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.
If you're interested in AI systems, cloud architecture, or the projects I'm building, you can explore more of my work here:
🌐 Portfolio: https://rahulshekhawat.dev
I'm always happy to connect with developers, founders, and anyone building in the AI space.
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