Evidence enters.
Bounded evidence comes from the workflow: source checks, model output, policy constraints, approvals, exceptions, and gaps.
How Forge works
AI agent action in. Defensible proof out.
Forge starts where the AI agent market gets uncomfortable: an agent is about to approve, release, bind, block, submit, change, or escalate something inside an enterprise workflow. Forge does not replace the agent, the workflow, human authority, or data custody. It turns the evidence around that action into proof a regulator, auditor, insurer, board, customer, or program office can verify.
The six things Forge preserves.
Most systems leave a trail of activity. Forge creates the trail an outside reviewer needs to understand why an AI agent action cleared the rules at the moment it mattered.
The rule is applied.
The customer defines the action menu and rules. Forge checks the evidence against that boundary.
Uncertainty stays visible.
Forge does not bury weak evidence. It carries confidence, conflicts, missing items, and the reason a hold or escalation exists.
Human authority stays named.
The proof trail shows where a person reviewed, where a person was required, and what the system was allowed to do.
The result verifies.
A reviewer can replay the canonical input and check the cryptographic signature without trusting the original system's own logs.
Calibration is measured.
When the customer later tags outcomes, Forge keeps score against prior confidence. That is measurement, not silent training.
Reviewer logic
The outside reviewer should not have to trust your own log.
- What evidence mattered? Forge names the evidence used, where it came from, and what it supported.
- What rule controlled the action? The policy boundary is visible instead of buried in code, email, or tribal memory.
- What uncertainty remained? The trail keeps the weak spots in view: missing facts, low confidence, conflicts, and required review.
- Who held authority? The human-review boundary is explicit, so the buyer can show where autonomy stopped and accountability stayed.
Forge grows with the customer without training on the customer.
Calibration is simple in plain English: Forge states its confidence, the customer later tags what actually happened, and Forge shows whether that workflow is calibrated, over-confident, under-confident, or too early to call. The customer gets better measurement of the workflow. Forge does not silently train on customer data.
| Day one | Forge can evaluate a named workflow immediately after integration and return a proof trail for each selected action. |
|---|---|
| After outcomes | The customer's outcome tags let Forge compare prior confidence with real results and show where the workflow is earning trust. |
| What improves | The buyer sees where confidence is strong, where review boundaries need to stay tight, and where the workflow needs better evidence. |
| What does not happen | Outcome feedback does not train Forge on customer data, create cross-customer learning, or hide model behavior inside an opaque loop. |
Start with the agent action that would hurt to defend later.
One workflow. One action menu. Synthetic or sanitized evidence first if needed. Forge integrates through the API, returns the proof trail, and gives the buyer a clear read on whether the next step is worth taking.