A service that appears healthy in monitoring can mask an AI agent whose output quality is declining. The solution lies in systematizing how production failures become evaluation cases that someone is accountable for maintaining.

Once an agent reaches production, the initial deployment becomes just the beginning. The real challenge emerges: How do teams ensure answers remain accurate and users stay satisfied? What operational costs accumulate? Without deliberate process, teams often spend more effort maintaining infrastructure and coordinating between groups than actually advancing the application.

Find the gaps between teams

When AI engineers hand off a trained model to application or site reliability engineering (SRE) teams, a structural problem emerges. The researchers know what quality looks like; the operations team owns the running service. Yet they often examine entirely different signals.

Application traces flow into dashboards designed around uptime metrics. An on-call engineer sees a stable system while the research team gains little visibility into answer quality or user satisfaction. Evaluation frameworks become stale as user patterns shift, but no single team owns updating them. When the next release cycle begins, researchers, AI engineers, and business stakeholders each consult separate dashboards.

The on-call engineer sees a healthy service, while the research team learns little about answer quality or user sentiment.

Start by identifying who can convert a production failure into a reusable evaluation case and who maintains that case over time. If answering requires moving context between teams, that handoff is where the process breaks down.

Connect the five stages of improvement

The AI improvement cycle follows a repeating pattern: execute a model or agent, monitor its output, transform production examples into datasets, enhance the system, test the result, and begin again. Each stage must preserve enough information for the next team to take action.

Run

Select the model and agent harness—the surrounding code and infrastructure—suited to the task. Gather the signals needed to investigate behavior once the system is live.

Observe

Look beyond uptime metrics. Collect traces, performance data, tool invocations, and user feedback to examine how an agent reasons and pinpoint failure points.

Curate

Convert production instances into structured datasets and refreshed evaluation frameworks. Maintain human review in this process and document the origin of each example.

Improve

Align the modification to the identified failure. This might mean adjusting the harness, switching models, or tuning behavior via reinforcement learning (RL), supervised fine-tuning, or model distillation. Specify the expected outcome in quality, speed, or expense.

Evaluate

Test the candidate against consistent benchmarks before, during, and after rollout.

A release should show what improved, rather than rely on a few promising answers.

Keep the record with the work

These handoff challenges informed the design of CoreWeave Forge, unveiled last week at the Fully Connected 2026 conference. The platform integrates running, observing, curating, improving, and evaluating into a unified development workspace. MasterClass and Canva are among the early adopters using Forge to complete their AI loop.

A connected environment's value shows itself when you can trace any deployment back to its model version, evaluation framework, dataset, and production examples. CoreWeave Registry manages models, agents, and datasets; Weights & Biases Models tracks experiments, hyperparameter sweeps, analysis, and automated workflows. These integrated records enable teams to understand what changed and whether it delivered improvements.

For observability, CoreWeave Agent Lens records steps, decisions, and tool calls with conversation views and technical breakdowns. Monitors score active traffic with human review and curation. The launch announcement reports Agent Lens achieves 20% better failure detection and resolves issues at half the cost; teams should validate these figures against their specific workloads.

CoreWeave Notebooks delivers managed Python environments for shared evaluations and custom analysis. CoreWeave ARIA examines runs, suggests experiments, and recommends code changes stored in GitHub. Its connection to Weights & Biases Models enables automated research based on detected patterns. Evaluation evidence stays alongside any proposed change so teams can assess the outcome.

CoreWeave Sandboxes provides isolated central processing unit (CPU) or graphics processing unit (GPU) environments for agents, tool calls, RL, and evaluations. CoreWeave ARIA and CoreWeave Sandboxes are generally available; Agent Lens, Notebooks, and Model Distillation are new in CoreWeave Forge and currently in preview as part of CoreWeave Training.

Choose the improvement your workload needs

Post-Training harnesses production signals to enhance model quality, latency, and cost without requiring a dedicated training cluster. Serverless SFT (supervised fine-tuning) and Serverless RL (reinforcement learning) enable teams to experiment with custom training approaches.

For tasks already validated in production, Model Distillation trains a smaller, open-weights model on outputs from the larger model. It then compares the candidate directly against the current version. This head-to-head comparison provides evidence for deciding whether to shift traffic. A smaller model only delivers value if it meets the task's requirements.

Treat inference as part of the loop

Every stage depends on inference, but different workloads require different levels of control. CoreWeave Inference offers Serverless Inference for accessing open-weights models and Dedicated Inference for workloads on isolated infrastructure.

Serverless Inference places infrastructure management on CoreWeave. Dedicated Inference gives teams control over model weights, deployment configuration, and GPU allocation. Select based on workload needs; teams can transition between options as requirements change.

Cline uses open-source models via Serverless Inference for its open-source, open-choice coding agent, which the launch post describes as trusted by more than 11 million developers. Grammarly uses Dedicated Inference with explicit GPU selection and fully managed operations.

RL introduces additional complexity. The policy generates rollouts—samples of its behavior—training produces updated weights, and those weights return to serving for the next iteration. Slow checkpoint loading becomes a bottleneck for the entire cycle.

The new RL rollouts feature in Dedicated Inference leverages the NVIDIA Dynamo foundation to hot-load updated checkpoints with minimal downtime. CoreWeave partnered with NVIDIA and you.com's engineering team to post-train NVIDIA Nemotron 3.5 Lightning using RL Rollouts in NeMo gym with you.com's web search application programming interface (API). The launch post reports a 15-fold improvement in model reload latency compared to baseline.

Keep the loop open to your stack

An improvement loop extends into the observability platform, data warehouse, and security infrastructure already in place. The CoreWeave Partner Network spans infrastructure, data services, independent software vendors (ISVs), and models and inference, with solutions validated on CoreWeave under production workloads.

This includes tools that agents invoke. Exa, Parallel Web Systems, and You.com provide live web search through a single integration. Search grants agents access to information beyond their training data; observing and evaluating tool calls and resulting answers remains essential.

CoreWeave Forge was built for teams running workloads on CoreWeave Cloud, other cloud providers, and on-premises data centers. Improvement data remains portable, with open interfaces between stages, so curated datasets and evaluation suites keep their value regardless of where you deploy.

Start with one production failure

Select a failure your team can explain. Track it from trace to curated example, proposed fix, evaluation, and deployment. Document where context gets lost and where accountability becomes unclear. This concrete case becomes your starting point for integrating the loop.

CoreWeave Forge offers a 30-day Pro free trial with credits across product lines for teams wanting to test this workflow. For those aiming to validate production workloads with direct access to CoreWeave experts and infrastructure, CoreWeave ARENA is available. The objective is to ensure each deployment informs what you build next, backed by evidence that the next version outperforms the previous one.