Forge AI Accountability

Lists the five sample scenarios for the fictional Acme Mutual Insurance that run_check accepts, with their evidence ids. Demo data only.

Takes no parameters.

Computes a Forge Orbital verdict for one of five fictional Acme Mutual Insurance scenarios and returns it with a signed decision record in the response. Read-only: nothing is stored, sent or changed. Optionally withdraw evidence ids to see how the verdict changes. Demo only: sample rules and sample evidence, not connected to the user's systems, policies or live actions.

Verifies the Ed25519 signature of a Forge Orbital demo decision record against the published demo key id. Pass the full signed record object returned by run_check (this demo keeps nothing, so a record id alone cannot be looked up). Editing any field makes verification fail. Demo records only.

Replays a Forge Orbital demo decision: re-runs the deterministic engine on the same sample scenario (and any withdrawn evidence) and checks that the replay key and the decision match the record. Replayability is the point: the same inputs and engine version give the same decision. Reads nothing about the user. Demo only, fictional sample data.

Verifies a Forge Orbital demo decision record against the pinned demo key and summarizes what it attests: signature status, the decision, whether the engine abstained, whether human review is required, and how many evidence items were used. Does not store the record. Demo records only.

Explains, in plain English, why Forge Orbital reached the verdict in a demo decision record and what evidence a reviewer would need to change it. Read-only, stateless, demo data only.

Run the Forge accountability engine evaluator and optimizer on synthetic examples or supplied labeled old/new outputs. For samples use payload={"sample_mode":"improved"}, or regression/changed_path. For a user-supplied comparison, translate only their supplied actions into this exact format: payload={"workflow":"User test","cases":[{"id":"case-1","expected":{"action":"hold"},"baseline":{"output":{"action":"allow"},"trajectory":[]},"candidate":{"output":{"action":"hold"},"trajectory":[]}}]}. Replace example actions with the user actions; never invent labels or steps. If the user has not said what should happen, ask them in plain English before scoring. The user never needs to write JSON or know these field names. Cases need id, expected.action, baseline.output.action, baseline.trajectory, candidate.output.action and candidate.trajectory. Use empty trajectory arrays when no action steps were supplied. Do not use plain strings for expected/baseline/candidate. Omit usage entirely unless token usage was supplied. If a format error occurs, repair the structure without changing the evidence and retry. A description alone returns a standard preparation checklist, not a measured evaluation. No code runs, external agent calls, changes or deployment. Synthetic prices are unsigned estimates, never approved savings.