AGX monitors agent failures, automatically drafts safety policies, shadow-tests them against your historical data, and enforces them in real time. Stop guessing if your business rules work: test them before they hit production.
read-only diagnostics · historical shadow-testing · inline policy enforcement
Built for finance and operations teams that need to know a guardrail actually works before putting it in the critical path.
Joined delivery, contract, and invoice evidence.
Survived historical replay (0 regressions).
Narrowed eligibility threshold to safe cohort.
Deployed to runtime.
Decision systems assume the business logic is already known. Generic agents act without enough proof. Analytics shows symptoms after the fact. AGX closes the gap between historical evidence and governed execution.
A workflow with documented logic.
The logic may be stale, too broad, or missing the conditions that actually separate safe cases from risky ones.
AGX tests candidate guardrails against historical cases before they become production workflow gates.
Faster execution through AI agents.
Speed without a proven control boundary creates audit gaps, behavioral drift, and unclear ownership.
AGX gives agents deterministic boundaries: what they may prepare, route, request, block, or escalate.
Dashboards, alerts, and root-cause analysis.
Finding a problem is not the same as stopping the next bad invoice, claim, forecast, or approval.
AGX turns evidence into inline checkpoints that existing workflow systems can enforce.
AGX is not a generic workflow engine, and it is not passive analytics. It is a continuous policy engine that turns operational evidence into enforced business guardrails.
standardize fragmented ERP, CRM, PSA, warehouse, and event data into bounded evidence fields
generate candidate guardrails, backtest them against historical logs, reject weak policies, and tune thresholds
return allow, block, needs_evidence, or review_required inside existing workflows with explicit degradation rules
let agents prepare packets, request evidence, and route exceptions only inside the approved guardrail
An enforced policy checkpoint with source evidence, owner boundaries, inline response states, degradation behavior, and audit history.
The same operating model extends to any workflow where decisions, evidence, policies, and outcomes can be reconstructed.
AGX shadow-tested a delivery-to-cash policy that pauses invoice readiness when billing evidence is missing, rate terms conflict, or scope recovery is unresolved.
Connect delivery, contract, approval, and invoice evidence.
Reject billing guardrails that fail historical replay.
Narrow thresholds to the safe invoice-readiness cohort.
Return allow, block, needs_evidence, or review_required.
AGX shadow-tested a margin-readiness policy that requires staffing and cost drift to clear evidence and owner review before forecast-ready status is accepted.
Connect staffing, cost, utilization, forecast, and owner evidence.
Reject margin guardrails that fail replay or policy checks.
Set thresholds for cases where intervention actually helps.
Require evidence, owner clearance, or remediation before forecast lock.
Start with one painful workflow, then extend to adjacent processes once the evidence model and shadow-testing loop are working.
AGX starts read-only. If the data cannot support a shadow-test, you stop. If it can, the pilot backtests candidate guardrails before any production rollout.
See pricing →Map the evidence schema, reconstruct historical execution, and quantify the risk.
Generate, backtest, reject, and tune candidate guardrails against your past cases.
Run approved guardrails as inline checkpoints while agents operate inside the boundary.
Short answers for the blockers that usually slow down governed agent adoption: workflow ownership, messy data, availability, and rule authority.
No. AGX is an inline checkpoint. Temporal, ServiceNow, SAP, or your existing workflow system handles the state machine. AGX handles the decision gate.
AGX provides the evidence schema and gap analysis. Your data team maps source data into bounded evidence fields; AGX tests whether that evidence can support a guardrail.
The degradation behavior is explicit. Depending on the workflow, AGX can fail open with mandatory post-hoc review or fail closed for high-risk cases.
No. AGX searches and tunes candidate guardrails inside a human-defined domain grammar. It rejects weak policies and only promotes the ones that survive historical replay and policy checks.