You build the agents
Builder
You are wiring models into tools, repos, queues, and customer workflows. Start with the mechanism: grants, target enforcement, receipts.
See the layers →For teams putting AI into real work
If an agent touches money, customers, code, or operations, “the model said so” is not enough. Graph Native links what the agent knew, who delegated authority, what was executed, and what became true — before autonomy scales past your controls.
No signup. No invented metrics. A model you can test against your own stack.
“Most AI systems can explain what they did.
Cloud Native organized infrastructure. Graph Native organizes authority, context, execution, and proof.
Far fewer can prove they were allowed to do it.”
Where you start
The same gap looks different depending on what you own. Start where the pain is already yours.
You build the agents
You are wiring models into tools, repos, queues, and customer workflows. Start with the mechanism: grants, target enforcement, receipts.
See the layers →You own the risk
You will be asked how autonomy was controlled after something goes wrong. Start with the check your board, auditor, or regulator will run.
Run the 60-second check →You have heard “governance” before
Most governance talk means dashboards, reviews, and policy documents. Start with the difference between recording and refusing.
See the test →The map
Different players are building different layers of one stack. Graph Native is the name for the connected whole — and for the layers that are still missing.
Context & decision records
Open-source context graphs, decision journals, PROV-O provenance, deterministic reasoning. Strong evidence that the memory layer is real.
Local governance kernel
Policy interception, agent identity, AgentMesh, an Agent Hypervisor. Governs what the agent attempts — beside the agent.
Agent computer
Workspaces, generated apps, Gatekeepers, wallets. An execution environment where humans, agents, and apps meet.
Graph storage
The storage substrate. Essential — but storing relationships is not the same as governing action.
The connected category
The umbrella: the doctrine, the observatory, the test — and the layers few others name: authority lineage, execution grants, target enforcement, outcome proof, earned autonomy.
Choose the next surface
The category page explains the gap. The console concept shows how a governed operating surface could feel. The design lab isolates alternate visual directions without diluting this canonical page.
Authority ledger, proof metrics, deployment config, and dense operational views — all labeled as simulated concepts.
Design lab Compare ten visual directionsInstitutional blue, kinetic green, pastel AI, quiet fintech, brutalist lime, and more — each contained in its own preview.
The agent · graphnative.xyz Meet the agent that asks before it actsThe governed-agent surface: the run loop, capabilities under grant, and the refusal path — all labeled concepts.
The platform · graphnative.io Read the headless endpoint contractGrants, target verification, sealed receipts, and autonomy review — the backend the surfaces speak to.
Your stack Run the readiness check firstIf authority, execution, and outcome do not stay connected, start there before choosing any interface.
What this page will not do: invent customer counts, hide demo data, or call a dashboard “control.” Simulated modules are labeled. The comparison is a capability test. The standard is simple: before a consequential action, prove authority; after it, prove truth.
Questions
No. A graph database stores nodes and relationships. Graph Native is an operating model: context, authority, execution, evidence, and outcomes stay connected so consequential AI action can be authorized before it happens and verified after it happens.
No. The model assumes those pieces exist. The question is whether memory, policy, execution, and proof are connected into one chain — or scattered across tools that each hold a partial account of the same action.
Pick one consequential action an agent should never take without permission, then test whether your stack can show delegated authority before execution and verified outcome after execution.
This page is a category surface. It names the architectural gap, shows the test, and maps the layers. Product surfaces can implement the model; the category is bigger than any one vendor.
Recording explains an action after it happens. It does not, by itself, bind authority to a purpose, refuse an unauthorized transaction at the target, or prove that the required outcome became true.
Pick a real action your AI should never take without permission. Then ask whether your stack can prove authority before execution and truth after it. If it cannot, you have found the gap Graph Native closes.