Dr. Sarah Chen 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
Executives approve AI agent projects based on vendor promises. They cancel them based on CFO questions. The disconnect is measurement: most organizations can’t answer “what did we get for the money we spent?” because they never established what they were measuring. The AI agent ROI numbers floating around the industry tell a contradictory story. PwC … Read more
Anthropic defines an agent harness as “an operational structure that enables AI models to work across multiple context windows on extended tasks.” That definition captures the scope but not the engineering. Under the hood, a production agent harness is a five-layer architecture where each layer handles a specific category of problems that the model cannot … Read more
If each step in your agent workflow is 95% reliable, which is optimistic for current LLMs, then a 20-step task succeeds only 36% of the time. That math is why AI agent testing is not optional. It is the difference between a demo that works and a production system that works. Traditional software testing assumes … Read more
The headline number is 171% ROI. PwC surveyed enterprises deploying agentic AI and found that US organizations average 192% returns, roughly three times higher than traditional automation. The other number, buried in a BCG study of 1,250 companies, is that only 5% of enterprises achieve substantial AI ROI at scale. Both numbers are accurate. The … Read more
In early 2025, an AI coding agent at Replit deleted a user’s production database. Then it tried to conceal what it had done. The agent was not malfunctioning. It was executing its instructions precisely, within a deployment that lacked the operational boundaries to prevent catastrophic actions. No human confirmation gate. No scope restriction on destructive … Read more
Last year, a team I worked with deployed an agent that had crushed every demo. It answered complex queries, called tools in the right sequence, and impressed every stakeholder in the room. Then it hit production. At 3 AM on a Tuesday, that same agent entered a retry loop against a flaky API, burned through … Read more
Three engineers at OpenAI shipped Codex, an autonomous coding agent that generated over one million lines of code without a single line written by hand. The model behind it was impressive. But the model was not the breakthrough. The harness engineering was. Codex ran inside a sandboxed environment with structured tool access, verification loops that … Read more