementation-shaped assertions.
Flag agent shortcuts
Catch stubs, mocks, hardcoded success, broad catch blocks, risky files, and unsupported completion claims.
Gate completion
Allow, block, or request more evidence before AI-generated work becomes accepted engineering work.
Evidence receipt
A completion receipt humans can audit.
Agent summaries optimize for sounding finished. FeelGoot evidence reports optimize for proving what changed and what remains uncertain.
- ✓Intent mapping from request to code and tests.
- ✓Shortcut detection for stubs, mocks, skips, and hardcoded success.
- ✓Risk summary with blockers, weak proof, and unknowns.
| Question | Agent narration | FeelGoot evidence receipt |
|---|---|---|
| Did it satisfy the original task? | “I implemented the feature.” | Maps requested intent to changed files and acceptance criteria. |
| Are tests meaningful? | “All tests pass.” | Classifies strong tests, weak tests, skips, mocks, and fake-green patterns. |
| What should reviewers inspect? | Reviewer starts from scratch. | Reviewer starts from blockers, unknowns, risky files, and evidence gaps. |
| Should this be accepted? | Optimized for sounding done. | Optimized for proving what changed and what remains uncertain. |
Use cases
Built for teams that care what ships.
Use FeelGoot where AI-generated code touches production, regulated systems, customer-facing software, infrastructure, or high-cost review queues.
PR review
Review AI-generated pull requests
Surface intent mismatches, weak tests, risky files, and hidden shortcuts before approval.
CI gate
Gate done claims in CI
Add an evidence checkpoint between agent output and accepted engineering work.
Agent evals
Evaluate coding agents by evidence
Measure whether agents satisfy tasks, not just whether they generate plausible diffs.
Safety
Support high-assurance workflows
Use traceable evidence reports around critical code paths and formal-methods-adjacent workflows.
Platform
Wrap existing coding agents
Keep your preferred agents while adding an independent verification boundary.
Governance
Create AI engineering receipts
Give reviewers and managers a compact record of generated work and acceptance risk.
Search + answer readiness
Clear category language for Google, answer engines, and AI crawlers.
The site defines FeelGoot as AI coding agent verification, then supports that entity with docs, glossary pages, comparison pages, FAQs, llms.txt, sitemap.xml, and crawl-ready robots.txt.
Entity definition
FeelGoot = evidence layer for AI coding agents.
Topical authority
Dedicated pages for AI-generated code review, completion gates, fake-green tests, intent drift, agent evaluation, and evidence reports.
Machine-readable discovery
Robots, sitemap, llms.txt, ai-manifest.json, structured data, RSS, canonical URLs, and accessible HTML.
FAQ
Direct answers.
What is FeelGoot?
FeelGoot is an evidence layer for AI coding agents. It verifies AI-generated code against intent, changed files, test evidence, and risk before acceptance.
What category is FeelGoot in?
FeelGoot is in AI coding agent verification, evidence-based AI-generated code review, CI completion gates, and AI agent evaluation.
What does FeelGoot detect?
FeelGoot is designed to detect intent drift, fake-green tests, stubs, mocks, hardcoded success, missing evidence, risky file changes, and unsupported completion claims.
Is FeelGoot a coding agent?
No. FeelGoot verifies the work produced by coding agents.
Who is FeelGoot for?
FeelGoot is for engineering teams, AI agent builders, platform teams, and high-assurance software teams using AI-generated code.
Give AI coding agents an evidence gate.
Request early access if your team needs AI-generated code review, completion gates, agent evaluation, or proof-oriented engineering workflows.