A Systematic AI Debugging Workflow That Gets Smarter Over Time

Most developers debug the same classes of bugs repeatedly. Connection pool exhaustion. Auth token expiry edge cases. Race conditions in async handlers. The specific service changes, but the pattern is familiar.

Each time, you start from scratch. Your AI is smart, but it doesn't know you solved almost exactly this problem six weeks ago. It doesn't know what you've already ruled out. It doesn't know the fix that worked last time.

Here's a four-step workflow that changes that.

Step 1: Load context before you start

Before writing a single hypothesis, check whether you've been here before.

# Check for similar past investigations
mx memories search --query "database connection timeout" --brief

# Load your current project context
mx memories get --name "project-context"

The --brief flag returns a fast, focused result — top matches with an 80-character preview. If something relevant comes back, read the full memory before touching anything. You might already have the answer.

Even when nothing matches, this step is worth the ten seconds. You start the investigation knowing what you know, not guessing at what you might have forgotten.

Step 2: Capture findings as you go

Don't wait until after you've solved it to start saving. Save while you're still inside the investigation.

mx memories create \
  --conversation-id "conv_debug_session" \
  --content "Investigating timeout in payment service. Error: Connection timeout after 30s. Database is responding — issue is connection pool exhaustion. Max pool size: 10, but we're seeing 15+ concurrent connections under load."

This does two things. First, it creates a record you can return to if you get interrupted or need to hand off. Second, it starts building the incident thread — a chronological record of how this investigation unfolded. That thread becomes searchable later.

You don't need to have answers yet. Save what you know, what you've ruled out, and your current hypothesis.

Step 3: Save the resolution and root cause

This is the most important step. Future you needs the full picture, not just the fix.

mx memories create \
  --conversation-id "conv_debug_session" \
  --content "RESOLVED: Payment service timeout. Root cause: connection pool was set to 10 but peak load needs 20+ connections. Fix: increased pool size to 25 in config/database.ts. Also added pool monitoring metrics. Lesson: always check pool exhaustion before assuming network issues." \
  --topics "completed"

Write it for someone who wasn't there. Include the root cause, not just the symptom. Include what you ruled out. Include the specific file or config that changed. The 60 seconds you spend here is the payoff moment — everything before this was building toward it.

The --topics "completed" tag marks the investigation closed and makes it easy to filter for resolved incidents when you're searching later.

Step 4: Search for patterns over time

After a month of saving investigations this way, your memory store becomes something more than a log. It becomes a pattern detector.

mx memories search --query "connection pool"

That search might return three separate incidents across three different services — all with the same root cause class. That's information you couldn't have had without the memory store. Now you know: connection pool sizing is an ongoing tuning concern for this system, and services need at least 2x expected concurrent connections as a safety margin.

You've gone from fixing individual bugs to understanding a systemic pattern. That's a different class of engineering knowledge.

The compounding value

After six months of consistent debugging notes, your AI can synthesize across incidents you've long since forgotten. It can read five past investigations and tell you: "This looks like the same class of issue you've seen before. The consistent root cause was X. The fix that worked was Y. You've already ruled out Z in a previous incident."

That's not just memory — it's accumulated engineering judgment. Every debugging session you save makes the next one faster. The workflow compounds in a way that ad-hoc investigation never can.

The habit is simple: search before you start, save as you go, record the root cause when you find it. The intelligence builds from there.

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