You launched your multi-agent pipeline in production. It worked beautifully in testing. Then, two weeks later, users start filing vague complaints: the outputs feel "off," the summaries miss the point, the recommendations don't quite fit. You rerun your test suite. Everything passes. You check your

Agentic AI has a spending problem, and most teams discover it far too late. Uber's engineers found out in April 2026: the company's entire annual AI budget had already been consumed after rolling out Claude Code broadly across its engineering organization. They were not outliers. Industry

In June 2026, something remarkable happened: Cursor, GitHub Copilot, and Windsurf all abandoned unlimited subscription models within days of each other. The flat-rate era of AI dev tools was over, and every developer who had been shipping agents without thinking about token efficiency suddenly had a bill that made

Your evaluation suite gives the agent a 90% accuracy score. You ship it. Within a week, production is failing on roughly one in three real user requests. This is not a hypothetical scenario; it is the experience of most teams deploying agentic AI systems in 2026. The problem is not

The traces looked perfect. Every tool call was logged, every reasoning step was captured, every LLM response was recorded in sequence. The agent's trajectory was a model of clean, efficient execution. And for 48 hours, it quoted stale Q1 prices to every customer who asked for a shipping

When a multi-step AI agent workflow breaks, the instinct is to rewrite the prompt. Add more instructions, tighten the wording, or upgrade to a larger model. This approach rarely works, and the reason is straightforward: most failures in production agent systems are not reasoning failures. They are architectural failures.