Topics
Technical deep dives into the engineering practices that make AI systems reliable in production. Each topic covers definitions, architecture, real-world examples, and common failure modes — drawn from hands-on experience building and operating AI systems.
AI Reliability
The discipline of building AI systems that work consistently in production — covering constraint enforcement, drift detection, and failure recovery.
Systematic approaches to diagnosing and resolving failures in AI systems — from hallucinations to tool call failures.
A taxonomy of how AI agents fail in production — from hallucinations and tool misuse to cascading failures in multi-agent systems.
AI Infrastructure
Monitoring, tracing, and understanding AI agent behavior in production — from token usage to decision quality.
Distributed tracing for multi-agent AI systems — following a request from user input through orchestration, tool calls, and response synthesis.
An open standard for connecting AI models to external tools and data sources through a unified, structured interface.
Coordinating multi-step AI workflows — from single-agent task execution to multi-agent fan-out with parallel tool calls.
Engineering practices for deploying and operating AI systems in production — beyond prototypes and demos.