Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Learn how to perform database schema migrations for AI agent systems with zero downtime. Covers online migrations, backward compatibility, data backfill, and rollback strategies.
Design dead letter queue systems for AI agent workflows that capture failed tasks with full context, enable automatic reprocessing, and support manual review for permanently failed operations.
Learn how to detect, diagnose, and fix infinite loops and circular handoffs in AI agent systems using loop detection, max_turns limits, break conditions, and real-time monitoring.
Master techniques for debugging multi-agent systems including interaction diagrams, distributed message tracing, replay tools, and correlation analysis. Turn opaque agent failures into diagnosable problems.
Learn systematic techniques for diagnosing why an LLM produces incorrect or surprising outputs, including prompt debugging, temperature tuning, few-shot correction, and structured output analysis.
Master techniques for diagnosing and fixing context loss during multi-agent handoffs, including context inspection, handoff logging, serialization validation, and state verification strategies.
Learn systematic approaches for debugging multi-agent systems, including structured logging, trace visualization, identifying bottlenecks in agent chains, and replay testing to reproduce and fix failures.
Build a production observability stack for AI agents with structured logging, distributed trace correlation, timeline reconstruction, and systematic root cause identification techniques.
Learn systematic approaches to debugging RAG retrieval failures including query analysis, embedding inspection, relevance scoring evaluation, and chunk quality review for more accurate AI agent responses.
