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Give your AI agents a brain they can trust.
Build agents that act — safely, traceably, across systems. One shared knowledge graph, one governance layer, one audit trail. Every action grounded in governed enterprise data; every decision traceable to the row it came from.
What is an AI agent governance platform?
An AI agent governance platform controls what data every AI agent can see and what actions it can take — and proves it, logging each decision with full provenance. Fluree enforces this at the data layer: policies live in the graph itself and evaluate at query time, so no prompt injection or agent-framework vulnerability can bypass them.
Governance pairs with shared context. Fluree runs your agents as an AI agent mesh — specialized agents for sales, finance, compliance, and content sharing one governed knowledge graph. Every agent draws from the same data, respects the same policies, and logs every action with full provenance.
Without that, teams build agents the way they built apps a decade ago: disconnected, ungoverned, impossible to audit. That’s agent sprawl — a proliferation of tools that erode trust instead of building it.
Fluree makes the shared brain and the shared rulebook native — AI-ready data underneath, governed agents on top — and the same graph that grounds your agents powers enterprise AI search for your people.
Agents on top. Data at the bottom. Governance in between.
A three-band stack that gives every agent a shared governed brain, enforced policies, and a full audit trail — regardless of framework or model.
Agents
Any framework, any model
Fluree
The mesh layer
One governed brain. Every agent, every action.
Knowledge
Shared governed graph — one truth across every agent.
Governance
Policies embedded in the data, not the prompt.
Protocol
MCP, REST, and SPARQL — any framework, any LLM.
Audit
Every action logged — data, policy, user, time.
Sources
Connect everything
Any database · any document · any system
Six properties DIY agents and vendor platforms can’t give you.
Shared context, data-layer governance, action provenance, and framework freedom — built in, not bolted on.
Shared context, not silos
Every agent draws from the same knowledge graph. No inconsistent views, no duplicated entity resolution — one truth across the whole mesh.
Governance at the data layer
Policies live in the graph, not the prompt. No prompt injection bypasses them; no agent framework vulnerability exposes data it shouldn’t see.
Action provenance, not just logs
A full cryptographic trail of what data was queried, what policy evaluated, what action was taken — and by whom, for whom, when.
Any framework, any model
MCP, REST, SPARQL. LangChain, CrewAI, AutoGen, custom. Claude, GPT, Llama, Bedrock. Bring whatever stack you already use.
Human-in-the-loop by design
Configurable escalation per action and risk. High-risk actions wait for human approval; low-risk actions execute. You set the threshold.
Scales without sprawl
Add agents without adding governance overhead — every new agent inherits the shared brain and rulebook automatically.
DIY agent stacks vs.
governance at the data layer.
A direct capability comparison between do-it-yourself agent stacks and agents governed on Fluree.
Capability | Traditional DIY & platforms | Fluree Governed agents |
|---|---|---|
Shared context | Each agent builds its own view | One governed knowledge graph for every agent |
Governance | App-level or platform IAM | Data-centric — policies in the graph itself |
Audit trail | Prompt/response logs | Data + policy + action + user + time, cryptographically |
Entity resolution | Not supported | Golden records across every system |
Framework freedom | Single framework or vendor lock-in | Any LLM, any framework, via MCP |
Human-in-the-loop | Custom or basic approvals | Configurable escalation per action + risk |
Scaling | Agent sprawl as the org grows | Shared brain + rulebook, no added overhead |
Context model | Keyword or vector lookup | Full ontology — entities, relationships, meaning |
The agent governance playbook.
Webinars, whitepapers, and practitioner guides for building governed multi-agent systems in production.
The Future of RAG — Graph-Native AI with Fluree and MCP
The protocol + governance + graph story end-to-end — exactly the stack behind a governed agent mesh.
Watch replay
The Power Trio Reshaping Business Intelligence: GraphRAG, MCP & LLMs
Why these three together produce agents that actually work in production — and what the stack looks like end-to-end.
Read the articleThe complete guide to retrieval, knowledge graphs & LLMs.
Download whitepaper
Enterprise AI Accuracy: Building Reliable AI Systems
The architecture patterns behind AI systems that hold up under audit — with governance enforced at the data layer.
Read the articleRecognized by Gartner
Everyone can build an agent. Not everyone can govern a fleet of them.
Fluree makes governed autonomy the default — one shared brain, policies that can’t be prompt-injected away, and cryptographic provenance for every action your agents take.