ACPT reconstructs what enterprise AI agents did, verifies the business outcome against trusted records, evaluates whether defined company and regulatory guardrails were followed, detects material deviations or policy violations, and preserves the evidence needed for investigation and audit.
Observed payment conflicts with the approved source record. Likely amount extraction deviation before payment creation. Recommended action: suspend the workflow and review the transaction.
Traditional observability can tell you that a model responded, a workflow completed or an API returned 200. It does not prove that the resulting business action was correct or that the agent stayed within the rules it was supposed to follow. ACPT closes that gap by independently checking expected vs observed outcomes and evaluating defined guardrails.
Reconstruct the complete run across models, tools, APIs, workflows and business systems.
Compare expected state with observed destination state using explicit outcome contracts.
Preserve the provenance chain linking source evidence, actions, policies, incidents and human decisions.
Evaluate each consequential action against defined company policies, approval thresholds and applicable regulatory requirements.
The first product is deliberately narrow. ACPT reconstructs the agent run, resolves the relevant business record, derives the expected outcome, captures the observed destination state, compares the two, evaluates defined guardrails, and preserves the evidence needed to understand any deviation or policy violation.
Ingest telemetry, tool calls, middleware and business events, then normalize them into one coherent timeline of what the agent actually did.
Resolve the business record that should determine the outcome: purchase order, approved invoice, ERP record, contract or human-approved rule.
Derive the expected business result independently, capture the observed destination state, and compare the two deterministically.
Turn material mismatches into structured incidents with the amount, constraint, systems touched and exact point of divergence.
Check consequential actions against defined company policies, approval rules, operational boundaries and mapped regulatory requirements.
Preserve source record, agent run, action, destination result and timestamps so a reviewer can understand the incident quickly.
The current milestone is one convincing end-to-end assurance loop against a consequential agent workflow. ACPT verifies the business outcome and evaluates a small set of defined guardrails. It is not claiming to certify regulatory compliance, and it is not optimizing for universal rollback, autonomous remediation or dozens of integrations.
Ground truth, run reconstruction, expected vs observed, guardrail evaluation, deterministic violation detection, incident generation and evidence.
Build nowEvidence correlation, root-cause assistance and human investigation workflows after the assurance loop is credible.
After proofApproval, containment, remediation, reversible correction and post-recovery verification, earned through customer demand.
Not the wedgeACPT derives or receives the expected business outcome from a trusted source independent of the agent, then evaluates the action against the relevant policy context. That can include a purchase order, approved invoice, ERP record, contract, approval threshold or human-approved business rule.
Example: an invoice agent is expected to create a payment for EUR 12,400. The payment API succeeds, but the destination system shows EUR 124,000. Technical status: success. ACPT assurance status: failed.
Each incident links the authoritative source record, reconstructed agent run, business action, destination result, applicable guardrails and timestamps so a reviewer can understand the mismatch or policy violation without trusting the agent’s own explanation.
Any team running agents against consequential business workflows — finance, operations, procurement, customer systems — where a wrong automated action creates real financial, operational or governance cost and the expected outcome or policy boundary can be independently verified.
Assurance across agents, models, workflows and downstream systems.
Verify consequential outcomes before errors become expensive cleanup.
Continuous evidence showing whether autonomous actions stayed within defined policies and mapped regulatory guardrails.
Understand agent permissions, boundaries, incidents and control points.
The first design-partner cycle is intentionally narrow. We connect one agent workflow, identify the record that should determine the result, define the small set of policies or approval boundaries that matter, reconstruct the run, compare expected and observed outcomes, evaluate guardrail adherence and prove the evidence loop end to end.
Start with one high-impact AI agent workflow — invoice/AP, procurement, refunds, or another consequential process — where an incorrect automated action would create real financial or operational cost.
Connect the purchase order, contract, ERP record, policy or approved rule ACPT can use to derive the expected outcome independently of the agent.
Reconstruct the run, capture the destination result, detect the mismatch, open an incident and preserve enough evidence for a reviewer to understand it in under 60 seconds.
ACPT is continuous assurance infrastructure for enterprise AI agents, across whichever consequential workflows an enterprise runs them against. Each design-partner engagement proves the assurance pattern on one workflow first; the same architecture extends across other agent workflows and later into investigation, control and verified recovery.
If an AI agent can create, approve or influence a consequential business action, we want to understand how your team verifies the result and checks policy boundaries today. We'll start with one workflow, connect the relevant business record, define the critical guardrails and test whether ACPT can prove an outcome deviation or policy violation end to end.