The agentic AI governance landscape, mapped
“AI gateway,” “AI governance platform,” “agent security tool,” “MCP registry” — the labels get used almost interchangeably, but the products behind them govern different boundaries of an agent’s life and answer different questions. Here’s what each one actually does, where mAIndala fits, and which one you need.
Six categories
AI governance & compliance platform
- Governs
- The organization’s AI systems as registered entities — models, applications, and agents — through intake, risk assessment, and policy workflow.
- Sits at
- The documentation and approval layer, around deployment rather than inside it.
- Does not cover
- What a system does at runtime. It governs what was declared about an AI system, and is not in the call path when the system acts.
Example: Credo AI
AI / agent gateway
- Governs
- Traffic — LLM calls, MCP tool invocations, and agent-to-agent delegation — as it passes through a proxy.
- Sits at
- The call path, at runtime.
- Does not cover
- The definition behind the call. An agent nobody approved and a fully vetted one are indistinguishable at the gateway if both present valid credentials.
Examples: Gravitee, TrueFoundry
AI-native platform / LLMOps
- Governs
- The infrastructure agents run on — model serving, deployment, routing, and cost attribution — with role-based control over who may deploy and execute.
- Sits at
- The execution and infrastructure layer.
- Does not cover
- Agents that run somewhere else. Governance is scoped to workloads on that platform.
Example: TrueFoundry
Agentic AI security & posture
- Governs
- Agent behavior — discovering agents already running, scoring their exposure, detecting anomalous or malicious activity, and responding to it.
- Sits at
- Runtime observation and response, typically after deployment.
- Does not cover
- Whether an action was permitted under a stated policy, as distinct from whether it looked dangerous. Output is alerts and investigations rather than a portable record of authorized activity.
Example: Zenity
MCP registry & hosted runtime
- Governs
- Supply — which tool servers exist, how they are discovered, and, where hosted, how they are run and credentialed.
- Sits at
- The capability-supply layer, before an agent is assembled.
- Does not cover
- Use. A registry governs what is available, not what an agent did with what it installed.
Example: Smithery
Agentic AI trust & governance control plane
This is usThe other five categories each govern one boundary of an agent’s life. This one governs four, and turns the result into evidence somebody else can check.
- Governs
- The agent’s definition before it runs, the credentials it acts with, the tools it can reach, and the record it leaves — for agents built on any platform, including ones built somewhere else entirely. Policy applies at three checkpoints rather than one, and the resulting record exports as a signed, independently timestamped file a third party verifies offline, without an account on the platform that produced it.
- Sits at
- Approval time, install time, and call time.
- Does not cover
- Model-level evaluation (bias metrics, fairness testing, model documentation); model serving, training, or LLMOps infrastructure; high-volume protocol proxying with real-time distributed tracing; behavioral threat detection feeding a security operations center; or deployment into air-gapped and on-premises environments — it is cloud-hosted. It does not claim organization-level security certification.
Which one do you need
- Your priority is turning frameworks like the EU AI Act or the NIST AI RMF into a policy-pack library across a large, heterogeneous AI estate, with a formal risk-assessment workflow. → that’s a AI governance & compliance platform — see Credo AI.
- You need protocol-level proxying — multi-provider LLM routing, real-time distributed tracing — at high volume, and you already run gateway infrastructure you want to extend. → that’s a AI / agent gateway — see Gravitee.
- You need the model-serving and LLMOps platform itself, not only governance of what runs on it — deployment, routing, and cost attribution under one vendor. → that’s a AI-native platform / LLMOps — see TrueFoundry.
- Your agent estate runs substantially on Microsoft Copilot Studio or Power Platform, and detecting and responding to attacks already in production is the immediate need. → that’s a Agentic AI security & posture — see Zenity.
- You want the largest available selection of MCP servers and the fastest possible path to a running hosted server, with no governance requirement yet. → that’s a MCP registry & hosted runtime — see Smithery.
- You need to know an agent’s definition was approved before it ran, control the credentials and tools it can reach, and hand a regulator or auditor a record they can verify without access to your systems — for agents built on any platform, not only ones built on one vendor’s infrastructure. → that’s mAIndala.
Compare mAIndala directly
Category map last reviewed August 29, 2026 against publicly available information. See something out of date or inaccurate? Let us know.
Product and company names mentioned are trademarks of their respective owners. mAIndala is not affiliated with, endorsed by, or sponsored by any product named on this page.
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