AI Agent Observability
See, evaluate, and improve agents in production.
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LLM as a Judge: The Eval Method Everyone Uses and Nobody Calibrates
LLM as a judge lets one model score another. Learn how to build LLM evals, the documented judge biases, and the judge noise most teams forget to measure.
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RAG Evaluation: Score the Pipeline, Not Just the Answer
RAG evaluation splits into retriever and generator metrics. See which metrics matter, how to build eval sets from traces, and why RAG breaks quietly.
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Prompt Enhancer vs Prompt Optimizer: Only One Can Prove It Helped
Prompt enhancers rewrite your prompt. Optimizers measure whether the rewrite works. Compare free enhancers, OpenAI and Anthropic tools, DSPy, and Mutagent.
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AI Governance Tools for Autonomous Agents
AI governance tools monitor, audit, and enforce policy on AI. Most govern static models. Learn what changes when you must govern agents that take actions.
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Prompt Optimization: Why Tweaking Prompts Doesn't Scale
Prompt optimization is improving a prompt against a measure, not by feel. Learn the manual techniques, why they stall, and how eval-driven optimization works.
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LLM Monitoring in Production (the Layer Tracing and Evals Don't Cover)
LLM monitoring watches aggregated signals over time to catch drift, cost creep, and regressions before users do. Set thresholds that fire and close the loop.
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Prompt Versioning for AI Agents (a Regression Problem, Not a Storage One)
Prompt versioning is how you attribute a production regression to a version and roll back safely. Link every version to its eval score and the traces it ran.
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What Is LLM Tracing (and Why Your Tracing Tool Might Be Lying to You)
LLM tracing records every step an AI agent takes so you can debug failures. Learn what to trace, the silent-drop problem, and how to keep signal reliable.