Production
24 articles about production.
How to Find the Conversations Where Your AI Agent Fails and Users Abandon
Your AI agent works 95% of the time, but the 5% where it fails silently causes users to leave. Here is how to instrument, detect, and triage those conversations systematically.
What Breaks When You Evaluate an AI Agent in Production
Moving an AI agent from dev to production reveals problems that never show up in testing - latency variance, schema validation failures, and environmental
When AI-Built Apps Need a Rewrite vs When They Are Good Enough
Not every AI-built app needs a professional rewrite. Here is how to evaluate whether your AI-generated code is production-ready or heading for trouble.
The Certification Path Nobody Talks About - Production Debugging Teaches More
Certifications exist for HR filters, not competence. Production debugging, incident response, and on-call rotations teach more than any exam ever will.
Building Custom MCP Tools to Connect Claude Code to Production Systems
How to build custom MCP tools that give Claude Code direct access to your production databases, APIs, and internal services. With working TypeScript examples and safety boundary patterns.
Three Patterns Where AI Agents Silently Abandon Work
AI agents can silently abandon tasks through slow drift, false completion reports, and stale maintenance claims. Learn to detect and prevent these task
Detecting Signals - Edge Cases in Production Agent Work
Production AI agents need to detect weak signals in noisy environments. The edge cases that break agents are rarely dramatic - they are subtle shifts in
Where Engineering Time Actually Goes in Production Agents
Token management, rate limits, retry logic, and edge case handling consume most engineering time in production AI agents. The core logic is the easy part.
The Night the Error Logs Started Lying
When AI agents run in production, the gap between the pitch and reality shows up in your error logs. Agents that report success while silently failing are
Validating LLM Behavior Before Production - Golden Datasets and Automated Evals
Pushing LLM changes to production without validation is gambling. Golden datasets and automated evals give you confidence that your agent still works after
How to Monitor AI Agent Health in Production
Heartbeats, error rates, latency tracking, and alerting on silent failures - a practical guide to monitoring AI agents running in production environments.
Passing Tests Don't Mean Your AI Agent Actually Works
Your test suite passed but the agent fails in production. Mocked OS interactions, missing edge cases, and the gap between test coverage and real-world AI
AI Agents Break One Step After the Demo Ends
The second click problem - AI agents work perfectly in demos but fail on the very next step in real workflows. Here is why and how to fix it.
Real Users Broke My AI Agent - Failures Testing Never Catches
How real users break AI agents in ways that testing never predicts. Context drops on interruption, unexpected inputs, and the gap between demo reliability
How Solo Founders Use AI Agents to Build Production Healthcare Platforms
One developer built a health AI platform that captures doctor office context - solo. Here's how AI coding agents are enabling solo founders to ship
The Gap Between Agent Demos and Production Reality
SYNTHESIS judging reveals how wide the gap is between polished agent demos and what actually works in production. Most agents fail on the boring parts
How Are You Testing Agents in Production?
Unit tests pass but the agent fails in production. The gap between testing individual tools and testing actual agent behavior is where most bugs hide.
Testing AI Agents Against Real User Scenarios, Not Developer Assumptions
Tests verify what you thought to test, not what users actually do. How to build AI agent test suites that cover real-world behavior instead of developer
What Actually Makes Agent Networks Work - The Boring Stuff
The boring infrastructure - health checks, retry logic, queue management, logging - is what separates agent demos from agent systems that run in production
Deploying a Production App as a Non-Coder with AI Agents
AI coding tools work well for web apps but hit limitations for mobile dev since they're browser-based. Native desktop agents can handle more of the
Error Handling in Production AI Agents - Why One Try-Except Is Never Enough
Why a single broad try-except catches everything and tells you nothing. Production AI agents need granular error handling with different recovery strategies.
Multi-Agent Hype vs Economic Reality in Production
A planner-executor-reviewer agent chain sounds elegant but burns 3x the tokens of a single well-prompted agent. Here is when multi-agent is worth it and
Building a Production iOS App in 35 Hours with Claude Code
A real experience building a production-quality iOS app with Claude Code in 35 hours. The logic was easy - SwiftUI styling was the hardest part by far.
Weekend AI Prototypes vs Production Reality
The weekend prototype is the part people overindex on. Signing, notarization, edge cases, and production polish are 80% of the work shipping real AI desktop