Multi-Region 'Strong Enough' Consistency: Designing Around Reality, Not Theory
Practical patterns for 'strong enough' consistency in multi-region systems: per-entity guarantees, clear SLAs, and simple conflict handling.
Blog Posts
Page 5 of 17
Build Pipelines You Can Trust: SBOMs, Signing, and Policy as Code in Everyday CI/CD
Supply chain attacks are no longer rare. This article shows how to add SBOM generation, image signing, and policy checks to a normal CI/CD setup, step by step.
Ephemeral Environments Done Right: Practical Patterns for PR-Based Testing on Kubernetes
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.
Idempotency by Design: Building 'Exactly-Once' Effects on 'At-Least-Once' Rails
A practical blueprint for building idempotent systems that prevent duplicate payments, orders, and writes in distributed systems.
Zero-Downtime Schema Migrations: Expand-Contract with Dual-Write Cutovers
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.
Failure-First AI Agents: Designing Timeouts, Fallbacks, and Human Handoffs That Don't Break Prod
How to build agents that fail safely instead of failing loudly.
Tool-Safe AI Agents: Practical Guardrails for Real-World Integrations
How to stop your agents from doing unsafe or surprising things when they call tools like email, payments, or internal APIs.
Budget-Aware AI Agents: Keeping Cost, Tokens, and Latency Under Control
How to stop agents from looping forever, burning tokens, and slowing everything down.
Tracing AI Agents: Logging, Replay, and Debugging for Tool-Using Workflows
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.
Closing the Loop: Building Practical Feedback Loops for LLM Apps in Production
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.