The Production Engineer’s Guide to Multi-Agent Supervisor Design: What Actually Breaks (And How to Build Past It)
The real engineering decisions behind multi-agent supervisor design — what breaks in production, how to scope workers, manage state, and build systems that actually hold.
What Actually Breaks When You Ship a GPT to Production (And How to Stop It)
5% of production LLM requests fail. Here’s the operational breakdown — prompt drift, rate limits, rollback complexity — and the engineering controls that stop it.
The Engineer’s Reality Check: What Actually Happens When You Ship AI to Production
70-90% of AI pilots never reach production. Here’s the engineering and operational reality of what actually has to change — from data readiness to kill-switches.
Agentic Pipelines: Why They Break in Production — and How to Engineer Ones That Don’t
Most agentic pipelines fail not because of bad models — but bad architecture. Here’s a technical deep-dive into building AI-first ops that hold in production.
The Deployment Gap: Why Most AI Workflows Die Between the Notebook and the Network
Only 48% of AI projects reach production. Here’s why they fail and how the fastest teams ship workflows in weeks, not months, in 2026.
Why Agent Workflows Break in Production — And the Engineering Discipline That Fixes Them
57% of orgs have AI agents in production but observability is still the weakest layer. Here’s the engineering discipline that makes agent workflows actually reliable in 2026.
The Engineering Reality of AI Agent Handoffs: Why the Seams Break (and How to Fix Them)
Multi-agent AI systems fail 50%+ of the time at handoffs, not model steps. Here’s the engineering reality: root causes, handoff contracts, protocols, and production patterns.