An AI-native operating system that connects agents, workflows, and data into a single operational layer — turning AI from a set of experiments into a managed business capability.
of enterprises start AI pilots
achieve their stated AI goals
of content has zero AI discoverability
connective tissue between tools
Autonomous, governed units of work. Each agent has a defined scope, tool set, and governance boundary. They execute tasks, report results, and escalate when confidence is low.
Orchestrated sequences that connect agents to business processes. Workflows define the order, conditions, and human checkpoints that turn agent output into operational decisions.
The governed data layer that agents read from and write to. Schema-validated, access-controlled, and auditable. Every mutation is logged. Every source is tracked.
Design, deploy, and manage AI agents with defined scopes, tool registries, and governance policies. Each agent is observable, auditable, and constrained.
Build multi-step workflows that chain agent actions, human approvals, and conditional logic. Visual canvas for workflow design, execution logs for debugging.
Schema validation, access control, audit trails, and data quality monitoring. Every piece of data in IO has provenance, type safety, and lifecycle tracking.
Connect IO to external systems — CRMs, content platforms, analytics tools, and APIs. Managed integrations with retry logic, rate limiting, and error handling.
Standards for responsible AI usage including guardrails for data access, prompt injection defense, output validation, and human-in-the-loop escalation policies.
Performance dashboards, agent execution metrics, workflow completion rates, and data quality scores. Automated daily digests and exception reports.
IO is not theoretical. It runs a real business. See how it works at Windfield, or read about the architecture in detail.