@levie: Almost all AI model and agent progress is downstream from evals. Open weights post training for specific domains comes …
Summary
Almost all AI model and agent progress depends on evaluations (evals). Understanding workflows and agent performance through evals will become a core enterprise competency for driving automation.
View Cached Full Text
Cached at: 06/23/26, 03:51 PM
Almost all AI model and agent progress is downstream from evals. Open weights post training for specific domains comes down to evals. Agent improvements in the applied AI layer is all about evals. Agentic enterprise deployments that actually can augment work is all about evals. It’s all evals.
This will become a core competency of any enterprise in the future. The companies that are able to best understand their own (and/or customers) workflows and how well agents participate in that work will be in the best position to actually drive real automation.
Similar Articles
@OpenAI: Let’s talk about evals. We’re always looking for better ways to measure and forecast model progress, especially as benc…
OpenAI discusses the importance of evals (evaluations) for measuring and forecasting model progress, especially as benchmarks become saturated or gamed, featuring insights from Tejal Patwardhan and Andrew Mayne.
How evals drive the next chapter in AI for businesses
OpenAI publishes a framework for business leaders on using AI evaluations (evals) to measure and improve AI system performance in organizational contexts, distinguishing between frontier evals for model development and contextual evals tailored to specific business workflows.
@AdamRLucek: What are Online Evals? Most agent evals run "offline": a premade dataset of inputs goes through the agent, and an inter…
Explains the concept of online evaluations for AI agents, which measure agent performance on live traffic over time, as opposed to offline evaluations that use fixed datasets.
How are you all actually evaluating agent decisions, not just agent outputs?
The author questions how to evaluate AI agent decisions beyond simple pass/fail metrics, proposing cost-weighted evaluations and confidence calibration.
Why agents that pass every eval still drift once they hit real production traffic
AI agents often drift in production after passing evals due to distribution shifts and upstream changes; continuous evaluation and real-time monitoring can mitigate this.