Building AI Agents That Actually Work in Production
Lessons learned from deploying autonomous AI agents at scale — the good, the bad, and the unexpected.

A curated journal on agent architecture, retrieval quality, backend systems, and the leadership decisions that turn prototypes into durable products.
6
Articles
11
Topics
8
Case studies
Explore by Interest

Lessons learned from deploying autonomous AI agents at scale — the good, the bad, and the unexpected.
Editorial Picks
Moving past naive RAG implementations to build retrieval systems that actually answer complex questions.
What I learned about caching, databases, and distributed systems while scaling a B2B platform.
Reading Paths
Start with production constraints, then move into retrieval and agent reliability.
Operational lessons for scaling AI and backend systems under real usage.
Architecture trade-offs for monoliths, services, caching, and durable boundaries.
Practical field notes for engineering judgment, trust, and team clarity.
Collections
Production behavior, retrieval quality, and agent design beyond the demo.
Lessons learned from deploying autonomous AI agents at scale — the good, the bad, and the unexpected.

Moving past naive RAG implementations to build retrieval systems that actually answer complex questions.

Scaling, service boundaries, infrastructure choices, and operational trade-offs.
What I learned about caching, databases, and distributed systems while scaling a B2B platform.

Spoiler: probably not as early as you think. A practical guide to making the right architecture decisions.

How technical leaders create clarity, momentum, and healthier execution.
Lessons from early technical bets, shortcuts, and systems that need to survive speed.