Page 4 of 16
Most teams still rely on one shared staging cluster. It's noisy, slow, and hard to trust. This article shows how to create short-lived, per-PR environments on Kubernetes, wired into CI, with simple guardrails.
A practical blueprint for building idempotent systems that prevent duplicate payments, orders, and writes in distributed systems.
Most outages in mature systems are caused by migrations. You can avoid them with safe steps and the right guardrails. A practical guide to the expand-contract pattern for zero-downtime schema changes.
How to build agents that fail safely instead of failing loudly.
How to stop your agents from doing unsafe or surprising things when they call tools like email, payments, or internal APIs.
How to stop agents from looping forever, burning tokens, and slowing everything down.
How to see what your agent actually did, step by step, and debug it like normal software. A practical guide to logging, replaying, and debugging AI agent workflows.
How to capture real user behavior and use it to quietly improve LLM prompts, tools, and policies over time. A practical guide to building feedback loops that turn messy usage into structured improvement.
How to stop agents from running wild: clear limits on time, tokens, tools, and user data. Practical patterns to keep AI agents under control in production systems.
How to treat LLMs as strict, structured components instead of free-form text generators. Start with schemas and tools, then write prompts around them.