Agent reliability analysis
In plain wordsYour AI agent gives different answers, retries too much or fails without telling anyone. We find out why and what to fix first.
Croutons evaluates unreliable agent behavior across context, routing, tools and failure modes.
The problem
Agents produce inconsistent answers, retry excessively or fail silently when upstream context or tools break.
Signs you have it
- Escalating support tickets on agent answers
- Rising retry rates without root-cause ownership
- Hallucinated citations or tool results
- Unstable tool-call chains
Who it is for
AI operations teams supporting customer-facing and internal agents.
What we look at
- Prompt and context structure
- Retrieval quality dependencies
- Tool failure modes
- Routing and fallback gaps
- Session-level failure patterns
What to send us
- Failure and success traces
- Tool schemas and error rates
- Human review samples
- Current alert or support categories
How the diagnosis works
Map execution paths and failure clusters. Separate context, routing, tool and governance gaps. Prioritize fixes by user and operational impact.
What you get
- Reliability diagnosis brief
- Failure mode inventory
- Prioritized fix list
- Monitoring recommendations
Expected outcome
Documented failure boundaries and actionable fixes for the highest-impact breaks.
Limits, stated up front
- Does not replace observability platforms
- Reliability gains depend on implementing recommended controls
- Some failures originate outside the model layer
Related use cases
Agent fan-out control
One question to an AI agent can trigger dozens of hidden calls. We map that chain and show where to cut it.
Evaluation-system design
If your tests do not match how your AI fails in real life, they will not catch the next failure.
Runtime governance review
When AI decisions touch customers or money, someone should be able to approve, stop and audit them.
Agent reliability analysis: common questions
Why does our agent give different answers to the same question?
Common causes are changing retrieval results, context that grows across turns, different models picked by fallback rules, or tools that fail and get silently skipped. The diagnosis tells you which one applies to you.
Is this the same as an observability dashboard?
No. Dashboards show what happened. This analysis groups failures by cause, separates model problems from tool and data problems, and ranks fixes by impact on users.
How many failure examples do you need?
A few dozen real failures with their traces is a good start, plus some successful runs to compare against.
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