The Indie Founder Agentic QA Hardening Playbook
Harden AI-generated code for production before users find the bugs
For solo founders shipping AI-generated code who need production-grade reliability without a QA engineer or security team. This playbook catches architectural drift, adversarial agent failures, and spec regressions before they reach users. Use it after every major vibe-coded sprint or before any public launch.
Goal
Ship AI-generated code to production with confidence and zero critical regressions
Who this is for
Solo founders and indie hackers shipping apps built with AI coding tools like Cursor or Lovable
When to use
When you have a working vibe-coded app you want to harden before a public launch or paid tier rollout
When NOT to use
If your codebase is pre-MVP and still changing shape daily — harden once the core structure is stable
How to set it up
Scan your codebase for drift and security gaps
Run VibeDrift against your full codebase to identify where AI-generated code has drifted from your original architecture intent and flag security gaps to prioritise before launch.
Generate living specs from your current codebase
Run Specsight on your repo to auto-generate a living product spec document. Share this with stakeholders and use it as the source of truth for your AI coding tools going forward.
Red team every AI agent in your product
Run Agent Red Team against any AI agent or LLM-powered feature in your app. Use the adversarial test results to patch prompt injection vulnerabilities and edge case failures before public launch.
Add LLM firewall to every API call
Integrate Senthex with a single line of code around your LLM API calls to block malicious inputs, enforce content policies, and add runtime protection across your entire AI surface area.
Review all AI-generated PRs before merging
Configure Stage to review every pull request from your AI coding tools. Use the chapter-based review format to sanity-check logic changes without drowning in raw diffs.
Scan AI-generated codebases for architectural drift and security gaps
Scans your AI-generated codebase for architectural drift and security gaps so you know exactly what drifted from intent before it ships.
Test AI agents for adversarial attacks before production
Runs adversarial attacks against any AI agents in your product to surface prompt injection, jailbreaks, and edge case failures before real users trigger them.
Auto-generate living product specs from your codebase for PMs and stakeholders
Auto-generates living product specs from your codebase so your AI tools have accurate context for future changes and stakeholders can understand what shipped.
AI code review that organizes pull requests into logical chapters for clarity
Organises pull requests into logical chapters so you can review AI-generated changes without losing the thread across hundreds of modified files.
Protect LLM API calls with sub-16ms security layer
Adds a one-line security firewall around every LLM API call in your product to block prompt injections and malicious inputs with under 16ms overhead.
Expected outcome
A production-ready codebase with documented architecture, security gaps closed, adversarial agent tests passing, and living specs your AI tools can reference
Related playbooks
The Indie Founder Codebase Health Audit Playbook
Audit, document, and harden an existing codebase for production readiness
The Indie Founder EU AI Act Readiness Sprint Playbook
Produce verifiable EU AI Act compliance documentation and adversarial test evidence from your existing codebase
The Indie Founder AI Agent Red Team Playbook
Identify and patch adversarial vulnerabilities in AI agents before shipping to production users
The Indie Founder SaaS Architecture Clarity Playbook
Turn a system idea into a documented, cost-estimated, reviewable architecture
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This playbook is a curated starting point, not a definitive recommendation. Pricing and features change — always verify on each tool's official website. Tools marked "affiliate link" may earn this site a commission at no extra cost to you.