AGX tests which guardrails hold up against evidence before they reach production. AGX Lite then enforces the approved guardrails at runtime, before an agent changes workflow state.
This separation lets agents move work forward while AGX dictates what is actually allowed.
AGX does not require custom data pipelines. It ingests fragmented records from ERP, CRM, PSA, warehouse, and event logs, then maps them into clean structured fields.
Output: clean operational data ready for analysis and replay.AGX analyzes agent execution traces to find failure patterns. LLMs draft candidate guardrails, then AGX replays those rules against historical data, rejects weak rules that create false positives, and tunes thresholds.
Output: validated safety policies, or rejected drafts.AGX sits directly in the critical path of your existing workflow. Before an agent takes action, AGX evaluates the case against active guardrails and returns a deterministic decision: allow, block, needs_evidence, or review_required. Degradation behavior is explicit: fail open, fail closed, or force review.
Output: real-time governance for Temporal, ServiceNow, SAP, or similar systems.Once a policy is approved, agents operate strictly inside the control boundary. They can prepare review packets, request missing evidence, route exceptions, and update workflow state, but only when AGX has cleared the action.
Output: autonomous work, safely contained.The architecture separates expensive evidence work from the millisecond runtime decision. That keeps analysis rigorous and enforcement simple.
Discovers process signals, reconstructs workflow paths, tests bounded controls against history, and runs shadow pilots when the downstream path is uncertain.
Approved control bundle, evidence packet, and rollout decision.Loads the approved control bundle, receives prefetched evidence and a live event, then returns a deterministic pre-action decision without calling models or source systems.
allow, needs_evidence, review_required, or block.A real recovery path is rarely one rule. Billing leakage, for example, requires evidence completeness, rate-card checks, scope recovery, approval state, and finance authority to work together. AGX tests the entire governed path.
AGX turns these pieces into one governed Billing Readiness Gate.
AGX checks whether the gate reduces leakage without creating unmanageable review burden or slowing down cycle times.
The agent can monitor, package, and follow up because AGX has already tested the combined controls and enforcement has authorized the next step.
Most systems track what worked. AGX treats rejected policies as first-class assets because knowing what does not work prevents the engine from repeating mistakes.
Every failed candidate records exactly why it failed: increased cycle time, policy violation, weak evidence, bad cohort fit, or too many false positives. Those failures become reusable constraints for the next search.
AGX keeps LLM work separate from guardrail validation, replay, statistical testing, and inline enforcement. AI drafts and explains; deterministic systems decide what is allowed.
Translates unstructured logs and stakeholder frames into structured safety policies for the engine to test.
Drafts owner questions, classifies replies, and prepares packets only inside an enforced guardrail.
Links decisions to source evidence, replay results, rejected policies, and audit records.
Uses symbolic logic, statistical testing, and state machines to decide whether a guardrail can be promoted or enforced.
AGX downgrades, blocks, asks for evidence, or sends the guardrail back for revalidation.
AGX cannot claim leakage, risk, or improvement unless source records map cleanly to the data schema and pass quality checks.
Every generated policy must name the operating decision it changes and the accountable human owner.
An agent may execute only when policy, role, approval, and system boundaries validate the action.
A guardrail cannot move from sandbox to live enforcement unless it survives historical replay and live canary testing with measurable outcome improvement.
Evidence state, guardrail logic, policy result, owner decision, agent work, and final outcome must be reconstructable for audit.