TL;DR
AI agent lifecycle management is the practice of running every AI agent through seven controlled stages, from design to retirement. The stages are design, build, test, deploy, monitor, improve and retire. Each stage has a named owner, a required record and an exit test before the agent moves on. A central registry keeps one entry per agent, covering its owner, purpose, versions, permissions and status. A model swap, a prompt rewrite, a new tool, a wider data scope or detected drift all trigger re-evaluation and, when risk rises, fresh approval. Retirement is a formal stage too, so credentials are revoked, memory is purged, audit records are kept and the registry entry is closed.
Key Takeaways AI agent lifecycle management covers seven stages, namely design, build, test, deploy, monitor, improve and retire. Every agent needs one registry entry with a business owner, an approved purpose, pinned versions and a current status. A RACI matrix gives each lifecycle decision exactly one accountable person, which prevents orphaned agents. Release manifests pin every component of a version, and a trigger table says which changes need fresh approval. Drift in models, data, tools or permissions should start a review that ends in continue, update, suspend or retire. Retirement is planned work that revokes access, purges memory, keeps audit records and closes the registry entry. Watch on YouTube
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What Is AI Agent Lifecycle Management?
AI agent lifecycle management is the discipline of running an AI agent from its first design note to its final shutdown. It then gives every stage in between an owner, a record and an exit test. The seven stages are design, build, test, deploy, monitor, improve and retire.
The definition matters because agents keep working long after launch. They call tools, read company data and act under a service identity for months, so their risk changes even when the code stays the same. A model update, a new API or a wider permission can change what an agent does on an ordinary Tuesday.
Classic release management loses interest once software ships, while agents need the opposite. Microsoft’s own agent development lifecycle runs through five phases and ends in an operational steady state, with no separate retirement phase. Lifecycle management therefore adds that missing end, along with the change control that keeps every version accountable.
Standards bodies also point the same way. The NIST AI Risk Management Framework core expects organizations to inventory their AI systems and to have procedures for decommissioning them safely. Similarly, ISO/IEC 42001 sets requirements for an AI management system that is maintained and continually improved, which is lifecycle thinking applied to the whole company.
Lifecycle Management vs AgentOps vs MLOps
Lifecycle management defines the stages, decisions and control points. AgentOps, on the other hand, is the daily operating practice that runs them, from release pipelines to on-call reviews. MLOps, by contrast, manages models and features, which is only one part of what an agent depends on.
Teams that already run MLOps on Databricks have a head start, since model registries and approval flows carry over. Even so, an agent adds prompts, tools, memory and permissions that a model registry never tracks. Table 1 shows where the three practices differ in practice.
Table 1: DevOps vs MLOps vs AgentOps
Dimension DevOps MLOps AgentOps
Main asset Applications and services Models and pipelines Agents and everything they depend on What changes Code and configuration Models, features, training data Prompts, models, tools, memory, permissions Release question Is the software stable? Is the model accurate enough? Does the agent act within approved limits? Main operating risk Outages and defects Model and data drift Wrong actions, tool failures, permission creep How it ends Service shutdown Model archived Access revoked, memory purged, records kept
Where Identity Governance Fits
Identity vendors often frame the agent lifecycle as an access lifecycle. That view is correct, but only as far as it goes. After all, every agent needs its own identity, scoped permissions and a clean revocation path. Products such as Microsoft Entra Agent ID now apply lifecycle controls to agent identities. Entra also logs every agent sign-in and action, which helps a great deal at the retire stage.
Access is one slice, though. An agent with perfect credentials can still run a stale prompt, call a changed API or answer from outdated data. So this guide treats identity as one lane inside the wider lifecycle, and the guide to agentic AI security covers the access controls in depth.
The Seven Stages of the AI Agent Lifecycle
Each stage below ends with an exit test, which is a short list of evidence that must exist before the agent moves on. Exit tests keep handoffs honest, because a stage cannot close on a verbal okay. Monitor and improve repeat for as long as the agent runs, so together they form a loop rather than a straight line.
Table 2: The Seven Stages With Owners, Records and Exit Tests
Stage Primary owner Records produced Exit test
1. Design Business owner with the AI lead Purpose, scope, risk tier, success measures Purpose, scope and owner approved before build 2. Build Agent engineering lead Versioned prompts, tools, model settings, dependencies Build complete, so every part has a version 3. Test QA lead with a risk reviewer Test evidence, known limits, fix log Checks passed, then residual risk accepted 4. Deploy AgentOps or platform lead Release manifest, approval record, rollback plan Release registered before traffic arrives 5. Monitor AgentOps with the business owner Health reports, incidents, review notes Review held, then issues assigned 6. Improve Engineering with the business owner Change requests, re-evaluation results Change released only when approved and tested 7. Retire Business owner with AgentOps Retirement approval, revocation and archive records Entry closed once the agent is disabled
1. Design
Design starts with a plain statement of the job, before any code exists. What task will the agent do, for whom, with which data and which actions? The business owner signs off on that scope. Then the AI lead assigns a first risk tier, based on the systems the agent can touch.
Also write down what the agent must never do. Those limits become test cases during the test stage and approval triggers after launch. They also give reviewers a fixed reference when someone later asks for more scope.
2. Build
Build turns the design into a working agent, and everything it depends on gets a version number. That includes the system prompt, the model and its settings, each tool definition, the retrieval setup and the memory configuration. Tool connections built on MCP need versions too, because a server update changes what the agent can call. The AI agent architecture guide explains those components, while how to build AI agents covers the build work itself.
3. Test
Testing then proves the agent does the task inside its approved limits. Functional checks, vulnerability assessments and behavior tests all produce recorded evidence, and a named reviewer signs off on any risk that remains. The methods belong in our guide to agentic AI testing , and AI agent evaluation covers the metrics.
4. Deploy
Deployment is a gated release, never a copy and paste into production. The release gets a manifest, an approval reference and a rollback target, and AgentOps registers it before any user traffic arrives. Before go-live, run the agent in shadow mode for a short period, so its outputs can be compared with the current process. Then switch on write actions one tool at a time, starting with the least risky. Hosting and scaling choices sit in the post on AI agent infrastructure .
5. Monitor
Once live, the agent needs an owner who actually reads its signals. For example, failed tool calls, rising cost per task, odd outputs and user complaints all land in a review log. When a signal crosses its threshold, the owner opens a review instead of patching the agent in place. The tracing and alerting stack is covered in AI agent observability , so this stage focuses on who acts and when. Pair those traces with data observability on the sources the agent reads, since bad inputs often look like agent errors.
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6. Improve
Improvements come from incidents, user feedback and new business needs. Each change follows the same path as the first release, which means versioning, a targeted re-test and the right level of approval. The re-approval table later in this guide sets that level for each type of change.
7. Retire
Every agent eventually stops earning its keep. Retirement shuts it down on purpose, with access revoked, records kept and dependent teams told. Because so few teams plan for this stage, it gets its own section below.
Why Every Agent Needs a Registry Entry
A registry is the system of record for every agent the company runs, whether built in-house, bought or switched on inside a SaaS app. NIST lists an AI inventory as an expected governance practice, since lifecycle management cannot work without one. After all, an agent missing from the registry cannot be reviewed, changed safely or retired.
Buyers often ask which platform should handle this work. Whatever tool holds the record, it needs to track ownership, versions, access and status for agents from every vendor. A registry that only sees one platform leaves the rest of the estate unmanaged. Since bought and embedded agents rarely expose their internals, record at least the vendor, the version and the data they can reach. Otherwise, the first audit question about a SaaS agent has no answer.
Fields Every Registry Entry Needs
Keep each entry short, so that owners will actually maintain it. The list below covers the fields that matter most, grouped by the question each one answers. Anything beyond it should earn its place by supporting a real review or audit.
Identity covers the agent ID, name and description, so anyone can tell which agent this is.Ownership names the business owner, technical owner and support team, because someone must answer for it.Purpose records the approved task, business unit and scope limits, so the reason it exists stays clear.Risk holds the risk tier, data sensitivity and autonomy level, which then set how closely it is reviewed.Configuration lists the agent release, model version and prompt version, so you always know what is running.Dependencies track tools, APIs, data sources and other agents, since each one can break or change.Access shows the service identity, permission scope and credential references, so reviews know what it can do.Status keeps the lifecycle status plus the last and next review dates, so nothing goes unreviewed.Closure stores the retirement date, archive location and closure evidence once the agent is shut down.
Lifecycle Statuses and Shadow Agents
Statuses also give every team the same vocabulary. A simple set works well, namely draft, in test, approved, active, suspended and retired. Only the owner or AgentOps should move an agent between them, and every move gets a date and a reason.
Even so, shadow agents are the registry’s biggest enemy. Business teams can now build agents in low-code tools in an afternoon. Also, adding a new AI agent skill can widen what an existing agent does overnight. So reconcile the registry every month against identity logs, gateway traffic and platform admin consoles, then flag anything running without an entry.
Who Owns an AI Agent? A RACI Matrix by Lifecycle Stage
An agent touches the work of several teams, so it needs one accountable owner and clear roles for everyone else. To begin with, the business owner answers for value and scope. Meanwhile, engineering owns correctness, AgentOps owns operations, and security and risk own the controls in between.
A RACI matrix then makes those roles explicit. In Table 3, R means responsible for doing the work, A means accountable for the decision, C means consulted and I means informed. Each row gets exactly one A, with no exceptions.
Table 3: RACI Matrix for AI Agent Lifecycle Decisions
Lifecycle decision Business owner Engineering AgentOps Security and risk
Approve design and scope A C C C Register the agent A C R I Build and version I A, R C C Approve production release A R R C Run monitoring reviews C C A, R I Respond to incidents I R A C Approve material changes A R C C Suspend an agent A I R C Approve retirement A C R C Confirm closure evidence I C R A
One rule saves a lot of pain later. If a row has no single A, the decision either stalls or happens with nobody owning it. Security and risk hold the final A on closure on purpose, because they defend the evidence in an audit and in any AI compliance review.
When an Owner Leaves
People change roles faster than agents retire, especially in large companies. Tie the owner field to the HR system or the identity directory, so a departure or transfer opens a reassignment task automatically. Give that task a short deadline, such as ten working days. Meanwhile, the departing owner should hand over open incidents and pending changes, so the successor starts with the full picture.
If nobody accepts ownership by then, suspend the agent. An agent without an accountable owner is an orphan, and orphaned agents keep their credentials and keep acting while no one reads the logs. Suspension keeps the record intact, so a new owner can pick it up cleanly.
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Kanerika covers risk tiering, post-change audits after model updates and a model registry with a full audit trail, so every AI agent stays governed from design to retirement.
Explore AI Governance Versioning, Release Manifests and Re-Approval Triggers
An agent’s behavior comes from several parts at once. The prompt, model, tool schemas, retrieval sources, memory settings and permissions all shape what it does. If you version only the prompt, a tool schema change can alter behavior with no record anywhere. The same goes for context, which is why context engineering settings belong in the version history as well.
What a Release Manifest Records
A release manifest, in short, pins every component of one production version in a single file. Months later, anyone can then see exactly what was running when a given decision was made. Here is a compact example for an invoice-matching agent in accounts payable.
agent_id: ap-invoice-matcher
release: 2.4.0
status: active
owners:
business: finance-ops-lead
technical: ap-agent-engineering
model:
model_id: pinned-model-id
temperature: 0.1
prompt_version: system-prompt-v14
tools:
- erp.read_invoice: v3
- erp.post_match: v2 # write action, high risk
retrieval_index: vendor-contracts-v7
memory: session-only, no long-term store
permission_scope: ap-agent-prod-role
risk_tier: high
evaluation_ref: eval-run-8812
approval_ref: change-board-1193
rollback_target: 2.3.2Store the manifest with the code and link it from the registry entry. Rollback then becomes a known target, not a guess at 2 a.m. It also means an auditor can trace any action back to one approved configuration, which is the core of any AI auditing framework .
Which Changes Force Re-Approval
Small edits should not wait for a committee, yet some changes alter what the agent can do or decide. Those need fresh evidence and, when risk rises, a fresh sign-off. Table 4 sets sensible default rules.
Table 4: Changes That Trigger Re-Evaluation and Re-Approval
Change Re-evaluation required Re-approval needed
Prompt wording fix Targeted behavior check When instructions or limits change in substance Model swap or version change Full regression on the evaluation set Always for high-risk agents, otherwise when scores drop New tool or API Integration and action-scope tests When the tool can write, pay, send or delete Wider data scope Access and data-use review When sensitive data or a new data owner is added More autonomy Behavior and operational-risk review When any human approval step is removed New business use Fit-for-purpose review When the task differs from the approved purpose Dependency or library update Compatibility checks When behavior or cost moves outside agreed limits
When the Model Swap Is Not Your Choice
Model swaps deserve special care, because they are not always your choice. Providers retire older models on a published schedule, and Anthropic’s model deprecation page states that requests to retired models fail. Plan each forced swap as a full release, with the regression run finished well before the cutoff. Similarly, keep the evaluation set itself under version control, because a changed test set can hide a real regression.
The tool and autonomy rows, in particular, map to a well-known risk. OWASP lists excessive agency among its top risks for LLM applications, with excessive functionality, permissions and autonomy as the usual causes. Before an agent loses a human approval step, check the change against the patterns in human-in-the-loop AI agents .
Rollback and Emergency Changes
Still, some fixes cannot wait for the full path. Allow emergency changes, but require a named approver, a manifest entry and a follow-up review within a set window, such as five working days. If that review fails, roll back to the last approved manifest.
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Read the Case Study → Drift-Triggered Re-Evaluation After Deployment
Agents drift even when nobody ships a change. The world around them moves, and the approved behavior slowly stops matching what the agent actually does. NIST’s Manage 4.1 asks for post-deployment monitoring plans that include change management and decommissioning, which is exactly this loop.
Five Kinds of Agent Drift
In practice, production agents drift in one of five ways. Each one needs its own signal, because a single accuracy score rarely catches them all.
Model drift happens when a provider updates or retires the model, so answers shift in tone, accuracy or format.Knowledge drift sets in when source documents age, so retrieval returns facts that were true last quarter.Tool drift appears when an API changes its schema or error codes, so tool calls start to fail or misfire.Permission drift builds up when access grows through inherited roles or new connectors beyond the approved scope.Context drift follows when users, volumes or business rules change, so the original task no longer fits.
Knowledge drift is the hardest of the five to spot in retrieval-heavy agents. Teams running agentic RAG should therefore track source freshness as a first-class signal, next to error rates and cost. For example, a contract-review agent can keep scoring well on its test set while the templates it reads change underneath it. In that case, only a freshness check on the source library catches the problem.
Scheduled Reviews vs Event Triggers
Good programs use both. Scheduled reviews run on a cadence set by the risk tier, for example quarterly for high-risk agents and twice a year for low-risk ones. Event triggers, by contrast, start a review at once, whatever the calendar says.
Typical triggers include a failed evaluation run, a cost or error spike and a provider deprecation notice. Security findings and a change in the owning team also count. Each trigger should open a ticket against the registry entry, so nothing depends on someone’s memory.
Continue, Update, Suspend or Retire
Every review ends in one of four decisions. Continue when the agent still meets its purpose and limits. Update when a fix through the normal change path can restore it.
Suspend when the risk is live and the fix is not ready, which keeps the record but stops execution. Retire when the purpose is gone, a better agent replaces it or the fix costs more than the value. Then record the decision, the reason and the date in the registry, so the next audit has the full story.
How to Retire and Decommission an AI Agent
Retirement is the stage most often left out, and it is where risk lingers longest. A retired agent that still holds a token can be called by a forgotten scheduler or misused by an attacker. NIST’s Govern 1.7 asks for procedures that phase out AI systems safely, without raising risk.
When an Agent Should Retire
Common triggers are low use, a duplicate agent doing the same job, a retired business process and an unsupported model or integration. Unresolved risk findings and the loss of an accountable owner belong on the list too. Any one of them should prompt the question, and two or more usually mean the answer is yes. When the call is close, suspend first and retire later, since suspension is easy to reverse.
Five Steps to a Clean Shutdown
The order of these steps matters as much as the steps themselves. Work through them in sequence, and record each one against the registry entry.
Approve and map dependencies first, so you record the reason, name a closure owner, list every user, workflow and system that calls the agent, and set a cutover date.Notify and transfer before anything stops, which means you tell users and downstream owners, move any needed functions to a replacement and confirm nothing still depends on the agent.Stop execution and revoke access once dependents are ready, so you disable schedules, triggers and endpoints, then revoke tokens, keys, service identities and tool permissions.Archive, purge and retain by keeping audit logs, manifests and approvals for the retention period, while deleting memory, caches and vector data that no longer need to be held.Verify and close only when no calls succeed, no credentials remain and no paid resources still run, then set the registry status to retired.
Sequence errors, though, cause most of the damage here. Revoking credentials before notifying dependents breaks live workflows without warning. Likewise, deleting logs before archiving them destroys the audit trail you will need later.
Retirement Checklist
Before the registry entry closes, the closure owner should confirm every item below and attach the evidence.
Retirement reason and business approval recorded
Downstream users and systems notified, functions transferred
Schedules, triggers and endpoints disabled
Credentials, tokens, keys and service identity revoked
Tool and data permissions removed
Agent memory, caches and embeddings purged under retention rules
Audit logs, manifests and approvals archived for the retention period
Hosting, licenses and paid resources released
Registry status set to retired, with closure evidence attached
Run one final check a week later. Search gateway and identity logs for any call that still names the agent, since a stray call means something was missed.
The AI Agent Lifecycle Management Maturity Model
Use this maturity model to locate where your program stands and to pick the single next priority. Each level builds on the one before it, so skipping a level rarely sticks. Table 5 lists the five levels.
Table 5: Five Levels of AI Agent Lifecycle Maturity
Level Name What it looks like Next priority
1 Ad hoc Agents built by separate teams, few records, unclear owners Build the inventory first 2 Registered Central registry with owners and statuses Define stage gates and exit tests 3 Controlled Versioning, release manifests and a retirement procedure in use Add drift triggers and scheduled reviews 4 Managed Reviews, change triggers and lifecycle reporting run on schedule Automate registry updates and checks 5 Optimized Registry, approvals and revocation mostly automated and tuned from evidence Refine controls using incident and review data
To score yourself, check five things, namely inventory coverage, owner coverage, release control, review completion and retirement readiness. Your level is the lowest of the five, because one weak area drags the whole program down. Our AI maturity model applies the same idea to AI programs as a whole.
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Start Your AI Assessment → A program at level one can usually reach level three without new tools, because the first three levels are mainly about records and owners. After that, automation starts to pay off, since manual registry updates stop scaling once agent counts climb.
Six Lifecycle Mistakes That Create Orphaned Agents
The same failure patterns show up across agent programs. Each one is cheap to prevent, but expensive to clean up.
Agent sprawl happens when teams launch overlapping agents with no shared inventory, so costs and controls drift apart.Orphaned agents appear when an owner leaves and nobody inherits the agent, so it keeps running with live credentials.Treating launch as the finish line fails because, without post-release reviews, outdated dependencies and fading value go unnoticed.Untracked changes occur when a prompt or model edit ships without a manifest, which makes incidents hard to trace and rollback unreliable.One approval forever means the first sign-off is treated as valid for every later version, even after scope or autonomy grows.Half-retired agents are marked retired, but their tokens, schedules or stored memory stay active.
The post on AI agent challenges covers technical failures, while these six are process failures. Fixing them takes clear ownership far more than extra tooling.
How to Put AI Agent Lifecycle Management in Place
Start with the agents you already run, not the ones on the roadmap. The six steps below fall into three phases, which are establishing control, standardizing operations and keeping accountability.
Inventory existing agents first. Pull lists from every platform, identity directory and gateway, then flag agents with no owner or purpose.Define registry fields and statuses next. Agree on the minimum entry and the six statuses, then make registration mandatory before production.Assign owners and approval rights so that decisions never stall. Publish the RACI, including who may suspend an agent and who confirms closure.Add versioning and release gates before the next release. Require a manifest, test evidence and an approval reference for every production release.Set reviews and retirement procedures once releases are under control. Schedule risk-based reviews, wire up drift triggers and adopt the retirement checklist.Report lifecycle health every month, so leadership sees registered agents, missing owners, overdue reviews, unapproved changes and the retirement backlog.
Steps one to three usually come first, because nothing else works without a complete list and named owners. Teams that want a wider policy frame can then map these steps to an agentic AI governance model or a broader AI governance framework . For buyers comparing enterprise platforms, our guide to enterprise AI agents lists the controls each one should support.
Checklist
AI Governance Checklist
A practical checklist for the policies, ownership, reviews and controls that keep AI systems accountable from launch to retirement.
Get the Checklist → How Kanerika Runs the Agent Lifecycle
Kanerika builds and operates AI agents for enterprises, and the lifecycle above draws on that delivery work. Through our agentic AI services , each engagement moves through four steps. We pick the workflow, design the agent, then connect and test it before go-live. We then run and improve it with monitoring, audit logs and a review loop. Where agents sit on top of custom models, our MLOps consulting team runs the model side of the same pipeline.
Governance runs alongside delivery rather than after it. Our AI governance services cover risk tiering, post-change audits after model updates and a model registry with a full audit trail. Kanerika also holds ISO 27001 and SOC 2 Type II certifications, which shape how we handle credentials and logs.
The same release discipline shows up in our product engineering work. For an AI-powered DataOps platform, we built a CI/CD path from local through development, QA and production. We also added correlation-ID tracing with masking for sensitive data and fixed schema drift at the architecture level. The client saw 65% faster customer onboarding and a 37% cut in operational costs.
Agents in customer-facing roles need the same care after launch. For a luxury jewelry and apparel retailer, we deployed Karl as an AI stylist and shopping concierge across web and in-store kiosks. Add-to-cart rates rose 52%, and customer service workload fell 41%.
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Many clients already run more than one agent platform, so we start with one registry that spans all of them. We also agree on the re-approval table before the first release, so nobody has to argue about it during an incident.
Even so, three pitfalls come up again and again on our projects. Write access arrives before read-only behavior is proven, model swaps skip the regression run, and retired agents keep tokens nobody remembers. Each one is cheap to catch at a stage gate and costly to find in an incident review. For us, that early catch is what responsible AI looks like on an ordinary project day.
What AI Agent Lifecycle Management Comes Down To
AI agent lifecycle management comes down to a short list of habits. Run every agent through seven stages with an owner and exit test at each, keep one registry entry per agent and pin each release in a manifest.
Then re-evaluate on any material change or drift signal, and decide to continue, update, suspend or retire. Finally, retire agents on purpose, with credentials revoked, memory purged and audit records kept. Do that, and every agent in the estate has a known owner, a known version and a known end.
Frequently Asked Questions
What is AI agent lifecycle management?
AI agent lifecycle management is the practice of running an AI agent from design to retirement with a named owner, a required record and an exit test at every stage. The seven stages are design, build, test, deploy, monitor, improve and retire, and a central registry tracks each agent through them.
What are the seven stages of the AI agent lifecycle?
The stages are design, build, test, deploy, monitor, improve and retire. Design sets purpose and scope, build versions every component, test proves the agent stays within limits, and deploy releases it through an approval gate. Monitor and improve repeat while the agent runs, and retire shuts it down on purpose.
How is AgentOps different from MLOps?
MLOps manages machine learning models, features and training pipelines. AgentOps manages the whole agent, which adds prompts, tools, memory, permissions and multi-step actions on business systems. An agent can change behavior through a tool or permission change even when its model stays exactly the same.
What should an AI agent registry record?
Each registry entry should record the agent ID, business and technical owners, approved purpose, risk tier, pinned model and prompt versions, tools and data sources, service identity and permission scope, lifecycle status, review dates and, at the end, retirement and closure evidence.
Who owns an AI agent after deployment?
One named business owner stays accountable for the agent’s value and scope after launch. Engineering owns technical correctness, AgentOps runs monitoring and incident response, and security and risk review access, material changes and closure evidence. A RACI matrix with exactly one accountable person per decision keeps those roles clear.
Which changes should trigger re-approval of an AI agent?
Re-approval is usually needed for a model swap on a high-risk agent, a new tool that can write, pay, send or delete, wider access to sensitive data, a removed human approval step or a new business use. Smaller prompt fixes and dependency updates need targeted re-testing and re-approval only if behavior or risk changes.
What is drift in an AI agent?
Drift is the gap that grows between an agent’s approved behavior and what it actually does over time. It comes from model updates or retirements, aging source documents, changed APIs, permissions that grow beyond scope and shifts in users or business rules. Drift signals should trigger a review even when no release happened.
How do you decommission an AI agent safely?
Approve the retirement and map every dependency, then notify users and transfer needed functions. Next, disable schedules and endpoints and revoke tokens, keys and permissions. Archive audit logs and manifests, purge memory that no longer needs to be held, verify that no calls succeed and close the registry entry with evidence.
How can enterprises prevent orphaned AI agents?
Make registration mandatory before production, tie each owner field to the HR or identity directory, and open a reassignment task when an owner leaves. If nobody accepts ownership within a set deadline, suspend the agent. Monthly reconciliation against identity and gateway logs also finds agents running without an entry.
