AI agents are getting powerful.
We can now build systems that:
- Call APIs
- Use tools
- Chain multiple LLM steps
- Make decisions autonomously
But there’s one problem I keep running into:
When something goes wrong, it’s incredibly hard to understand why.
The Illusion of Observability
Today, we have tools that provide:
- Logs
- Traces
- Token usage
- Cost tracking
These are useful.
But in practice, they answer only one question:
👉 “What happened?”
Not:
👉 “Why did it happen?”
A Simple Example
Imagine an AI agent:
- Takes user input
- Calls an API
- Processes the response
- Generates final output
Now suppose the final answer is wrong.
Where did it fail?
- Was the prompt incorrect?
- Did the tool return unexpected data?
- Did the model misinterpret context?
- Did a previous step introduce noise?
Most of the time, you’re left manually digging through logs.
The Real Problem: Debugging, Not Logging
We don’t just need better logs.
We need:
- Step-by-step replay of workflows
- Visibility into intermediate decisions
- Clear identification of failure points
- Understanding of how context evolves
In short:
We need debugging tools for AI systems, not just observability tools.
What’s Missing Today
From my experience, current workflows rely heavily on:
- Manual inspection
- Trial and error
- Adding more logging
- Using evals to detect issues
But even then:
👉 You still don’t get a clear answer to why something failed.
A Different Way to Think About It
Instead of asking:
“How do we log more?”
We should ask:
“How do we make AI systems debuggable?”
That means:
- Replaying executions like a timeline
- Highlighting where things diverged
- Understanding cause → effect relationships
- Reducing guesswork
Where I’m Heading
I’ve been exploring this space and working on something focused on:
- Debugging multi-step AI workflows
- Understanding root causes of failures
- Improving trust in AI systems
Still early, but the goal is simple:
Help developers understand why their AI behaves the way it does.
Open Questions
If you’re working with AI:
- How do you debug failures today?
- Do you feel current tools are enough?
- What’s the most frustrating part of working with AI systems?
Would love to hear your thoughts.
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