Gateway. Guardrails.
Deterministic enforcement.
One platform.
Runtime AI governance infrastructure to trust and control AI usage across the enterprise
Orchestrate IT, Security, Cost, and Compliance guardrails
Map and enforce action surface; route and attribute every model and MCP call
Enriched context-graph trace and record of every agent action
Evaluate scope and authorize every agent action before it executes
What is an agent control plane?
An agent control plane is the governance layer between your AI agents and the systems they act on. For every action an agent attempts it makes a deterministic decision, in the path of the action and before it executes, on whether that action is allowed: which identity the agent runs as, which data it may touch, and whether a person must approve it first.
What does a neutral control plane mean in plain English?
Neutral means it is not tied to one model vendor, agent framework, or MCP server. Policy is written once and enforced the same way whether the agent runs on OpenAI, Anthropic, Bedrock, or something built in house. Without that, every agent platform gets its own incompatible rules, and nobody can answer what your agents are collectively allowed to do.
How does an agent control plane actually work?
In three layers. At design time the agent's action surface is mapped and classified, so you know what it could do before it ships. At runtime every action is authorized against that scope before it executes, with the agent's identity and the requesting human both attached. Every decision, allowed or denied, is recorded as evidence rather than as a log line.
Why does enforcement have to happen at runtime?
Because an agent decides what to do while it is running, not when it ships. The action it takes depends on the prompt it was given, the data it just read, and what the model made of both, none of which exist at review time. Design-time analysis tells you what an agent could do; only a runtime check can rule on what it is about to do. It is also why detection is not a substitute: a log saying an agent deleted a production record is evidence, not a control.
Why does the decision have to be deterministic?
Because an authorization decision has to be reproducible and explainable, and a probabilistic one is neither. If the thing deciding whether an agent may move money is itself a model, you have added a second system that can be wrong in unpredictable ways to govern the first. Segregation of Duties is a rule, not a pattern: whether the agent that raised a purchase order is now approving it is a fact you evaluate, not a likelihood you score. The same action against the same policy must produce the same answer today and in a March audit.
Why does governance sit outside the agent's execution loop?
Because an agent that is behaving unpredictably cannot be the thing that decides whether its own behaviour is acceptable. Forrester makes this the defining property of the category: oversight has to remain possible precisely when the agent is doing something you did not expect. Controls that live inside the agent fail in the case they exist for.
Is a control plane the same as an AI gateway or an AI firewall?
No. A gateway governs the request path: routing, keys, rate limits, and which model answers. A firewall inspects content for prompts and data it considers unsafe. A control plane governs the decision to permit the action at all, which is a different question and the one an auditor asks. Most enterprises end up running a gateway and a control plane together.
For the analyst framing behind the category, including Gartner's AI TRiSM and Forrester's Agent Control Plane market, read the full explainer.
How It Works
Every agent action moves through three layers before it ever acts on your enterprise systems: a single control plane connecting AI agents to the systems they touch.
Why one platform, not three point solutions
Point solutions cover one layer of the agent lifecycle. LangGuard covers all three, on shared policy and a shared audit trail.
| Layer | What it governs | LangGuard |
|---|---|---|
| MCP & Agent Gateways | Routes and authenticates every model and MCP call; meters cost | Built-in |
| Guardrails pre-reasoning | Screens intent before the agent reasons; maps to security, compliance, and operational risks | SCOPE-MCP |
| Deterministic enforcement post-reasoning | Evaluates the planned action itself and allows, escalates, or blocks it before it executes | Arbiter |
Already running a gateway? Arbiter works alongside what you have. Deterministic enforcement is the layer worth adding regardless of what sits underneath it. See how Arbiter works →
Four Pillars of AI Governance
The foundational capabilities every enterprise needs to govern AI agents at scale
Accountability
"What are the approved AI assets in use?"
Gain visibility into AI assets that previously have no system of record
Traceability
"How is the approved AI agent behaving in use?"
Provision and approve AI assets, not just code or infrastructure
Auditability
"Is the AI agent behaving as intended?"
Provide immutable evidence of AI agent decisions and behaviors
Enforcement
"Is the AI agent action authorized?"
The only layer that stops an unsafe action before it happens, not after
Forward-Deployed Engineering
Don’t just buy the platform. Ship a governed agent.
LangGuard embeds forward-deployed engineers with your team to move a priority agent into trusted production: fixed scope, tied to production milestones, in four to six weeks. Compliance-ready for SOC 2, PCI, HIPAA, ISO, and more from day one. You keep the agents, policies, and implementation.
Foundation
Weeks 1–2
Scope locked around one priority agent or workflow. The runtime governance harness is deployed across your gateways, tools, models, and runtimes.
Policies & Integrations
Weeks 3–4
Identities, trusted paths, and human authority controls wired in. Policies authored for what Arbiter should allow, escalate, or block.
Production Cutover
Weeks 5–6
Monitoring, audit, and containment live. Runbooks handed off. The agent runs governed in production, on a foundation your next agent reuses.
Integrates with your stack
LangGuard sits above your existing infrastructure, not inside it. It integrates with the tools you already use for routing and identity, and governs the actions your agents take on top of them.
Recognized need by Industry Analysts
Leading analysts recognize the AI Control Plane as critical enterprise infrastructure
Forrester
An agent control plane is an enterprise control plane that inventories, governs, orchestrates, and assures heterogeneous AI agents across vendors and domains.
Gartner
As enterprises move to AI agents that take action, the control plane serves as the "missing" infrastructure for ensuring observability.
Opus Research
AI agent control plane as the shared brain and rulebook that sits above experience and applications.