I’m going to say something that’ll make some people uncomfortable: if you don’t have test coverage in your solution, you’re going to fail at using agentic AI. Not “struggle with it.” Not “get mixed results.” You will fail.
After months of building agentic DevOps workflows and wrestling with AI agents that think they’re helpful but actually break things, I’ve learned this the hard way. AI writes code fast. Really fast. But there’s a dark pattern emerging that nobody talks about enough: AI writes fake tests that pass but test nothing.
This isn’t theoretical. Research from multiple teams shows AI-generated tests achieve only 20% mutation scores on real-world code. That means 80% of potential bugs slip right through. The tests compile, they run, they pass — and they validate absolutely nothing.
So I built guardrails. Not suggestions. Not best practices. Hard stops that prevent broken code from shipping, even when AI thinks everything’s fine. Here’s what actually works.
The Testing Reality for Agentic Teams
Most teams think unit tests are enough. They’re wrong. Agentic teams need the full suite: unit, integration, end-to-end, and UI automated tests. Why? Because AI agents need different types of guardrails at different layers.
Here’s the insight that changed everything for me: the ONLY difference between unit, integration, and e2e tests is WHERE you mock. That’s it. Same testing frameworks, same assertions, different mocking boundaries.
- Unit tests: Mock everything external to the function
- Integration tests: Mock only external services (APIs, databases)
- E2E tests: Mock nothing — test the full stack
Once I understood this, building comprehensive test suites became way simpler. Not easier — simpler. There’s a difference.
Pre-Tool-Use Hooks: Your First Line of Defense
The first guardrail I built blocks direct git push commands. Sounds extreme? It is. But it works.
Using GitHub Copilot’s pre-tool-use hook system, I force all pushes through a custom npm run push script. No exceptions. If an agent tries to push directly, it fails instantly.
Here’s what npm run push actually does:
- Checks uncommitted changes — stops if you forgot to commit something
- Runs type checking — catches TypeScript errors before they leave your machine
- Validates test coverage — enforces minimum thresholds
- Runs integration tests — proves components work together
- Builds the project — catches build-time errors
- Pushes to remote — only if everything above passed
- Polls for PR reviews, check runs, and security alerts — monitors what happens next
The last step is crucial. Most CI/CD stops at “push succeeded.” I don’t. My script waits for GitHub Actions to finish, checks PR status, and surfaces security alerts immediately. Why wait 10 minutes to find out you broke something?
Test Coverage Ratchets: The Line Only Goes Up
Here’s a pattern that’s saved me countless times: coverage ratcheting. Test coverage thresholds in my Vite config act as a ratchet — they only go up, never down.
// vite.config.ts
export default defineConfig({
test: {
coverage: {
branches: 85, // Can increase, can't decrease
functions: 85,
lines: 85,
statements: 85,
thresholds: {
autoUpdate: true, // Bump thresholds when coverage improves
},
},
},
});Every time I improve coverage, the threshold automatically increases. If AI generates code that drops coverage below the current threshold, the build fails. The bar only moves one direction: up.
This forces a fundamental shift in how you think about technical debt. You’re not fighting to “get to 80% coverage someday.” You’re making tiny, incremental improvements that compound. Ship one well-tested feature and the baseline rises permanently.
The Core Folder Pattern: Centralize to Control
One of the subtler issues with agentic AI is that it doesn’t naturally think about dependency management. It’ll import fs in one file, path in another, and FFMPEG in three different places. Then when you need to swap implementations or add instrumentation, you’re hunting through the entire codebase.
I use a core folder pattern: all external dependencies get imported through centralized modules in a core/ directory. My pre-tool-use hook blocks any import that reaches outside core for system-level dependencies.
// ❌ Blocked by hook
import fs from 'fs';
import path from 'path';
// ✅ Allowed
import { fs, path } from '@/core/fs';
import { ffmpeg } from '@/core/ffmpeg';Why does this matter for testing? Because now I can mock at the module boundary. Every integration test can swap out @/core/fs with an in-memory file system. Every e2e test can stub @/core/ffmpeg without installing actual binaries.
AI agents don’t understand this pattern naturally, but hooks enforce it. They don’t need to understand — they just need to comply.
Memory Systems and Context Engineering
Here’s something most people don’t realize: context matters more at the beginning than the end of a conversation. I call this context rot. The longer a Copilot session runs, the more likely AI is to drift from project conventions.
I built a memory system that saves lessons from hook violations. When an agent violates a hook — tries to push without tests, imports outside core, whatever — Copilot’s workspace memory captures why it failed. That lesson gets injected into future sessions.
// Pseudo-code for memory integration
when hookViolation occurs:
extract violation reason
save to workspace memory with category "hook_enforcement"
include in context for next agent sessionThis creates a feedback loop. AI makes mistake → hook blocks it → memory captures why → AI learns the rule → compliance improves over time.
The Stanford study on AI ROI showed that developer productivity with AI varies wildly based on codebase quality. My data confirms this. Teams with strong guardrails see 3x productivity gains. Teams without them often see negative ROI because they spend more time fixing AI-generated bugs than writing code themselves.
Three Prompts to Get Started
You don’t need to build everything I did. Start with these three prompts for GitHub Copilot:
1. Audit your codebase for testability:
Analyze this codebase and identify all functions that lack test coverage.
Prioritize by risk: focus on business logic, data transformations, and
public APIs. Generate a markdown report with test coverage gaps.2. Create a test suite with coverage thresholds:
Create a comprehensive test suite for [module name] with:
- Unit tests for all public functions
- Integration tests for database/API interactions
- E2E tests for critical user workflows
- Vite config with 85% coverage thresholds
Ensure tests verify behavior, not just exercise code.3. Build enforcement hooks:
Create a pre-tool-use hook that blocks git push and forces execution
through an npm script. The script should: check uncommitted changes,
run type checking, validate coverage thresholds, run integration tests,
build the project, then push. Include polling for PR status.These won’t give you everything, but they’ll establish the foundation. The rest you can build iteratively.
With Great Power Comes Great Responsibility
Spider-Man had it right. Agentic AI gives us incredible power to ship code faster than ever. But without guardrails, that power becomes reckless.
I’ve seen teams spin up AI workflows, get excited about velocity, then three weeks later they’re drowning in production bugs. The AI shipped fast, but it shipped broken. Tests looked good but tested nothing. Coverage metrics climbed while defect detection plummeted.
The answer isn’t “don’t use AI.” The answer is build the infrastructure that makes AI safe. Tests that actually test. Hooks that enforce quality. Coverage ratchets that prevent regression. Memory systems that capture lessons.
This isn’t optional anymore. If you’re betting on AI to accelerate development — and you should be — then you’re also betting on having the discipline to constrain it properly.
The line between “AI that 10x’s your team” and “AI that destroys your codebase” is surprisingly thin. That line is called test coverage. Don’t cross it unprepared.