Track
AI Observability
Your LLM doesn't throw a 500 when it's wrong.
The track
What it covers
Monitoring LLM applications and agents in production: token and cost tracking, latency and quality evals, tracing multi-step agent calls end to end, and catching drift and hallucination before your users do.
Audience
Who it's for
Teams shipping LLM-backed features or agent workflows into production, discovering that traditional observability wasn't built for a system that can be “up” and still wrong.
Example sessions
A taste of the program
Illustrative: call for papers pending; final lineup announced with the CFP.
Your LLM Doesn't Throw a 500
Example session · CFP pending
Tracing a Multi-Agent Pipeline in Production
Example session · CFP pending
Cost Observability for AI Workloads
Example session · CFP pending
Matching workshop
AI Observability Workshop
Half-day, workshop day. Monitor a real LLM app or agent: token and cost tracking, latency and quality evals, tracing multi-step agent calls, and catching drift and hallucination in production.
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