How Leading AI Teams Design Evals for Production Agents

As AI transitions from single models to autonomous "fleets" of agents, traditional testing proves insufficient because real-world user behavior is too unpredictable to be fully covered by test suites. To ensure reliability, leading companies are prioritizing a dual approach: implementing automated technical instrumentation to detect anomalies and keeping human domain experts close to catch subtle errors. Ultimately, successful production deployment requires building low-friction evaluation frameworks that focus on structural system failures rather than simply relying on better-performing models.