The problem: context breaks agents.
AI agents do not fail at generation. They fail at interpretation when context is untrusted, incomplete, or unconstrained.
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 Digital Integrity Platform (DIP)
An end-to-end platform for authentic context: cryptographic provenance, policy enforcement, and live trust graphs.
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.
Authentic context supersedes systems of record
Systems of record describe what is. Authentic context governs what is allowed to be believed and acted upon. Before agents reason, context must be authored, verifiable, policy-bound, and enforced at runtime.
DIP makes that layer real. It combines cryptographic provenance, policy enforcement, and live trust graphs into a single integrity pipeline consumable by humans and autonomous agents.
Bad behavior is not just monitored. It is made unrepresentable through structural enforcement and auditability at every decision point.
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
Why this matters now
2025 was about content. 2026 is about context.
- Interpretation becomes the attack surface
- Hallucinations are downstream symptoms
- Agent safety is a provenance problem
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
Ready to secure context before agents act?
Make trust verifiable, governed, and enforceable across your autonomous stack.