Over the past year, AI agent projects have flooded the landscape—autonomous coding assistants, browser automation tools, multi-agent orchestration frameworks. Every week brings a new entrant. But an uncomfortable truth remains: most agents still fail frequently in production, and for the same reason—they lack a trustworthy runtime.
The industry has poured enormous effort into optimizing how models think, yet almost no one has focused on how agents execute. This imbalance is becoming increasingly dangerous.
Prompt Engineering Solved the Easy Part
Prompt engineering was indispensable during the early phase of LLM adoption. Techniques like chain-of-thought, ReAct loops, and planning agents dramatically improved reasoning quality.
But they also created a dangerous illusion:
