The predictions are everywhere. AI will write all the code. Engineering headcount will collapse. Junior developers will be obsolete. Most of these forecasts come from people who have not operated AI-assisted development systems at scale, and it shows. I have spent the past year talking to engineering leaders at companies that are actually running AI … Read more
The inflection point for AI agents has arrived. This week’s industry signal is unmistakable: the market is no longer debating whether AI agents will enter production—it’s grappling with how to make them reliable, observable, secure, and governed at scale. This shift from proof-of-concept to production-grade infrastructure is precisely what harness engineering addresses. Let’s examine the … Read more
You deploy an agent to production. Task completion rate looks acceptable in your logs — roughly 80% success. Two weeks later, a customer files a support ticket: the agent has been silently returning malformed outputs on a specific input pattern. You check your dashboards. Nothing flagged it. You trace backward through the execution logs and … Read more
The inflection point for AI agents has arrived. This week’s industry signal is unmistakable: the market is no longer debating whether AI agents will enter production—it’s grappling with how to make them reliable, observable, secure, and governed at scale. This shift from proof-of-concept to production-grade infrastructure is precisely what harness engineering addresses. Let’s examine the … Read more
There is a clean dividing line in the history of enterprise AI. Before 2024, the dominant deployment pattern was a chatbot: a system that accepted a user’s text input, generated a response, and stopped. The contract was simple — one turn, one output, no side effects. Infrastructure implications were minimal. The model did the hard … Read more
As AI agents move from research prototypes into production systems, the infrastructure layer—what we call harness engineering—becomes the primary differentiator between aspirational demos and reliable, scalable deployments. Today’s news cycle reveals a maturing industry grappling with five critical challenges: distribution strategy, coordination patterns, privacy-first architecture, governance at scale, and real-time observability. Let’s break down what’s … Read more
Most teams deploying agents in production treat governance as a compliance checkbox — something you bolt on before a security review, not something you engineer from day one. That approach collapses. An agent with access to your CRM, email system, and internal databases is not a chatbot. It is a principal in your system, capable … Read more
A team at a logistics company shipped an agent that routed customer requests to the correct department. It worked well in demos. Two weeks after deployment, they changed the system prompt to handle a new product line. Customer routing accuracy dropped from 94% to 71% overnight. Nobody noticed for three days because they had no … Read more
Most AI agent projects take 2-3x longer and cost 2-4x more than the initial estimate. Not because the technology is harder than expected, but because teams underestimate the gap between a working prototype and a production system that handles real users, real failures, and real compliance requirements. The prototype works in two weeks. The production … Read more
A customer support agent that costs $0.03 per conversation at launch costs $0.45 per conversation six months later. Nobody changed the prompts. Nobody added features. But the context windows grew, the conversation turns multiplied, users discovered complex edge cases, and the retry logic fired more often as usage patterns diversified. This is the standard trajectory … Read more