Legacy systems capture the what, not the why.
Accounts, orders, cases. They record state. They are blind to which exception was granted, what precedent was consulted, what cross-system context informed the decision.
Decision intelligence for enterprise AI agents
The OS for AI agents your enterprise can trust.
Network Operating System (nOS) turns every agent action into a verifiable trace — building a shared memory your whole organization can replay, audit, and reuse.
Two commands
Wire any agent framework to verifiable memory.
npm install -g @origintrail-official/dkg dkg hermes setup
Two commands to give Hermes agents verifiable memory.
Supply chains
Traceable handoffs from source to shelf.
Shared context graphs connect every transfer, inspection, and compliance decision across partners. Portable. No single vendor controls the audit trail.
Cybersecurity
Threat intelligence that compounds across agents.
Context graphs link detections, escalations, and remediations into searchable precedent. Your threat context travels with you — not locked inside one platform.
Transportation
Predictive maintenance powered by shared context across operators.
Context graphs connect IoT sensor data, maintenance events, and cross-border tracking across rail networks. AI agents generate trusted insights from multi-operator data — no system overhauls required.
Life sciences & healthcare
Patient data sovereign. Clinical evidence verifiable.
Context graphs connect drug interactions, trial data, and regulatory submissions with full provenance. Owned by your organization, not your vendor.
Manufacturing
Production intelligence that compounds on the factory floor.
Context graphs link quality checks, process adjustments, and compliance decisions into compounding knowledge. Switch providers without losing a single trace.
Sport
Performance data with unbreakable provenance.
Context graphs connect athlete analytics, scouting, and contract intelligence across seasons. Owned by the organization, portable across platforms.
Government & critical infrastructure
Sovereign AI for energy, transport, and national security.
Context graphs give institutions verifiable decision traces on the DKG. No vendor lock-in. Full democratic accountability and data portability by design.
Construction
Every compliance decision enshrined in the building’s digital twin.
Context graphs connect stakeholders across the building lifecycle. Data persists across contractors, owners, and regulatory bodies — no single point of control.
Financial services
Compliance with end-to-end decision traces.
Context graphs link every regulatory determination into verifiable, portable precedent. Auditors verify independently — no dependency on the originating platform.
Partners and Supporters
The thesis
Systems of record were built for objects. The enterprise runs on decisions.
Salesforce records accounts. SAP records transactions. ServiceNow records tickets. But the reasoning that turns data into action (the exceptions, the precedents, the judgment calls) was never captured. That is the gap AI agents inherit.
Deploy AI on legacy data, inherit every blind spot.
Exception logic lives in people's heads. Precedent from last quarter sits in a Slack thread nobody can find. Cross-system synthesis happens on calls that were never recorded.
Decision traces, enshrined in the DKG.
nOS builds a living context graph where every agent action produces a structured, replayable trace. Not what happened, why it was allowed. Traces compound into precedent. Precedent enables autonomy.
Memory model
Three memory layers, built on Shared Context Graphs.
Individual memory doesn’t scale. Shared context does. These three layers let context move from private creation to collaborative refinement to verifiable reuse.
Working memoryShared memoryVerifiable memory
Layer 1
Working Memory
Private to agents you control. This is where each agent creates memory first (drafts, notes, early context) before deciding what to share.
Scope: private drafts and early context, visible only to the creating agent.
Flow: context starts here before it moves outward.
Flow: context starts here before it moves outward.
Layer 2
Shared Memory
Multiple agents draft, refine, and reuse context together. This is where collective intelligence starts to emerge and context compounds.
Scope: shared project context across agents.
Outcome: compounding intelligence, context gains value as more agents contribute.
Outcome: compounding intelligence, context gains value as more agents contribute.
Layer 3
Verifiable Memory
Where source, lineage, and provenance stay attached. Context can be reused with a verifiable history that helps prevent drift.
Protection: where context came from, how it changed, and what can be trusted.
Outcome: reuse with confidence, the path behind context is always checkable.
Outcome: reuse with confidence, the path behind context is always checkable.
Before
Isolated agent memoryContext stays trapped in one agent, limiting reuse and continuity across workflows.
After
Shared Context GraphsContext compounds across agents while verifiable history helps prevent drift.
Why it scales
Each agent makes every other agent cheaper and smarter.
Legacy systems get more expensive with every agent you add. nOS gets cheaper. Coordination overhead drops as the shared memory grows.
Compounding precedent, not compounding cost.
Every decision trace becomes precedent for every future agent. Agent 50 inherits the compounded intelligence of agents 1–49.
Graphs get richer, not heavier.
Unlike databases that slow as they grow, shared context graphs become more useful with scale. More agents, more traces, richer precedent.
Cost per agent goes down, not up.
Token spend, compute, and coordination all decrease per agent as the graph grows. Economists call this sublinear scaling — no legacy stack can replicate it.
Cost per agent · 1 → 100LegacynOS
At agent 100, the per-agent cost on shared context graphs is a fraction of legacy infrastructure.
Why it matters
Three differences between verifiable AI and the rest.
Shared context over isolated recall.
One agent creates context. Others refine and reuse it. The 50th agent starts with everything agents 1–49 already learned.
Decision traces replace audit logs.
Audit logs record that something happened. Decision traces record why it was allowed to happen. That is what regulators, auditors, and enterprise compliance actually need.
Enshrined in the DKG, not a vendor database.
Independently verifiable and owned by you, not Trace Labs. Replace us with a self-hosted instance anytime, zero data loss.
Replacing systems of record across industries.
Wherever enterprises make decisions that matter (regulated, high-stakes, cross-organizational), nOS provides the verifiable AI infrastructure that legacy systems cannot.
Supply Chains
Traceable handoffs from source to shelf.
Learn moreCybersecurity
Threat intelligence that compounds across agents.
Learn moreTransportation
Predictive maintenance powered by shared context across operators.
Learn moreLife Sciences & Healthcare
Patient data sovereign. Clinical evidence verifiable.
Learn moreManufacturing
Production intelligence that compounds on the factory floor.
Learn moreSport
Performance data with unbreakable provenance.
Learn moreGovernment & Critical Infrastructure
Sovereign AI for energy, transport, and national security.
Learn moreConstruction
Every compliance decision attached to the building’s digital twin.
Learn moreFinancial Services
Compliance with end-to-end decision traces.
Learn moreDr Bob Metcalfe
Internet pioneer · Turing Award · inventor of Ethernet
“We live in a time of abundant connectivity and abundant misinformation. The OriginTrail Decentralized Knowledge Graph is an evolving tool for finding the truth in knowledge. Knowledge graphs improve the fidelity of artificial intelligence.”
Greg Kidd
Early investor · Twitter, Square, Coinbase
“OriginTrail connects the dots between physical and digital supply chains, making real-world assets trackable and verifiable.”
Dan Purtell
Group Director of Innovation, BSI
“Trust is transparency and transparency is trust. Understanding decentralized knowledge graphs can be a bit complex, but what I am excited about is how companies like The Home Depot and SCAN are willing to go ahead and experiment. We have demonstrated very clearly practical applications of decentralized knowledge graphs. This is real world stuff. Digital trust is solving real world problems.”
Ken McElroy
Global Manager, Trade Risk & Export Compliance, The Home Depot
“It has been kind enough to partner with BSI and OriginTrail to really take a circumstance where competitors in a marketplace are able to utilise the technology to provide value-added services to their individual organizations while maintaining the integrity of their own proprietary data.”
Chris Rynning
AMYP Ventures · Piëch-Porsche family office & Umanitek Chairman
“OriginTrail's technology is proven, having gone through several iterations. Its application in tracking and authenticating both physical and digital assets is vital in addressing the upcoming challenges of AI.”
FAQ
Frequently Asked Questions
What is nOS?
nOS is the Network Operating System for verifiable AI. It gives enterprise agents shared context graphs and turns every action into a structured decision trace, recorded in the OriginTrail DKG.
What is a decision trace?
A signed, replayable record of why an agent action was allowed: inputs, policy, exceptions, precedent, outcome. Captured at execution time so any third party can verify it.
How is this different from an audit log?
Audit logs capture that something happened. Decision traces capture why it was allowed, with the policy applied, the inputs consulted, the precedent inherited, and a signature chain back to source.
How is nOS different from a traditional system of record?
Traditional systems of record capture objects after the fact. nOS captures decisions at execution time, with full reasoning, and stores them as queryable precedent your agents can inherit.
What agent frameworks does nOS support?
Out of the box: Claude Code, MCP-compatible agents, OpenClaw, Hermes, LangChain, CrewAI, AutoGen. Pro adds custom frameworks and bring-your-own orchestration.
What does it cost?
The OriginTrail DKG is open-source and free forever. Pro nOS is $1,999/mo, with custom Enterprise pricing for fully bespoke deployments.
Is my data private?
Yes. Working memory and private context never leave your infrastructure. Only commitments and metadata you choose to publish enter the DKG. You control what is published and you control disclosure.
Start free
Power up your business with the OriginTrail DKG.
Create Knowledge Assets, build shared context graphs, enshrine decision traces. Open-source infrastructure. Community support. Zero cost.
Go Pro
The full Network Operating System.
Custom agent frameworks (Hermes, OpenClaw, LangChain, Claude). Custom data pipelines. Custom integrations with your enterprise systems. Dedicated infrastructure and support.