The operational control plane for enterprise AI.
Agents that work in demos fail in production without governance, quality and visibility. AgenticOps is one federated platform where Operational Intelligence is enforced and run, with security, observability, policy and evals built in from day one.
Every layer of enterprise AI operations, unified.
AgenticOps replaces the stitched-together point tools of the AI operations stack with five offerings on one control plane.
Code to operate, with evals as the gate
Plan, provision, ship and run agents and Kubernetes infrastructure, with Agent Evals in CI/CD and production.
Learn more Agent EvalsEvals are the new CI for agents
Offline gates in CI/CD and online scoring in production, on its own or inside AgentOps.
Learn more AI GatewayOne gateway. Any model. Every guardrail.
Access, policy, cost and audit for every model, agent and MCP interaction.
Learn more MCPOpsOpenAPI to MCP server in under 30 minutes
Design, generate, deploy and operate governed MCP servers your agents can trust.
Learn more AI Token Cost OptimizationUp to 50–60% lower AI coding costs
Optimize coding-assistant requests and route each task to the right model, with no workflow changes.
Learn moreStart with a single capability. Most teams begin with one offering, often the AI Gateway, and add the rest as their AI programme grows. Each one joins the same control plane, so nothing has to be re-integrated.
One identity. One policy. One audit trail.
AgenticOps is one operating environment the offerings run within, not several products placed side by side. A control defined once applies wherever your agents run.
One identity
One identity and access model across every offering, for users, applications and agents.
One policy
One policy engine, enforced at runtime at the point of action, everywhere.
One audit trail
One record covering models, agents, releases, MCP servers and infrastructure.
One set of signals
Measured consistently and exported to the tools your operators and auditors already use.
The connections are part of the product, not your integration project.
The offerings are built and tested together as a system before they reach you. Integration is something we maintain, not something your team takes on.
Security only counts if it can't be bypassed.
The moment an agent can reach a model directly, every control becomes optional. AgenticOps puts one control plane in the path of every AI interaction, where identity is verified, policy fires, safety scans run, budgets are enforced and telemetry is captured.
You can't govern what you can't see.
Each offering produces its own signals, measured continuously and exportable to your existing tools. They are also what Operational Intelligence learns from.
AI Gateway
Cost per model, team, project and agent. Token usage and budget headroom. Routing and fallback events. Policy hits. Spend forecasts.
Agent Evals
Eval pass rates by suite and version. Behavioural drift from baseline. Regressions across providers. Step-level failures on the execution graph.
AgentOps: release & run
Release outcomes and rollback time. Reasoning traces per run. Tool-call activity. Kill-switch and policy events. Cluster health, tag coverage and drift.
MCPOps
Per-tool call volume and latency. Deployment and version history. Rate-limit and access events across Dev, Staging and Prod.
Product & engineering
- Faster AI experimentation and delivery
- Less operational complexity
- Focus on outcomes, not plumbing
Platform & security
- Centralized governance and control
- Policy-driven guardrails by default
- Standard operations across teams and environments
Leadership
- Faster, safer enterprise AI adoption
- Predictable cost and visibility
- Lower enterprise and compliance risk
Where Operational Intelligence is enforced
Operational Intelligence works on its own in your environment. With AgenticOps, the knowledge it captures is enforced at runtime, and every governed action becomes new knowledge to capture.