How to turn AI production feedback into better agents

To improve AI agents, teams must connect production traces, curated data, and evaluations to identify failures, choose improvements, and prove the next version works before it reaches users. After the first deployment, the focus shifts to maintaining tools and coordinating handoffs, which can detract from actual improvement. The AI loop consists of five stages: running a model or agent, observing its behavior, curating signal into data, improving the system, evaluating the result, and repeating.

Each stage requires carrying sufficient context for the next team to act. To capture behavior after deployment, teams should look beyond availability and monitor traces, metrics, tool usage, and behavioral feedback. Production examples can be turned into useful datasets and refreshed evaluation suites, preserving the lineage that explains where an example came from.

When moving from training to production, it's essential to match changes to failures, adjusting the harness, switching models, or refining behavior through reinforcement learning, supervised fine-tuning, or model distillation. Evaluating the candidate against repeatable standards before, during, and after deployment ensures that the release shows what improved, rather than relying on a few promising answers.

To facilitate this process, CoreWeave Forge provides a development environment that connects running, observing, curating, improving, and evaluating stages. CoreWeave Registry manages models, agents, and datasets, while Weights & Biases Models tracks experiments, analyses, and automated workflows. CoreWeave Agent Lens traces steps, decisions, and tool calls, with conversation views and technical detail, while CoreWeave Inference offers Serverless and Dedicated Inference for accessing open-weights models based on workload needs.