Agentic Integrations

Let agents verify provenance, integrity, and processing context before they take consequential action.

AI agents act on information they receive, but the boundary between the outside world and automation is where trust breaks down. Before an agent pays, approves, or files, it needs to know the input is genuine, unaltered, and understood in context.

The agent trust problem

An agent that acts on an unverified document inherits every risk in that document. Provenance, integrity, and processing context are what separate a safe action from a costly one.

A tool and API pattern for processing and verification

Staple AI provides the processing and verification step agents call before acting, returning structured results with the evidence behind them.

• Verify provenance and integrity at the boundary.

• Return an evidence payload downstream agents can rely on.

• Gate higher-risk actions with human approval and policy checks.

Evidence that travels with the data

Results can carry a Metastructured Data payload, so verification is available to every downstream agent and system rather than being lost after the first step.

A reference architecture to start from

Review a sample integration journey and a reference architecture for adding verified, policy-aware inputs and outputs to your agentic workflows.

Ask to see:

• A reference architecture for verification at the agent boundary.

• An example evidence payload passed to downstream agents.

• A sample integration journey from first request to production.