Observability tools weren’t built for AI debugging

Estimated reading time: 10 minutes

Key takeaways:

• AI debugging has a data problem, not a model problem. Smarter models won’t fix bad observability. Garbage in, garbage out.
• You’re drowning in telemetry but starving for signal.
• Collect the right data upfront, not the wrong data at scale. Session-based, correlated collection gives AI agents the complete context they need.

Most engineering teams are using AI to write code faster than ever. However, now they are also shipping bugs with equal speed.

Here’s what that workflow actually looks like end to end:

[Auto-instrument everything via OTel]
→ [Collector samples/filters some]
  → [Store remaining data]
  → [Developer notices bug]
    → [Manually copy-paste error OR query via MCP server]
    → [AI gets incomplete/noisy context]
      → [AI suggests fix based on partial data]
      → [Human reviews]
        → [Code “looks plausible”]
        → [Deploy]
          → [Discover edge case not addressed by AI]
                        → [More bugs in production]

There are several places this workflow breaks...

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