Pilot autonomous agents with a gate before consequence.

Logs observe. Gates decide.

Enterprise AI oversight cannot stop at audit logs. Logs observe after the fact. AiGentsy gates before consequence: accept, reject, hold, or escalate the agent action — then export signed, independently verifiable evidence. Run a governed pilot before agents create uncontrolled consequence.

Built for teams where agent actions touch consequential or regulated workflows — financial services, insurance, employment, procurement, compliance operations, and internal approvals. Regulated teams increasingly need operational evidence, not policy PDFs.

Bring your own gates · test your gate

Define what should be accepted, rejected, held, or escalated before consequence — then watch the Vault show how the gate behaves across mandate → work → evidence → decision → consequence → ProofPack → Verify. Deterministic demo fixture — not a production policy editor; nothing is saved.

Live pilot surfaces

Native Consequence Agent (deployment readiness review) · Attach Your Agent (external coding agent gated by AiGentsy) · ProofPack export · offline verifier · tamper failure · accepted / rejected / escalated / retry branches · consequence allowed / blocked / held · Savings Trace labels where proof supports reuse.

Run an AI output through the acceptance gate and see what AiGentsy prevented, reused, shortened, escalated, or verified before consequence moved. Savings Trace is a presentation of fields the runtime already produces — potential exposure gated, evidence gaps identified, policy paths reused, downstream actions held or blocked, and the signed audit artifact behind each decision.

Every Savings Trace item is labeled measured, estimated, or demo/reference. Deterministic demo fixtures only — no live LLM call. Cross-model benchmarking and provider-measured savings remain operator-only.

Savings Trace shows what was gated before consequence moved. Consequence Memory records the accepted, rejected, held, or settled outcome so enterprise teams can verify the record and reuse trusted paths later. Every record carries a recorded decision, recorded consequence state, signed ProofPack export, verifier link, and a decision-envelope reference that shapes future Recall.

HoverStack learns which prior paths, proof shapes, refusal patterns, evidence gaps, and decision envelopes are reusable. Consequence Memory makes that learning visible and connected to accepted, rejected, held, blocked, or settled outcomes — it is not a new learning layer.

AiGentsy does not train on customer model content by default. It records acceptance, proof, verification, settlement, and reuse patterns so autonomous work becomes safer, reusable, and accountable. Every Consequence Memory item is labeled measured, verifier-backed, platform-attested, or demo/reference.

Step 1: Register an agent or workflow: POST /protocol/register

Step 2: Submit work / model output: POST /protocol/proof-pack

Step 3: Evaluate consequential output through Acceptance Runtime: POST /acceptance-runtime/evaluate

Step 4: Export and verify the bundle: pip install aigentsy-verify

Step 5: Read the signed OutcomeReceipt: GET /protocol/deals/{deal_id}/outcome-receipt

Step 6: Open the Vault: vault.html?demo=1

Five institutional layers. One signed, offline-verifiable ProofPack.

The AiGentsy Consequence Layer is the buyer-facing expression of this stack in motion: Recall, Accept, Prove, Verify, Settle.

Full architecture →

HoverStack is the optional compute-governance layer inside AiGentsy Stack.

It governs whether computation was necessary before proof creation (through recall, delta compute, risk gating, and preservation economics) and signs every decision into a Governed Economic Proof bound to the ProofPack.

Recall is the buyer-facing expression of HoverStack’s prior-attested-work reuse capability. HoverStack remains broader than Recall alone: compute governance, Decision Envelopes, negative compute, workflow execution, benchmark validation, and attestation paths.

Enterprise licensing available for high-volume, high-consequence, or multi-agent workflows that benefit from compute governance before handoff.

We are looking for the first production deployment partner.

If you run an agent system where cost, auditability, governance, or state-change accountability is starting to bite, we want to do the integration work alongside your team. Direct email works: w@aigentsy.com.