Evidence-bound agent CI/CD.
Apply Plan-Execute-Verify discipline to AI agent behavior before it reaches production.
Test agent behavior before deployment. GeoClear applies a Plan-Execute-Verify harness to agent workflows so teams can define policy, run behavioral checks, block unsafe deployments, and retain customer-held evidence of what was tested.
How the harness works.
The harness wraps an AI agent's behavior at deploy time, applying the same structural discipline engineering teams already apply to code. The action proceeds only with valid evidence. A deploy is blocked when behavioral checks fail. A record lands in the customer-held evidence vault either way.
Agent proposes a plan.
The agent declares the action it intends to take and the evidence supporting that plan.
Harness checks policy.
The harness evaluates the proposed plan against the customer's compiled policy and required evidence path.
Behavioral tests pass or fail.
Behavioral checks run against the plan: required evidence present, approval state captured, scope respected.
CI/CD blocks unsafe deploy.
When checks fail the CI/CD pipeline halts the deployment. The customer's pipeline is the enforcement point.
Operational evidence record retained.
A signed operational evidence record lands in the customer-held vault. The customer keeps the proof either way.
Offline verifier confirms later.
The retained record verifies later without contacting GeoClear servers. Independent reviewers can replay the check on their own infrastructure.
Where it fits in your stack.
The Agent Harness sits next to your existing CI/CD gates. The action executes only if authorized by your compiled policy, and the patent-pending operational evidence record lands in the customer-held vault. If the behavioral checks fail, the pipeline deterministically blocks the deployment.
Plan-Execute-Verify discipline at deploy time.
The same review pattern your code already passes through, now applied to AI agent behavior. The plan is reviewed against policy before any action runs. Behavioral checks gate the deploy. The verify step lands as evidence the customer keeps and can replay.
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.
Behavioral regression gates for agent releases.
Treat agent behavior the way you already treat code: behavioral tests, release gates, evidence of what changed. When an agent's plan diverges from approved scope, the pipeline holds the deployment instead of accepting on trust.
Where teams start.
Common entry points for DevOps, platform engineering, AI engineering, and back-office automation teams.
Back-office automation.
Approvals, payments, procurement, finance, and administrative workflows where the receiving system should verify evidence before accepting.
Agent tool-call interlock.
Tool calls from AI agents into customer systems. The interlock holds the call until the customer-designated system verifies the evidence path.
Agent-to-agent handoff.
Workflows that hand off between agents or services. Each handoff carries operational evidence the next receiver can verify locally.
Release gates for AI features.
Block AI-feature deployments when behavioral checks fail. Keep evidence of what was tested for later review.
What GeoClear proves. What GeoClear does not prove.
Honest boundaries are the most persuasive part of the trust story. The harness proves that the plan followed the approved evidence path; it does not stand in for model correctness or business judgment.
GeoClear proves
- The identity of the agent and the proposed action.
- The evidence the plan committed to.
- The policy result and the approval state at decision time.
- The verification result and tamper status of the retained record.
- Whether the action followed the approved evidence path before the customer-designated system accepted it.
GeoClear does not prove
- That the AI was right.
- That the mission or business decision was correct.
- Legal compliance by itself.
- Risk-free operation.
- That GeoClear replaces your systems.
When model confidence, model service cards, evaluation reports, or calibration evidence are available, GeoClear can enforce customer policy over that evidence. The model signal is evidence, not authority by itself.
Talk to us about the Agent Harness.
Architecture brief, integration shape, and an honest scope conversation. No demo theater.
Request architecture brief