ACPT reconstructs what enterprise AI agents did, derives the expected result from trusted business records, compares it with what actually occurred, detects material deviations 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. ACPT closes that gap by independently checking what should have happened against what actually happened.
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.
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 and preserves the evidence needed to understand any deviation.
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.
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 is not optimizing for universal rollback, autonomous remediation, dozens of integrations or a broad compliance product.
Ground truth, run reconstruction, expected vs observed, deterministic deviation 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 — a purchase order, approved invoice, ERP record, contract or human-approved business rule, depending on the workflow.
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 and timestamps so a reviewer can understand the mismatch 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 compliance cost and the expected outcome can be independently verified.
Assurance across agents, models, workflows and downstream systems.
Verify consequential outcomes before errors become expensive cleanup.
Evidence, policy context and accountability for autonomous actions.
Understand agent permissions, boundaries, incidents and control points.
The first design-partner cycle is intentionally narrow, whichever consequential workflow we start with. We connect one agent workflow, identify the record that should determine the result, reconstruct the run, compare expected and observed outcomes, seed or observe a material mismatch 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 today. We'll start with one workflow, connect the relevant business record and test whether ACPT can prove a material mismatch end to end.