Evaluation frameworks become critical for production AI agent reliability

2026 is being dubbed the year of AI evaluations, as companies move agents into production for customer-facing roles. Because LLMs are non-deterministic, evaluation harnesses are essential to ensure agents perform correct tool calls and adhere to company policies. Companies like DoorDash, LinkedIn, and Uber are building internal evaluation systems to measure not just answer correctness, but task completion, cost, latency, and safety. The evaluation process has become a core step in the development lifecycle, determining go/no-go decisions for releases. Metrics have expanded to include the number of human interventions required and whether unsafe actions occurred. For internal agents, evaluation rigor may be lower, but for external, customer-facing agents, it is a mandatory requirement to prevent rogue behavior and ensure reliability in real-world workflows. Panelists argue that without rigorous evaluation, it is impossible to know if an agent has gone rogue or is violating policies, making it a prerequisite for serious enterprise deployment. As one panelist stated, "If you're building a real serious agent, for your company, then definitely a must."