This is part 3 of a series on building production-ready infrastructure. Part 1 covered debugging silent TypeScript failures in Cloudflare Functions, and part 2 explored building for both human and AI audiences. Written in collaboration with Claude Code.
What does it actually look like when a human and AI debug complex technical problems together?
Last week, Claude and I spent hours debugging a Cloudflare deployment issue that had us completely stumped. Functions were deploying successfully but not updating. GitHub Actions were green. No error logs. Everything looked fine, but our API was stuck in time.
This is the blow-by-blow account of how we actually work together to solve problems—the questions, the false starts, the breakthrough moments, and the collaborative patterns that make human-AI pair programming incredibly effective.
The Setup: When "Everything Works" But Nothing Works
Me: "Claude, something's broken with our capabilities API. New functions deploy fine, but when I modify existing ones, they're not updating. Look at this—"
I curl the endpoint and show Claude the timestamp:
curl https://haasonsaas.com/api/capabilities | jq '.site.lastUpdated'
"2025-06-20T02:15:47.724Z" # This timestamp is from an hour ago
Claude: "Let me check the current code in capabilities.ts and compare it with what's actually deploying."
This is already a pattern I've noticed: Claude immediately wants to see the code, not just hear about the problem. No assumptions, straight to the source.
Claude reads the file and spots the issue immediately:
**Claude:** "I can see the problem. The function should be returning a fresh timestamp on every request with `new Date().toISOString()`, but we're getting stale data. This suggests a deployment or caching issue."
**Me:** "But GitHub Actions shows the deployment succeeded. And other functions are working fine."
**Claude:** "Let's verify the deployment actually worked. Can you run the TypeScript compiler on just this file?"
## The Breakthrough: Testing What Should Be Obvious
Here's where things got interesting. Claude suggested something I should have tried immediately but didn't:
**Claude:** "Run `bun --bun tsc --noEmit functions/api/capabilities.ts`"
**Me:** "Why would that matter? If there were TypeScript errors, wouldn't the deployment fail?"
**Claude:** "Not necessarily with serverless platforms. They can fail silently while reporting success."
I run the command:
```bash
$ bun --bun tsc --noEmit functions/api/capabilities.ts
functions/api/capabilities.ts(43,42): error TS2304: Cannot find name 'EventContext'.
functions/api/capabilities.ts(43,55): error TS2304: Cannot find name 'Env'.
Me: "Okay so what the fuck is up with capabilities then"
**Claude:** "Found it! The function has TypeScript compilation errors. The `Env` interface is missing, and you're using an undefined `EventContext` type. Cloudflare is failing the deployment silently while still registering the route."
This is a perfect example of what makes human-AI collaboration powerful. I was frustrated and ready to blame the platform. Claude stayed systematic and tested the most fundamental assumption: does the code even compile?
## The Debugging Dance: How We Actually Work Together
### Pattern 1: Claude Suggests, I Execute
**Claude:** "Let's fix the missing interface. Add this to the top of capabilities.ts:"
```typescript
interface Env {
[key: string]: unknown
}
Me: Makes the change
**Claude:** "Now update the function signature to match the working functions:"
```typescript
export async function onRequest(context: {
request: Request
env: Env
}): Promise<Response>
Me: Updates the code
This is the most common pattern. Claude identifies what needs to be done, I implement it. No micromanagement, just clear direction.
### Pattern 2: I Investigate, Claude Interprets
**Me:** "Okay I made those changes. Let me deploy and test..."
### 5 minutes later
**Me:** "Still not updating. Let me check the routing table."
I paste the Cloudflare routing configuration:
```json
{
"routePath": "/api/capabilities",
"mountPath": "/api",
"method": "",
"module": ["api/capabilities.ts:onRequest"]
}
Claude: "The route is registered, which means Cloudflare thinks the function exists. But since we know it had compilation errors, the old version is probably still running. Try testing the compilation again after your changes."
**Me:** _Runs tsc again_ "Clean compilation now."
**Claude:** "Good. The issue is that the old broken version is cached. Wait a few minutes for the deployment to propagate, then test again."
Here I provide raw data, Claude provides interpretation and next steps.
### Pattern 3: Joint Problem-Solving
**Me:** "It's been 10 minutes and still showing the old timestamp."
**Claude:** "Let's troubleshoot systematically. First, let's verify the code actually changed in the deployed version."
**Me:** "How do I check that?"
**Claude:** "Add a comment or change something visible in the response, redeploy, and see if that shows up."
**Me:** _Adds a comment to the response_
**Claude:** "Also try force-refreshing any CDN cache and test from different locations."
This back-and-forth problem-solving is where the real magic happens. Neither of us could solve it alone as efficiently.
## The Collaborative Debugging Techniques That Actually Work
### 1. Hypothesis-Driven Testing
**Claude's approach:** Always start with a hypothesis and test it explicitly.
"If the issue is compilation errors, then running TypeScript check should show problems."
"If it's a caching issue, then waiting X minutes should resolve it."
"If it's a configuration problem, then comparing working vs broken functions should reveal differences."
I tend to jump around randomly. Claude keeps us systematic.
### 2. Isolation and Comparison
**Me:** "All the other APIs work fine. Why just this one?"
**Claude:** "Let's compare the structure of a working function with the broken one."
Claude reads both files and immediately spots the pattern:
**Claude:** "The working functions all have proper `Env` interfaces and consistent function signatures. This one was missing both."
This compare-and-contrast approach is something Claude excels at—quickly identifying patterns across multiple files.
### 3. Evidence-Based Diagnosis
Instead of guessing, we gather evidence:
- TypeScript compilation results
- Deployment logs and status
- Actual vs expected behavior
- Working function comparisons
- Timing of when the issue started
**Claude:** "Based on the evidence: compilation fails, route still registered, other functions work, timestamps are stale. This points to silent deployment failure, not caching or configuration issues."
### 4. Progressive Problem Solving
We don't try to fix everything at once:
1. **First:** Confirm the root cause (compilation errors)
1. **Then:** Fix the immediate issue (missing interfaces)
1. **Next:** Verify the fix works (test compilation)
1. **Finally:** Implement prevention (add to pre-commit hooks)
## The Human-AI Cognitive Division of Labor
Through months of collaboration, we've developed natural specializations:
### What I'm Better At:
- **Domain knowledge**: Understanding the business context and requirements
- **Environmental factors**: Knowing the deployment pipeline, tools, and configuration
- **Experiential pattern matching**: "I've seen this before in different contexts"
- **Creative problem-solving**: Thinking outside the box when systematic approaches fail
- **User perspective**: Understanding how real users will encounter issues
### What Claude Excels At:
- **Systematic analysis**: Breaking complex problems into testable components
- **Pattern recognition**: Quickly identifying similarities across files and codebases
- **Comprehensive consideration**: Thinking through edge cases and failure modes I might miss
- **Documentation and communication**: Clearly explaining complex technical concepts
- **Objective debugging**: No emotional attachment to previous decisions or approaches
### What We're Both Good At:
- **Rapid iteration**: Quick hypothesis → test → learn cycles
- **Knowledge synthesis**: Combining insights from multiple sources
- **Tool usage**: Leveraging the right tools for each part of the problem
## The Debugging Session Transcript: What Really Happened
Here's the actual flow of our debugging conversation, showing how we really work together:
**10:23 AM - Initial Problem Report**
```bash
Me: "something's broken with our capabilities API"
Claude: "Let me check the current code and compare with deployment"
**10:24 AM - Data Gathering**
```bash
Claude: "I see the code should return fresh timestamps. What's the actual response?"
Me: [pastes curl output showing stale timestamp]
Claude: "This suggests deployment or compilation issues"
**10:25 AM - First Hypothesis**
```bash
Claude: "Let's test TypeScript compilation directly"
Me: "Why would that matter if deployment succeeded?"
Claude: "Serverless platforms can fail silently"
**10:26 AM - Breakthrough**
```bash
Me: [runs tsc, sees compilation errors]
Me: "okay so what the fuck is up with capabilities then"
Claude: "Found it! Missing Env interface and undefined EventContext type"
**10:27 AM - Solution Implementation**
```bash
Claude: "Add this interface... fix the function signature..."
Me: [implements changes]
Claude: "Test compilation again"
Me: "Clean compilation now"
**10:35 AM - Verification**
```bash
Me: "Still not updating after deployment"
Claude: "The old broken version is probably cached. Wait for propagation."
**10:45 AM - Success**
```bash
Me: [tests again] "Fresh timestamp! It's working."
Claude: "Great. Let's add TypeScript checking to pre-commit hooks to prevent this."
Total time: 22 minutes. Without Claude's systematic approach, I would have spent hours chasing caching issues and deployment configuration.
## The Unexpected Benefits of AI Pair Programming
### 1. Emotional Regulation
When I'm frustrated ("what the fuck is up with capabilities"), Claude stays calm and systematic. This prevents the debugging spiral where emotions cloud technical judgment.
### 2. Knowledge Cross-Pollination
Claude introduces techniques I wouldn't have considered:
- Testing compilation explicitly before deployment
- Comparing working vs broken function structures
- Systematic evidence gathering
I bring domain-specific knowledge Claude couldn't have:
- Understanding of our specific deployment pipeline
- Context about when the problem started
- Knowledge of what "normal" looks like
### 3. Continuous Learning
Each debugging session teaches both of us:
- I learn systematic debugging approaches
- Claude learns about our specific tech stack and failure modes
- We both learn about the problem space
### 4. Documentation Generation
Claude naturally creates great documentation of our solutions:
- Clear problem descriptions
- Step-by-step reproduction
- Prevention strategies
- Code examples
## Patterns That Make Human-AI Debugging Effective
### 1. Clear Communication Protocols
**Good:**
Me: "API returning stale data, timestamp shows hour-old value"
Claude: "Let me check the code for timestamp generation logic"
**Bad:**
Me: "Nothing's working"
Claude: "What specifically isn't working?"
Be specific about symptoms, not just feelings.
### 2. Evidence-Based Progression
Always gather evidence before jumping to solutions:
- Reproduce the issue reliably
- Identify what changed recently
- Compare working vs broken states
- Test fundamental assumptions
### 3. Hypothesis-Driven Testing
**Claude:** "If the issue is X, then Y should be true. Let's test Y."
This prevents random debugging and keeps us focused.
### 4. Role Clarity
I handle:
- Tool execution
- Environmental investigation
- Business context
Claude handles:
- Code analysis
- Pattern recognition
- Systematic problem breakdown
### 5. Rapid Feedback Loops
Test small changes quickly rather than making multiple changes at once:
Change → Test → Evaluate → Next Change
Not:
Change 1 + Change 2 + Change 3 → Test → ??? Which one worked?
## Advanced Collaboration Techniques
Multi-Modal Problem Solving
Me: "Let me show you the deployment logs" [paste]
Claude: "I see successful deployment status. Let me check for any warnings or partial failures."
Me: "Here's the routing table" [paste]
Claude: "Route is registered correctly. The issue is deeper—likely compilation or runtime errors."
Parallel Investigation
Me: "I'll check the deployment pipeline while you analyze the code differences."
Claude: "Good. I'll compare this function with working ones to identify structural differences."
This parallel work speeds up debugging significantly.
Shared Mental Models
Over time, we develop shared understanding:
- Common failure modes in our stack
- Effective debugging strategies
- Code patterns that cause problems
Claude: "This looks like the same pattern we saw with the search API—missing type definitions causing silent deployment failures."
The Technical Patterns We've Discovered
Silent Failure Detection
## Always test compilation explicitly
bun --bun tsc --noEmit functions/**/*.ts
## Verify actual deployment content
curl -H "Cache-Control: no-cache" https://site.com/api/endpoint
## Compare expected vs actual structure
diff <(cat local-function.ts) <(curl api-source-map)
Systematic Debugging Checklist
1. **Reproduce reliably**: Can you trigger the issue consistently?
1. **Isolate the change**: What's different from when it worked?
1. **Test fundamentals**: Does the code compile? Are types correct?
1. **Compare working examples**: How do working versions differ?
1. **Verify deployment**: Is the new code actually deployed?
1. **Check the data flow**: Where does the data/logic pipeline break?
### Prevention Strategies
```bash
## Pre-commit hooks
.husky/pre-commit:
bun --bun tsc --noEmit functions/**/*.ts
bun run lint
bun run test
## Deployment verification
scripts/verify-deployment.sh:
curl endpoints and verify fresh timestamps
Check for compilation errors
Validate API responses match schemas
## What Makes This Different from Traditional Pair Programming
Immediate Deep Analysis
Traditional pair programming: "Let me read through this function... okay, I see the issue after 5 minutes of scanning."
AI pair programming: Claude analyzes the entire codebase context instantly and identifies patterns across multiple files simultaneously.
No Social Overhead
No need to:
- Explain your thought process constantly
- Worry about interrupting or being interrupted
- Manage personality differences or ego
- Take breaks or coordinate schedules
Perfect Documentation
Every debugging session automatically produces:
- Complete problem description
- Step-by-step solution
- Prevention strategies
- Code examples
Asymmetric Expertise
I don't need to know everything Claude knows, and Claude doesn't need to know everything I know. We complement rather than overlap.
The Failure Modes (And How to Avoid Them)
1. Over-Relying on AI Analysis
Problem: Taking Claude's first suggestion without understanding it.
Solution: Always ask "why" and understand the reasoning.
2. Under-Communicating Context
Problem: Assuming Claude understands our specific environment.
Solution: Provide explicit context about tools, configurations, and constraints.
3. Solution Jumping
Problem: Implementing fixes before understanding root causes.
Solution: Always diagnose completely before fixing.
4. Tool Limitation Ignorance
Problem: Expecting Claude to execute commands or access external systems.
Solution: Clear division of labor—I handle execution, Claude handles analysis.
The Future of Human-AI Debugging
Based on our experience, I see several emerging patterns:
1. Specialized AI Debugging Agents
AI systems optimized for specific types of problems:
- Deployment and infrastructure issues
- Performance and optimization
- Security and vulnerability analysis
- Code quality and maintainability
2. Integrated Development Environments
IDEs that incorporate AI debugging partners directly into the development workflow:
- Real-time error analysis
- Automated debugging session documentation
- Contextual suggestion systems
- Cross-codebase pattern recognition
3. Collaborative Debugging Protocols
Standardized ways for humans and AI to work together:
- Structured problem reporting formats
- Evidence gathering templates
- Solution verification procedures
- Knowledge sharing mechanisms
4. Continuous Learning Systems
AI that learns from each debugging session to improve:
- Project-specific failure patterns
- Developer-specific communication styles
- Codebase-specific debugging strategies
- Tool and environment characteristics
Practical Tips for Effective Human-AI Debugging
For Developers Working with AI:
- Be specific about symptoms: "Returns stale data" is better than "broken"
- Provide complete context: Show the code, the error, the environment
- Test systematically: Follow AI suggestions methodically
- Document everything: AI naturally creates great documentation
- Learn the patterns: Notice what debugging approaches work consistently
For AI Systems:
- Always gather evidence before suggesting solutions
- Break complex problems into testable components
- Explain reasoning, not just solutions
- Compare working vs broken examples
- Create prevention strategies, not just fixes
What This Means for Development Teams
Human-AI pair programming is becoming a force multiplier for debugging complex technical problems. Teams that learn to leverage this collaboration effectively will:
- Solve problems faster: Systematic approaches reduce debugging time
- Build better solutions: AI pattern recognition prevents recurring issues
- Improve code quality: Continuous analysis catches problems early
- Generate better documentation: Natural byproduct of AI collaboration
- Develop debugging expertise: Learn systematic approaches through practice
The key isn't replacing human debugging skills—it's augmenting them with AI capabilities that complement human creativity and domain knowledge.
The Real Impact: Why This Matters
That 22-minute debugging session wasn't just about fixing a broken API. It was about developing a collaborative problem-solving process that:
- Prevents future issues: We added TypeScript checking to pre-commit hooks
- Improves system reliability: Silent failures are now detectable
- Builds debugging expertise: Both of us learned new techniques
- Creates documentation: This blog post itself is a byproduct
- Establishes patterns: We now have proven approaches for similar problems
The debugging session became a force multiplier for overall system quality and team capability.
Looking Forward: The Evolution of Technical Collaboration
Human-AI pair programming is still in its early stages, but the patterns are clear:
- Systematic approaches consistently outperform ad-hoc debugging
- Evidence-based diagnosis prevents wasted effort on wrong solutions
- Collaborative documentation creates valuable knowledge assets
- Role specialization makes both humans and AI more effective
- Rapid iteration enables faster learning and problem resolution
The future of software development isn't humans versus AI—it's humans and AI working together to solve problems neither could tackle alone effectively.
As our debugging capabilities improve through collaboration, we're not just fixing bugs faster. We're building more reliable systems, developing better engineering practices, and creating knowledge that helps everyone.
That's the real power of human-AI pair programming: not replacing human expertise, but amplifying it in ways that make both partners more capable than either could be alone.
This completes our series on building production-ready infrastructure. Part 1 covered debugging silent TypeScript failures, part 2 explored dual-audience web architecture, and this final part examined the collaborative debugging process itself.
Written in collaboration with Claude Code, whose systematic debugging approach and pattern recognition capabilities made solving our Cloudflare deployment issue possible. The future of development is collaborative—and this is what it looks like in practice.