Evidence-bound execution for AI agents.
GeoClear helps teams verify agent actions before tool calls, workflow handoffs, or production deployments are accepted. Valid evidence proceeds. Missing evidence holds. Policy violations block. Tampered evidence rejects.
Containment limits what an agent can reach. GeoClear helps the customer-designated system verify whether the action has the evidence required to cross the gate.
The market is shifting from AI answers to AI actions. Identity says who is acting, model safeguards influence what the model produces, logs describe what happened, but only GeoClear creates patent-pending operational evidence records for AI actions the receiving system can check before accepting the action.
GeoClear is not an agent framework or execution harness. It does not run, orchestrate, or contain your agents, and it does not replace your runtime. It is the independent verification-and-evidence layer that sits beside whatever agent stack you already use: your system keeps control of the action, GeoClear verifies the evidence and issues the record.
Three wedges. One verification path.
GeoClear meets AI agents at three points: at the tool call, at the agent-to-agent handoff, and at the CI/CD release gate. Each one carries the same operational evidence record the receiving system or pipeline can verify before accepting.
Agent tool-call interlock
- Agent proposes a tool call
- GeoClear binds the operational evidence record
- Receiving system verifies before accepting
- MCP-compatible workflows supported
Agent-to-agent handoff
- One agent passes work to another
- The handoff carries operational evidence
- The next receiver verifies locally
- No single agent is trusted on its own claim
Agent Harness for CI/CD
- Plan-Execute-Verify discipline at deploy time
- Behavioral checks gate the release
- Unsafe deployments are blocked by the pipeline
- Evidence retained for later review
AUTH.md as customer-provided policy context.
For supported repositories, GeoClear can use AUTH.md or equivalent repository policy files as customer-provided policy context for agent-harness checks. These files help define what an agent, workflow, or automated system is allowed to do before deployment or execution. GeoClear treats AUTH.md as policy context, not as model authority.
The repository policy file sits next to the code it governs. The harness reads it as customer-provided input alongside other policy sources. The verification result becomes part of the customer-held operational evidence record.
Four outcomes. One verification path.
Operational evidence, not raw mission data.
The tool action carries an operational evidence record. Raw mission or enterprise data stays inside the customer boundary by default.
Sent with the action
- Actor
- Action
- Policy reference
- Evidence commitments
- Approval state
- Freshness
- Signature / issuer reference
- Verification result
- Customer evidence record
Stays with the customer
- Raw imagery
- Raw logs
- PII
- Invoices
- Mission data
- Full policy systems
Where policy runs.
Three ways to produce the policy result. One verification path.
Customer-computed
Customer computes the policy result locally. GeoClear binds the result into a verifiable operational evidence record.
Minimized evaluation
GeoClear evaluates customer-approved minimized attributes only.
Customer-controlled boundary
GeoClear software is deployed inside a customer-controlled or authorized integrator-managed environment.
Customer-held evidence after every action.
The customer keeps the evidence. Verification works without a live GeoClear application call.
- · Operational evidence record (the technical artifact).
- · Operational evidence record summary the agent and downstream reviewer can inspect.
- · Verification material (customer-held).
- · Customer-held evidence bundle.
- · Offline verification path — try the offline verifier →
Start with the demo. Then ask for the deep dive.
The verification layer demo shows valid / missing / tampered outcomes in motion. Architecture brief covers the substrate and deployment modes.