Observability
8 practitioners working with Observability:
10 AI Agent Failure Modes: Why Agents Break in Production
The documented ways AI agents fail: hallucination cascades, context overflow, tool calling errors, and 7 more. Diagnosis patterns and fixes for each.
Agent Observability
How to implement distributed tracing, logging, and monitoring for AI agents using OpenTelemetry and purpose-built tools like Langfuse and Braintrust.
Debug Your RAG Pipeline Before Users Notice
Monitor retrieval-augmented generation systems with OpenTelemetry tracing. Find whether bad answers come from retrieval, context, or generation.
LLM Logging: Capture Every AI Conversation
Track prompts, responses, and token usage. Build a searchable archive of LLM interactions for debugging, learning, and prompt optimization.
LLM-as-Judge Evaluation
Use LLMs to evaluate LLM outputs. Build reliable automated judges through critique shadowing and iterative calibration with domain experts.
Memory Attribution and Provenance
Track where AI memories came from, when they were created, and how much to trust them
observability, orchestration, and the 73% shift
blind spots getting plugged: agent dashboards, karpathy's workflow flip, and anthropic's market capture
the blind spots are getting plugged
observability for agents, karpathy's workflow flip, and anthropic's 73% market capture