The Experiment Nobody Asked Me To Do
Two months ago, I decided to let AI agents handle my entire development workflow — from writing code to deploying to production. The results were… surprising.
Not because everything went perfectly (it didn't). But because the failures taught me more about software engineering than the successes.
Here's the full breakdown.
What I Automated (And How)
I split my workflow into 5 stages and assigned an AI agent to each:
1. Planning Agent — From Idea to Spec
I feed it a rough idea like "build a URL shortener." It generates:
- Feature list with priorities
- Database schema
- API endpoints
- Estimated timeline
Result: The specs were 80% solid. The missing 20%? Edge cases that only show up after real users touch the system.
2. Coding Agent — Writing the Actual Code
This agent takes the spec and generates code file by file. I used it with:
Tech stack: Next.js + TypeScript + Prisma + PostgreSQL
What went well:
- Boilerplate code was generated in minutes (what used to take hours)
- TypeScript types were consistently correct
- Database queries were optimized by default
What broke:
- Complex business logic needed manual intervention
- The agent loved
anytypes when it got confused (classic) - Generated tests passed but tested nothing meaningful
3. Review Agent — Code Review Before Human Eyes
This agent acts as a senior developer reviewing PRs:
- ✅ Caught missing error handling
- ✅ Found potential SQL injection risks
- ❌ Flagged intentional design decisions as "issues"
- ❌ Couldn't understand context-dependent logic
4. Testing Agent — Writing Tests
Generated unit tests covered the happy path beautifully. Integration tests? Not so much.
The biggest problem: tests that always pass aren't testing anything. I had to manually verify each test was actually asserting something useful.
5. Deploy Agent — CI/CD Pipeline Management
This was the most reliable agent. Setting up:
- GitHub Actions workflows
- Docker configurations
- Environment variables management
It worked 95% of the time. The 5% failure? Always a permission issue.
The Numbers
| Metric | Before AI | After AI | Change |
|---|---|---|---|
| Time to MVP | 2 weeks | 3 days | -78% |
| Lines of code | 1,200 | 1,800 | +50% |
| Bugs in production | 3/week | 5/week | +67% |
| Time on bug fixes | 10 hrs/week | 8 hrs/week | -20% |
| Developer happiness | 😐 | 😊 then 😤 then 😐 | Mixed |
3 Lessons I Learned
Lesson 1: AI Excels at the Boring Stuff
Setting up configs, writing boilerplate, generating documentation — these are tasks developers hate. AI does them fast and well.
Lesson 2: AI Struggles with "Why"
The agent can tell you how to implement a feature, but not why you should or shouldn't. Architecture decisions still need human judgment.
Lesson 3: The Debugging Paradox
AI-generated code is harder to debug because you didn't write it. You spend time understanding what the AI was thinking before you can fix it.
My Current Workflow (The Hybrid Approach)
After this experiment, I landed on a workflow that actually works:
🤖 AI handles: Boilerplate, configs, docs, tests, deployment
🧑 Human handles: Architecture, business logic, code review, debugging
📊 Ratio: 60% AI / 40% Human
This isn't about replacing developers. It's about removing the friction so developers can focus on what actually matters: solving problems and making decisions.
The Future I See
I think in 2-3 years, the split will be closer to 80% AI / 20% Human — but that 20% will be more important than ever. The developers who thrive will be the ones who can:
- Think in systems, not just code
- Validate AI output quickly and accurately
- Make architectural decisions that AI can't
The code itself? That's becoming the easy part.
What About You?
Have you tried integrating AI agents into your workflow? What worked? What failed spectacularly?
I'd love to hear your experiences in the comments. Drop your worst AI-generated bug story below — I guarantee mine isn't the funniest one. 😄
If you found this useful, follow me for more experiments at the intersection of AI and software development.
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