Blog
Practical thinking on AI governance, scalable systems, blockchain architecture, and the engineering discipline that makes complex systems reliable.
How to implement distributed tracing for multi-agent AI systems — propagating trace context across async boundaries, capturing LLM-specific signals, and building the observability that makes agent debugging possible.
A systematic approach to diagnosing tool call failures in AI agent systems — from incorrect parameter construction to silent schema mismatches and the debugging patterns that catch them.
What to monitor in a production RAG system — retrieval quality metrics, embedding drift detection, index freshness, and the alerts that catch degradation before users notice.
How to architect a scalable, event-driven AI agent system on AWS Lambda with SQS — the four-tier hierarchy, countdown latches, and the patterns that make it production-ready.
AI governance for engineering teams requires enforceable constraints in the development workflow, not just policy documents that sit in a wiki.
Practical lessons on building retrieval-augmented generation pipelines that work reliably at scale, beyond the demo stage.
Why we stopped calling OpenAI for embeddings and built a Rust-based vector generation service on AWS Lambda. Architecture, deployment, and the math that makes it obvious.
Architectural patterns for scaling backend systems that process large volumes of data reliably, from partitioning strategies to backpressure mechanisms.