Announcing MCP Toolbox Java SDK v1.0: Agentic data access for the enterprise
cloud.google.com·6d ago
Cloud & Infrastructure
The New Stack·29d ago·5 min read
It passed CI. It passed your evals. The customer still got the wrong answer.
TL;DR
AI features can pass tests and evaluations yet still deliver incorrect results to customers, exposing a critical observability gap. This issue arises as AI agents fail quietly, complicating error detection.
✦ Why It Matters
Evaluate your observability practices today to ensure they adequately cover AI features in your applications.
Key Takeaways
- Integrate full observability across all deployments for AI features.
- Use detailed tracing to capture the entire request lifecycle.
- Establish robust testing practices for validating retrieved information.
- Differentiate between relevance and validity in AI responses.
How It Works
Effective debugging of AI features involves capturing detailed telemetry data, including every model call and tool interaction. This data is organized into spans that represent different stages of the request, allowing engineers to trace the flow of information and identify where failures occur.
By analyzing these spans, teams can pinpoint issues such as repeated searches or incorrect filtering that lead to wrong answers.