Build, Deploy, and Observe AI Agents at Enterprise Scale

Most teams stitch together orchestration tools, model APIs, and monitoring services, creating fragile and hard-to-maintain systems. VAST AgentEngine replaces that complexity with a unified runtime, shared tool registry, and built-in observability. It’s designed for production from day one—handling autoscaling, identity isolation, and persistent state automatically—so developers focus on behavior design, not infrastructure.

VAST AgentEngine enforces data security through identity-bound access, where data retrieval is bound to the identity of the person using the agent.  When agents retrieve or reference data, they do so using the same permissions defined in the underlying storage system. This ensures RAG workflows always respect current access policies, eliminating the risk of privilege drift or stale permission models that can occur in other AI platforms. Governance and observability are built in, so access remains transparent and auditable.

VAST AgentEngine delivers comprehensive AI agent observability that extends beyond traditional infrastructure metrics to the reasoning behavior of agents themselves. Every agent action is logged, including detailed access logs, tool usage metrics, prompt and response history, and full chain-of-thought tracing. This transparency enables compliance reporting, streamlines debugging, and helps build user trust. The platform also captures feedback loops—both explicit signals like thumbs-up/down ratings and implicit reward cues—making this data immediately available for dynamic memory updates and future model refinement. For agent fleet management, this level of insight is critical for continuous improvement and production reliability.

Yes. VAST AgentEngine uses the Model Context Protocol (MCP), an open standard for agent-tool interaction, making integration straightforward. The platform includes a shared registry where you can publish custom tools alongside VAST's curated suite, creating reusable components accessible across your agent fleet. Tools are versioned, monitored, and discoverable through the Studio interface. Whether you're connecting to external APIs, internal workflows, or existing data pipelines, the MCP-based approach ensures standardization and portability. This composability lets teams build complex agent workflows without rebuilding integrations from scratch.

Unlike other frameworks, VAST AgentEngine is built for the demands of production AI agent deployment. It provides enterprise-grade capabilities including Kubernetes-based autoscaling via Knative, Kafka compatible message queuing for guaranteed durability and ordering, and stateful scratch space for resilience against failures. 

The AI OS handles event-driven workflows, allowing agents to be triggered directly from storage events like file updates or deletions—connecting infrastructure activity to real-time AI actions.