AI agents operate on unverified context.
When Agent A tells Agent B something, how does Agent B know where it came from, who authored it, or if it's been tampered with? Today, agents trust blindly. That's fine for demos. It's catastrophic for enterprise.
Inferred context
RAG, embeddings, and context graphs optimize recall but do not establish authority, provenance, or permission.
Implicit trust
Today’s stacks treat context as accumulated, vendor-owned, and implicitly trusted. That becomes a systemic risk when agents act.
No runtime enforcement
Once agents operate on meaning, context must be verified and policy-bound at runtime, not audited after the fact.
The provenance layer for AI agent context.
C2PA solved content authenticity for images. We're solving context authenticity for agents—proving the knowledge they use is legitimate, versioned, and traceable.
Sign under policy
Every artifact ships with enforceable context: in-toto attestations, C2PA claims, DSSE envelopes, embedded licensing, and required metadata.
Automate the trust graph
DIDs, verifiable credentials, and DTO mirroring keep context anchored in a live, resilient trust fabric.
Validate at runtime
LLM proxy enforcement, LangGraph/A2A integration, and policy gates ensure agents only receive authorized context.
The PKI for the agent economy
When an AI agent receives context, it can cryptographically verify the entire lineage—who created it, what it was derived from, and that it hasn't been modified.
We combine versioned JSON-LD context graphs, C2PA signing, in-toto attestations, and runtime policy enforcement into a single infrastructure layer for trusted agent ecosystems.
No more blind trust. Every piece of context your agents consume has provenance you can verify.
What makes Noosphere different
We are the source of authentic context that other systems depend on.
Not a knowledge graph
We do not infer meaning. We authenticate the context those graphs rely on.
- Authored context, not inferred
- Cryptographic proof, not implicit trust
- Runtime enforcement, not post-hoc audits
Not a memory system
We do not accumulate context. We govern what can be trusted and used.
- Policy-bound, not ambient
- Authorized access, not assumed
- Auditable decisions, not opaque chains
Not a RAG platform
We do not improve recall. We ensure context is authoritative and allowed.
- Standards-based, not platform-locked
- Normative context, not descriptive summaries
- Enforced at runtime, not audited later
Not a system of record
We do not describe what is. We govern what can be believed and acted on.
- Policy-defined permissions
- Live trust anchors
- Context constraints for agents
The agent economy is coming
Google A2A, Anthropic MCP, OpenAI Assistants—agents are becoming first-class citizens. But there's zero infrastructure for agent trust.
- C2PA solved images. We're solving context.
- SSL for connections. Provenance for knowledge.
- The PKI for AI agent ecosystems.
Who this is for
Teams building and governing autonomous workflows.
- Platform and infrastructure teams
- Security and trust leaders
- AI product teams shipping agents
Policy-enforced context
Before agents reason, context must be authored, verifiable, and enforced.
- Authored, not inferred
- Verifiable, not assumed
- Enforced at runtime, not later
Integrity pipeline, end-to-end
From creation to runtime, context remains authentic and enforceable.
- Cryptographic provenance
- Live trust graphs and DTO
- Runtime policy enforcement
Built on open standards
No proprietary lock-in. No opaque trust assumptions. Just verifiable context that survives every feed, API, and agent.
Working with Industry Leaders
Build on verified context.
Stop letting your agents trust blindly. Give them cryptographic proof of where their context comes from.