Agents execute on false premises silently
A prompt-injected agent runs malicious instructions. A context-poisoned agent runs legitimate instructions on corrupted inputs, and looks entirely normal while doing it.
Attest what you produce, verify what you consume
Attest the output of your models and check the provenance of the context your agents reason over — because autonomous systems cannot gut-check a claim the way a person can.
A prompt-injected agent runs malicious instructions. A context-poisoned agent runs legitimate instructions on corrupted inputs, and looks entirely normal while doing it.
Agent-to-agent protocols standardise how systems exchange context. They do not define how to verify that what arrives is authentic and from an authoritative source.
Synthetic content has to carry machine-readable disclosure in the EU and California. That obligation attaches to what your models generate.
Generated media and text carrying machine-readable disclosure metadata as it is produced, so downstream consumers can identify it as synthetic and trace it back.
Attested JSON-LD, so an agent can check the provenance of a claim rather than only a file.
Capability credentials for the agents themselves, evaluated at runtime before they are allowed to act.
Plugs into: Model endpoints and inference services · Retrieval and context pipelines · Agent frameworks and runtimes · Cedar policy at the point of decision
Decide which sources and counterparties your agents accept, curated as a trust graph rather than a vendor list.
Deterministic, auditable and fast enough to run at the point of decision rather than after the fact.
When an agent acts on bad context, the trail shows what it had and where that came from.
Publish with proof attached
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Run media production like a secured pipeline
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Ship releases your customers can verify
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Turn content and AI policy into a control you can evidence
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Stand behind what you issue, under your own name
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Tell us what your agents produce and what they act on.