Tracing agentic systems uses hierarchical IDs to form a System DAG, exposing performance and cost issues. Observer agents automate diagnosis and system self-correction.
What did the agent do? That’s a solved problem. Why did it do it? That’s not. Getting this right determines whether anyone trusts it with work that matters.
Log every AI agent action to one custom object, and force the LLM to include a reasoning field in every tool call so you always know why it did what it did.
A practical framework for tracking attribution, setting budgets, and circuit-breaking spending on LLM in your CI/CD pipeline by using an OpenTelemetry implementation.
Production AI agents can trigger cascading failures when observability tracks what broke, but not whether the system can safely absorb remediation actions.
RAG pipelines are getting more and more popular with vector search at the core of them. However, vector search might not be just enough for high-quality retrieval.
Learn about how middleware in AI agent frameworks enables request rewriting, tool filtering, and context control — capabilities callbacks alone can’t support.
Most agent frameworks observe model calls and allow rewriting them only after they reach the model, making an understanding of callbacks and middleware essential.
Part 3 of a step-by-step tutorial that decorates the implementation with Spring AI advisors to demonstrate how certain production concerns may be addressed.