# CoreWeave Fully Connected 2026 Keynote: Introducing CoreWeave Forge | Built for Adoption

## Executive summary

The CoreWeave Fully Connected 2026 keynote focused on moving AI from the demo stage into reliable, scalable production environments. The central theme is the 'AI loop' (Run, Observe, Curate, Improve, Evaluate), which is necessary for continuous improvement of AI agents. Key announcements included the launch of CoreWeave Forge, a unified platform for managing the entire AI lifecycle, and the debut of the NVIDIA Vera Rubin NVL72, designed specifically for the demanding, multi-agent inference era. Industry leaders demonstrated how to apply these tools in high-stakes domains like finance (Capital One) and physical automation (Caterpillar).

## Key takeaways

- CoreWeave Forge: Unifying the AI Loop: CoreWeave Forge brings all steps of the AI lifecycle—Run, Observe, Curate, Improve, and Evaluate—into one connected environment. This allows teams to move from a first agent to a best agent by ensuring context and evidence flow seamlessly between stages.
- NVIDIA Vera Rubin NVL72 Launch: The Vera Rubin NVL72 is announced as ready for production, designed from the ground up for inference in the agentic era. CoreWeave operates the entire stack, ensuring consistency across all layers (power, cooling, firmware, network, and applications).
- Enterprise AI Evaluation Standards: Capital One detailed their evaluation-first architecture, moving beyond legacy N-gram scorers (like BLEU/ROUGE). They utilize LLM-as-a-judge with Chain-of-Thought rubrics and track specialized metrics like 'actual state changes' (verifying API calls) to ensure trustworthy, auditable AI outputs.
- Agentic Failure Modes: The complexity of agents introduces new failure modes: context/memory drift, behavioral traps (false claims of action), and systemic complexity (minor early errors compounding exponentially). Robust evaluation must account for these multi-step failure patterns.

## Technical details

- AI Agent Architecture & Performance: Agentic workflows are complex, requiring multiple models (one for processing context, another for observing meaning) and tool calls. The process is a continuous inner loop of reasoning, consuming tokens, and performing actions, which is a data center scale problem.
- CoreWeave Mission Control: This system provides operational intelligence for AI workloads, monitoring everything from workload behavior to the physical state of the rack. It enables continuous optimization by identifying root causes (e.g., KV cache issues) and suggesting improvements (e.g., adjusting cache thresholds or scaling replicas) to boost throughput and efficiency.
- Model Distillation and RL Rollouts: To improve models, teams can use CoreWeave Model Distillation to create smaller, specialized models based on production traces. For Reinforcement Learning (RL), CoreWeave launched RL rollouts, allowing weights to be loaded straight into a running deployment without waiting for a full serving redeployment, accelerating the learning cycle.
- Data and Infrastructure Composability: The platform is designed to be composable, supporting six generations of NVIDIA accelerators (from Volta to Vera Rubin). This ensures that the infrastructure can adapt to evolving workloads (e.g., running both consumer-scale traffic and long-context coding agents) without requiring a full stack overhaul.

## Practical implications

- Implement an 'evaluation-first' architecture by integrating continuous evaluation (CI/CD) into your AI pipeline, using LLM-as-a-judge and golden datasets to enforce trust and safety.
- Adopt a unified platform like CoreWeave Forge to manage the entire AI lifecycle, ensuring that insights gained during 'Observe' directly inform 'Improve' and 'Evaluate' steps.
- Focus on observability tools (like Agent Lens) to detect subtle, high-impact failures in production agents, rather than relying solely on basic health checks.
- Leverage specialized hardware (like Vera Rubin) and advanced compute capabilities to handle the unique demands of multi-agent, long-context, and high-throughput inference workloads.

## Topics

AI Agent Development, MLOps, Cloud Infrastructure, Continuous Integration/Continuous Deployment (CI/CD), LLM Evaluation, Hardware Acceleration (NVIDIA), CoreWeave Forge, CoreWeave Builder Resource Center, CoreWeave Cloud

Source: https://www.youtube.com/watch?v=vRh3PtkQOOo