Enterprise AgentOps platform and agentic control plane
Build a control layer for organizations that have deployed many AI agents without centralized governance. The platform manages agent identity, policy enforcement, observability, evaluation, lifecycle, security, cost metering, and deterministic emergency controls while allowing agents to execute through a separate data plane.
online B2B Deep Tech / Infrastructure
From IBM Technology — Agent control planes & OpenAI model solves Erdős at 00:58
Problem: Organizations lose track of where agents are, what data they access, how they behave, how much they cost, and whether they comply with security and regulatory requirements. Probabilistic behavior also makes ordinary software testing insufficient.
For: Enterprises running dozens or hundreds of AI agents across business units, especially organizations requiring air-gapped, isolated, hybrid, compliance-focused, or on-premises deployments.
Examples
- IBM watsonx Orchestrate: supports on-premises and hybrid deployment, can distribute platform components across hyperscalers through OpenShift, can import LangGraph agents, and can connect agents through gateway, MCP, and OpenAI-compatible endpoints.
Behind this: 12 build steps · 5 tools and how each is used · how to validate demand · 1 more real example · 9 things the video never answers.
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