Agentic Trust

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.

It matters because…

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.

Protocols move messages, not trust

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.

Disclosure applies to your output

Synthetic content has to carry machine-readable disclosure in the EU and California. That obligation attaches to what your models generate.

What gets attested

Model output

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.

Context and knowledge graphs

Attested JSON-LD, so an agent can check the provenance of a claim rather than only a file.

Workload identity

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

What changes once it runs

01

Trust you set, not trust you inherit

Decide which sources and counterparties your agents accept, curated as a trust graph rather than a vendor list.

02

Verification before action

Deterministic, auditable and fast enough to run at the point of decision rather than after the fact.

03

A record of what was believed

When an agent acts on bad context, the trail shows what it had and where that came from.

Most organisations are more than one

Provenance is only as good as its governance.

Tell us what your agents produce and what they act on.