AI agents may outlive their creators. Ownership, knowledge, and accountability must endure.
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What happens when the person who built an AI agent is no longer available to support it?
Imagine an organization deploying an AI agent to automate SharePoint permission attestations.
The agent coordinates access reviews, follows up on pending approvals, and generates compliance reports. Everything works as expected, and over time, the organization starts depending on it.
Then the engineer who developed it leaves. Perhaps the engineer is an employee, or perhaps they work for a supplier whose contract is ending.
The agent continues to exist. But who understands its architecture, integrations, business rules, and recovery procedures?
Can another engineer take over without depending on the original developer?
This is where AI agent ownership becomes a digital succession challenge.
In my earlier article, Who Owns an AI Agent After It Goes Live?, I explored accountability after deployment.
Here, I want to look at something closely related but different: how organizations retain the knowledge and operational control behind the AI assets they create.
The offboarding risk we may be overlooking
Employee offboarding is familiar territory for most IT organizations. Accounts are disabled, access is revoked, and responsibilities are transferred.
But AI agents introduce dependencies that may not be immediately visible.
An agent could rely on Power Automate workflows, APIs, connectors, knowledge sources, service identities, and configuration decisions made by one engineer.
Microsoft already documents how Copilot Studio agents can become orphaned when owners leave, and how Power Automate flows may fail when they depend on connections associated with departed employees.
Microsoft's agent registry can help identify agents without owners, while Agent Builder supports multiple owners to reduce reliance on a single individual.
These are useful capabilities. But identifying an orphaned agent or assigning a new owner is only part of the process.
Transferring ownership does not automatically transfer operational knowledge.
There are security considerations too. Depending on assigned privileges, a new owner may gain access to existing agent transcripts. That makes permissions and data-access reviews an important part of succession.
The AI Asset Passport: Keeping knowledge within the organization
I believe every production AI agent should have a simple, maintained record of what it does, how it works, and who can support it.
I would call this an AI Asset Passport.
It is a proposed governance practice, not an official Microsoft framework.
| Area | What should be captured |
|---|---|
| Identity | Purpose, environment, business criticality |
| Ownership | Business owner, technical custodian, backup owner, supplier contacts |
| Dependencies | Workflows, APIs, connectors, knowledge sources |
| Security | Identities, permissions, data sensitivity |
| Operations | Monitoring, troubleshooting, recovery procedures |
| Knowledge | Architecture, source repositories, runbooks, change history |
| Succession | Successor details, handover evidence, validation date |
The passport doesn't need to become another lengthy document.
It could be a lightweight record linking to existing CMDB entries, SharePoint repositories, source-control systems, and operational documentation.
What matters is that these records remain accessible to authorized teams, version-controlled where appropriate, and regularly updated.
Knowledge retention cannot be a one-time activity performed during deployment or offboarding.
Six practical controls for digital succession
I would build succession into existing enterprise service management and AI governance practices through six controls:
Accountable ownership: Assign a business owner, technical custodian, and successor.
AI Asset Passport: Maintain an inventory of purpose, dependencies, ownership, and operational references.
Knowledge continuity: Preserve architecture, configurations, runbooks, and change history in enterprise-controlled repositories.
Supplier succession: Establish backup resources, knowledge-transfer obligations, and exit procedures.
Operational validation: Verify that another authorized engineer can troubleshoot, modify, and recover the agent.
Continuous assurance: Periodically review ownership, documentation freshness, dependencies, and succession readiness.
These controls are not intended to introduce bureaucracy. They are practical measures to prevent operational knowledge from disappearing when people change.
This approach also aligns with NIST's AI Risk Management Framework, which addresses AI system inventories (Govern 1.6), defined roles and responsibilities (Govern 2.1), safe decommissioning (Govern 1.7), and third-party risk management (Govern 6).
A practical example: SharePoint access attestation
Consider an organization that engages a supplier to develop an AI-assisted SharePoint permission attestation solution.
The solution uses an agent to coordinate reviews, Power Automate to orchestrate workflows, and approved Microsoft Graph integrations to retrieve relevant information.
A supplier engineer develops and supports the solution.
A few months later, that engineer is reassigned to another project.
The organization may have the agent, workflows, and necessary licenses, but the detailed knowledge of exception handling, approval logic, or technical dependencies may sit primarily with that engineer.
This does not mean the agent automatically stops working. The risk is that troubleshooting, maintenance, or future changes become difficult.
With a succession plan, the transition looks different.
The replacement engineer can access the AI Asset Passport, review current architecture and runbooks, validate workflow connections, and execute a controlled attestation test.
The service owner confirms that approvals, exceptions, reporting, and monitoring work before accepting the handover.
The organization has not simply reassigned ownership. It has demonstrated continuity.
Supplier dependency: The challenge deserves more attention
This is where I see a broader operational concern.
Across enterprise IT, knowledge transfer has always been a challenge, particularly in supplier-managed environments.
From my experience in enterprise service ownership and supplier management, I've seen how resource transitions, knowledge gaps, and unclear accountability can introduce operational risks, even when the underlying technology is working well. As AI agents become part of business operations, these challenges deserve attention from the outset.
People move between projects, contracts change, critical resources resign, and sometimes supplier teams are reorganized with very little notice. Often, the gaps become visible only when an incident occurs or a replacement engineer needs to make a change.
AI agents could make this challenge more significant.
A supplier may develop multiple agents across HR, Finance, Procurement, and IT. Each agent may contain business rules, prompts, workflows, integrations, and configuration decisions that are not obvious from the user interface.
If that knowledge stays with a few supplier engineers, the organization becomes dependent on individuals rather than on a sustainable operating model.
I believe supplier AI exit readiness should become part of vendor governance from the beginning of an engagement.
That means establishing a few expectations:
- Enterprise-controlled knowledge: Architecture, configuration references, runbooks, test evidence, and change records should be maintained in approved central repositories, accessible to authorized customer teams.
- Continuous knowledge transfer: Suppliers should update documentation as solutions evolve, with periodic walkthroughs and reviews rather than a last-minute handover.
- Resource succession: Critical agents should have identified primary and backup support resources, alongside an accountable internal custodian.
- Transition readiness: Changes in supplier personnel should trigger dependency checks, knowledge validation, and formal service-owner acceptance.
- Contractual clarity: Deliverables, intellectual-property rights, transition assistance, and access revocation should be addressed in supplier agreements.
For proprietary solutions, organizations may not always receive source code or full control over the underlying technology. In those cases, data portability, configuration exports, and documented transition procedures become important.
The goal is not to eliminate suppliers or reduce their contribution.
It is to ensure that the knowledge needed to operate enterprise AI assets remains available to the organization, regardless of who delivers the service.
Measuring whether succession really works
I would keep the measurements simple.
Are production agents assigned accountable owners and successors? Is their documentation current? Have supplier-managed agents completed knowledge-transfer reviews?
And perhaps most importantly, how long would it take an authorized replacement engineer to take over and demonstrate that the agent can be operated safely?
For business-critical agents, I would introduce a periodic succession test:
Can someone other than the original creator support this agent using the knowledge, permissions, and tools the organization has retained?
If not, the handover is not complete, regardless of how many documents have been signed off.
From Governance Principles to Practical Action
Understanding digital succession is only the beginning. Organizations also need repeatable processes to manage ownership, access, knowledge transfer, operational continuity, and lifecycle changes.
To help translate these principles into practice, I've developed the Digital Workplace & AI Lifecycle Toolkit—an interactive resource covering AI agent governance, employee onboarding and off-boarding, Microsoft 365 services, supplier transitions, device lifecycle, and continuous improvement.
From AI ownership to long-term stewardship
Digital succession should become part of existing offboarding, IAM, change management, service ownership, and supplier governance processes.
As organizations deploy more agents, they will inevitably face employee exits, supplier transitions, platform changes, and changing business requirements.
We should be thinking about these situations while designing and deploying agents, not after losing the people who understand them.
The number of agents deployed may demonstrate the pace of AI adoption.
But the ability to maintain those agents independently, securely, and consistently will demonstrate operational maturity.
An AI agent should never become an operational mystery simply because the person who built it has moved on.
References
- Microsoft — Reassign ownership of orphaned Copilot Studio agents
- Microsoft — Manage orphaned Power Automate flows
- Microsoft — Governance and lifecycle actions for agents available in Microsoft 365 admin center
- NIST — AI Risk Management Framework
Author's note: The AI Asset Passport and six digital succession controls are proposed enterprise governance practices, not official Microsoft or NIST frameworks. The SharePoint attestation example is illustrative and does not represent a specific organization.
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