Integration, Governance, and Evaluation # Define what the system can access, do, and prove. Design system boundaries, permissions, human oversight, evaluation, and operational responsibility around the specific role and risk of the AI system. Trust should come from clear answers, inspectable evidence, and honest limitations. [Review the technical approach ](https://www.aixccelerate.com/talk-to-us)[Talk to Us ](https://www.aixccelerate.com/talk-to-us) Control modelRole before authority Defined role Purpose · scope · owner Scoped authority Context · access · control Operating evidence Behavior · limits · decisions Human oversightEvaluationOwnership The evaluation framework ## Make the important decisions visible. Seven questions connect the business role to its architecture, controls, evidence, and accountable operating model. - 01 ### Responsibility What work should the system perform, and what remains human responsibility? - 02 ### Context Which data, knowledge, records, and interaction history does the role require? - 03 ### Access Which systems, tools, actions, and environments are permitted? - 04 ### Human control Where do people review, approve, override, pause, or intervene? - 05 ### Evaluation Which scenarios, measures, thresholds, and failures determine acceptability? - 06 ### Operation How are activity, quality, change, incidents, support, and improvement managed? - 07 ### Ownership Who controls the data, system, decisions, support, and intellectual property? Understand the system boundary ## Map what the AI system reads, writes, and changes. Integration begins with the workflow and responsibility. Every connection needs a business purpose, scoped authority, predictable behavior, and sufficient evidence. Approved context Data, knowledge, records, and events Sources, owners, sensitivity, transfer paths, storage, retention, correction, and provider use. Scoped AI role Identity · permissions · delegated authority · human controls Permitted action Reads, writes, changes, and delivery Actions, approvals, retries, duplicate prevention, attribution, and investigation. 01PurposeWhy this connection is necessary 02DataObjects, sources of truth, sensitivity, and retention 03AuthorityAuthentication, scopes, permissions, and approvals 04BehaviorReads, writes, triggers, retries, and duplicate prevention 05EvidenceAttribution, activity records, failures, and investigation Feasibility and scope must be verified for every integration. This page does not promise universal connectors or automatic inclusion of every system. People remain responsible where judgment matters ## Place human control at the consequential decision. Oversight should reflect the consequence, uncertainty, sensitivity, and operating risk of the action. Autonomy expands through evidence and can contract when conditions change. Control patterns 01Review before action 02Approval above a threshold 03Exception escalation 04Shadow mode 05Sampled review 06Immediate intervention Earned autonomy - ### 01Observe Analyze and recommend without acting - ### 02Assist Prepare outputs or limited steps for review - ### 03Act with approval Execute after explicit authorization - ### 04Act within boundaries Complete approved routine work and escalate exceptions - ### 05Coordinate broader work Manage larger workflows under defined monitoring and controls Only where evidence and approval support it Evaluation before and after launch ## Test before authority. Keep evaluating as the system changes. Pre-deployment testing cannot represent every production condition. Evaluation should create a feedback loop that can improve, restrict, pause, reconfigure, or expand the system. Before deployment Success criteria defined before testing Representative, difficult, ambiguous, and sensitive scenarios Quality, workflow, tool, permission, and human-control measures Failure, recovery, and prohibited-action tests Known limitations and untested conditions Bounded production authority Operate within approved role, permissions, controls, and acceptance evidence. In production Task and workflow completion Quality, exceptions, and escalations Human approval, override, and feedback Tool, permission, reliability, and latency behavior Knowledge, model, policy, and integration changes Changes, exceptions, and incidents trigger review and re-evaluation Operate the system responsibly ## Make change, failure, and ownership explicit before production. Governance continues through changes, ordinary exceptions, material incidents, support, and transition. The applicable responsibilities belong in the system design and agreement. 01 ### Change Treat instructions, knowledge, models, tools, permissions, and evaluation sets as production changes. 02 ### Exception Route ordinary uncertainty into the workflow with clear human authority and context. 03 ### Incident Define detection, containment, evidence, communication, recovery, and re-evaluation. 04 ### Ownership Decide where the system runs, who controls it, who operates it, and how transition works. Shared responsibility AI Xccelerate may be responsible for Accurate system description, contracted controls, relevant evidence, known limitations, agreed evaluation visibility, and in-scope escalation. The customer retains Purpose, accountable ownership, access approval, risk tolerance, customer-controlled systems, adoption, human review, and final business and production decisions. The actual division must be documented in a shared-responsibility matrix and the applicable agreement. Bring the hard questions early ## What must be true before your organization will let an AI system perform real work? Tell us about the systems, data, controls, review process, and evidence your use case requires. We’ll begin by defining the boundary and the questions the architecture must answer. [Review the technical approach ](https://www.aixccelerate.com/talk-to-us) --- **Canonical URL:** https://www.aixccelerate.com/integration-governance-evaluation