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.
One shared brain. Governed autonomy. Action with provenance.
An AI Agent Mesh is an architecture where specialized agents — sales, finance, compliance, content — share a common governed knowledge graph. Every agent draws from the same data, respects the same policies, and logs every action with full provenance.
Without one, 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 — so you can scale agents without scaling governance overhead.
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 agents & platforms vs.
the Fluree Agent Mesh.
A direct capability comparison between do-it-yourself agent stacks and Fluree’s governed Agent Mesh.
Capability | Traditional DIY & platforms | Fluree Agent Mesh |
|---|---|---|
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 Mesh 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 the 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 the mesh as the governance 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.