Vendor-neutral Real-time Multi-cloud

One Control Plane for Any
Agent and Data Source

Discover every agent, observe every decision, secure every action. Across any framework, any cloud.

See how it works Request a demo
◉ Connects to any agentic framework
MMicrosoft Copilot Studio
Databricks Agent Bricks
AAWS Bedrock
SSalesforce Agentforce
Cursor
CrewAI
LangChain
Anthropic
Google Cloud
OOpenAI
Snowflake
Meta · Llama
NVIDIA
+Custom builds
PROBLEM
Enterprise AI is scaling. Governance isn't.

Your agents are running. Who's governing them?

Your AI agents are accessing Snowflake, Databricks, SaaS platforms, and internal APIs. But who's enforcing what they can see, what they can do, and what data they can touch — in real time?

Observability tools tell you what happened. Authorization tools tell you what's allowed. Neither unifies the control layer that governs agents and the data they operate on.

That's the gap. Every enterprise running agents at scale is exposed to it.

Here is how it works.

HOW IT WORKS
Three layers. One story.

One Control Plane, powered by a Unified Trust Layer, built on AI-Native Metadata and Context.

Each layer does a specific job. Together they give you something no point tool can: governance that moves with the agent, not behind it.

01

One Control Plane.

A single surface to govern every agent across every framework, every cloud, every data source. Not a dashboard bolted onto your stack. The actual enforcement layer your agents run through.

◉ What you see and operate
02

Unified Trust Layer.

Sits between every agent and everything it touches: data, APIs, tools, other agents. Enforces policy at the moment of action. Before data moves. Before a tool fires. Vendor-neutral by architecture, not by marketing claim.

◉ What sits in the middle
03

AI-Native Metadata and Context.

Every enforcement decision carries agent identity, declared purpose, data lineage, and live policy state. Not static roles. Not job titles. Context that was built for agents and moves with them across every hop.

◉ What powers every decision
Control Plane. Trust Layer. AI-Native Context.
◉ Analyst validation

The category is defined. The gap is real.

"An enterprise control plane that inventories, governs, orchestrates and assures heterogeneous AI agents across vendors and domains."
Forrester Research · Agent Control Plane category definition · December 2025

Forrester places the control plane outside the build plane and the orchestration plane. Independent. Portable. Enforcing across vendors. That is the architecture Trust3 AI ships.

ARCHITECTURE

Agents on top. Data below. Trust Layer in between.

Every agent request passes through the Unified Trust Layer before it reaches anything: data, APIs, tools, or other agents. The layer knows who the agent is, what it declared as its purpose, and what policy allows right now. That context is AI-Native Metadata. It travels with every request, every hop, in real time.

Agentic frameworks
Copilot Studio
Cursor
AWS Bedrock
CrewAI
Agent Bricks
and more
Trust3 AI
Trust3 AI
Unified Trust Layer
Discover · Observe · Secure
Everything agents reach for
Data platforms
Snowflake Databricks BigQuery Iceberg
SaaS & apps
Salesforce ServiceNow Workday Slack
APIs & protocols
MCP A2A Internal APIs Tools

The Unified Trust Layer does three jobs →

01 / DISCOVER

Find every agent. Before it becomes a risk.

Enterprises undercount their agents 3–10x. Shadow AI is real. The Control Plane can't govern what it hasn't found. Discovery is where it starts.

  • Auto-discovery across every framework, cloud, and custom build
  • Identity mapping for ephemeral and delegated agent identities
02 / OBSERVE

Trace every decision. In real time.

If you can't replay what an agent did, you can't ship it. Every prompt, retrieval, tool call, and data access, captured live with full AI-Native Context attached.

  • Full-fidelity traces with real-time drift and injection detection
  • One-click audit evidence for EU AI Act, HIPAA, NIST
03 / SECURE

Authorize every action. At the moment it happens.

Roles say who someone is. They don't say what an agent should access right now, for this declared purpose. PBAC does. Enforced natively, before anything moves.

  • Purpose-based access evaluated per request at Snowflake, Databricks, BigQuery
  • Just-in-time grants, auto-expiring scopes, zero standing access

Discover. Observe. Secure. One Control Plane.

Explore the full platform Read the docs at docs.trust3ai.com

This is where Trust3 AI comes in.

◉ All the way down

Observability and authorization, at the protocol layer.

MCP servers, A2A delegation chains, AI gateways: every wire your agents use is a place to watch and govern. We're built for all of them.

MCP security

Asana's MCP flaw exposed ~1,000 enterprises. The WordPress AI Engine plugin put 100,000+ sites at risk. Every MCP server is an attack surface. We treat it like one.

Read more →

A2A security

Signed Agent Cards stop forgery, not identity propagation. Trust3 AI carries user identity, declared purpose, and PBAC verdicts through every hop, three agents deep.

Read more →

AI Gateway integration

Portkey, LiteLLM, Kong, Cloudflare, Apigee: we don't replace your gateway. We sit behind it and enrich every log with agent identity, purpose, and PBAC verdict.

See observability →
◉ The outcome

What Trust3 AI delivers, in numbers.

Quarterly review boards replaced with embedded guardrails. Audit packs in one click.

10×

Faster to production

Approval cycles compressed from weeks to seconds. Policy enforced where the agent runs.

Aug 2026

EU AI Act, ready

Every prompt and tool call logged, pre-mapped to EU AI Act, GDPR, HIPAA, and NIST AI RMF.

4×

Trustscore dimensions

Security · Safety · Compliance · Accountability. A 0–100 grade per agent, in real time.

0

Surface area for leaks

PBAC plus native masking keeps sensitive data out of model context. Out, not "mostly out."

Ready to build a governed AI enterprise?

The agents are already running. The question is whether your governance is keeping up.

Request a demo Talk to an architect
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