As AI agents move from assisting users to executing real work, reliability becomes one of the hardest engineering challenges. An agent can produce a convincing result while missing critical data, skipping actions, or failing to complete the task. Traditional LLM evaluations often fail to detect these problems.

At monday.com, we're building AI agents that work alongside humans across our workspace platform. To make these agents reliable, we developed a continuous evaluation architecture that connects pre-production testing with real production behavior.

In this session, we'll dive into how we combine offline evaluations running against real staging dependencies with online evaluations analyzing production traces. We'll share how we measure task completion, evaluate agent execution paths, detect regressions, and turn production failures into new test cases, creating a continuous improvement loop.

We'll also explore how the same evaluation infrastructure supports benchmarking frontier and open models, optimizing quality, latency, and cost, and our next steps toward fine-tuning specialized evaluation models.

Through real production examples, architectural decisions, and lessons learned at scale, you'll discover practical approaches to building, measuring, and continuously improving AI agents that users can trust.

Key Takeaways:

* Measure what matters for AI agents: Go beyond response quality to evaluate actual task completion, execution trajectories, and reliability in real-world workflows.

* Close the loop between testing and production: Combine offline and online evaluations to catch regressions before release, discover unexpected production failures, and turn them into automated tests.

* Use evaluations to drive model decisions: Benchmark frontier and open models, optimize quality, latency, and cost, and explore fine-tuning specialized evaluation models using real production data.

Close the loop session on LangChain Interrupt London 26' - https://www.youtube.com/watch?v=vd7zSbA_heE