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HIPAA Compliance Checklist for 2025
AI agent management is the practice of discovering, owning, securing, monitoring, and retiring every AI agent an organization runs, across every platform it was built on. It treats each agent as a non-human identity with a named owner, scoped access, a tracked cost, and a defined end date, so IT can say which agents exist, what they can reach, and who stops them.
The volume is why this can't wait. Gartner expects Fortune 500 companies to run as many as 150,000 agents each by 2028, up from about 15 in 2025.
Gartner also predicts that more than 40% of agentic AI projects will be canceled by the end of 2027, with inadequate risk controls among the causes.
What Does AI Agent Management Involve?
Builder teams think about agents in terms of prompts, tools, and outputs. IT sees something closer to a new hire: an identity that holds credentials, reaches business systems, costs money every time it runs, and keeps working until someone switches it off.
AI agent management applies the identity and access controls IT already runs for people to that identity. In practice, it answers five questions for every agent:
These records are what IT reaches for during an access review, an audit, or an incident. Without them, every one of those starts with a scramble across consoles.
Which Agents Fall Under AI Agent Management?
An AI agent is software that acts on its own using credentials it holds, such as updating a record, sending a message, or calling another system. Agents enter an organization through four routes:
- Platforms teams build on, such as Microsoft Copilot Studio, Salesforce Agentforce, and ServiceNow
- SaaS tools you already license, where vendors now embed agents by default
- Employees building or connecting agents on their own, often through accounts IT never approved
- The service accounts, OAuth tokens, API keys, and MCP servers agents use to reach your data
Most IT teams can see the first route. The other three are where unowned agents pile up, so a useful inventory covers both the agents and the credentials that let them act.
How Is AI Agent Management Different From Orchestration, Observability, and Security?
The terms overlap, and vendors use them loosely. Each one answers a different question:
Ask a platform team how many agents run on Copilot Studio, and they can likely answer. Ask how many agents across the company can write to your CRM, and the answer needs data from several systems. AI agent management is the work of answering that second question.
Why Do IT and Security Teams Need to Manage AI Agents Now?
Agents join an identity estate that is already hard to govern. CyberArk's 2025 Identity Security Landscape found 82 machine identities for every human one, and 42% of machine identities hold privileged or sensitive access.
In the same survey of 2,600 cybersecurity decision-makers, 68% said their organizations lack identity security controls for AI. Three gaps explain why.
1. Native Tools See One Platform at a Time
Platform vendors are adding real controls. Microsoft Copilot Studio offers agent inventory capabilities, and ServiceNow's AI Control Tower adds governance for AI systems.
Each console still starts from its own platform. Most enterprises run agents on several, so no single native view is complete.
What IT should do: build one inventory that reads from every source, then compare it with the approved register instead of assuming any one console sees everything.
2. Agents Outlive the People Who Built Them
Offboarding removes a departing employee's access. An agent that employee built often has its own identity and credentials, so it keeps running.
Offboarding misses happen even with human access. When Treasure Data connected its SaaS stack to CloudEagle.ai, it immediately found 30 former employees still holding access across 75 apps. The agents employees build are rarely on that checklist at all.
What IT should do: link agent ownership to current employee records, and trigger an ownership review whenever someone leaves or changes roles.
Related read: why offboarding misses agents their creators leave behind.
3. Permissions Drift Upward
Pilots get broad access so they work quickly, and that access is seldom scoped back down. An agent built to summarize support tickets can end up able to edit or delete them.
What IT should do: grant the minimum access each task needs, and review it before production and whenever the agent's tools or connected systems change.
How Does the AI Agent Lifecycle Work, From Joiner to Leaver?
The AI agent lifecycle follows the joiner-mover-leaver model IT already runs for employees, with one change: every step has to work across platforms, not inside one console. It breaks into three stages and six steps:
Stage 1: Joiner
Step 1: Discover Existing Agents
- Pull agent identities from identity providers such as Microsoft Entra ID and Okta
- Add each agent platform's admin console and your connected SaaS apps
- Map the service accounts, API keys, and integrations behind each agent
- Record each agent's platform, purpose, status, creator, and connected resources
A cross-platform inventory puts agent identities next to the managed identities, API tokens, and OAuth apps behind them.
Step 2: Register New Agents Before Launch
- Add an intake gate, so agents are registered before they touch production data or sensitive systems
- Capture the business purpose, owner, requested permissions, connected apps, and expected cost
- Investigate anything discovered but unregistered, and confirm its owner and purpose before restricting or retiring it
Step 3: Assign an Owner and a Backup
- Name one accountable owner who understands why the agent exists and approves its access
- Add a backup for continuity when the owner is away or leaves
- Link both to your HR system, so departures and role changes trigger a review automatically
Stage 2: Mover
Step 4: Scope and Review Access
- Default to read-only access unless the agent must change data
- Require explicit approval for write access to systems of record
- Add agents to periodic access reviews; quarterly suits most production agents, and higher-risk ones need more
- Remove permissions an agent no longer needs, after checking they don't support a rare but legitimate task
Step 5: Monitor Activity and Spend
- Agent activity, covering actions taken, systems touched, and changes made
- Identity events, covering sign-ins, permission changes, and credential use
- Operating cost, covering token consumption by agent, app, or team
- Execution patterns, such as repeated failures, activity spikes, or loops that trigger excessive tool calls
Every logged action should trace back to an agent and its owner. Set budget alerts too, since usage-based pricing can turn one looping agent into a cost spike.
Per-team limits catch problems early. Printify set AI spend limits with automatic alerts and caught $30K in overspend before it reached an invoice. Treasure Data now traces 2.8M tokens a month to a specific user, project, or model.
Stage 3: Leaver
Step 6: Retire Agents and Revoke Access
Start a retirement review when an agent's task ends, its owner leaves, its access is no longer appropriate, or its behavior turns risky. The runbook covers five actions:
- Disable execution, so the agent can't start new actions
- Revoke its roles and access to connected apps
- Revoke or rotate its tokens, keys, and secrets
- Remove leftover integrations, schedules, and related resources
- Record the retirement date, reason, and completed steps in the inventory
Give the runbook a named approver, so stopping an agent takes minutes rather than a ticket queue. If revoking roles, rotating secrets, and deactivating the identity happen in three consoles, containment waits on whoever has access to each one.
Want the product view of these controls? See how the CloudEagle.ai kill switch works.
How Much Oversight Does Each AI Agent Need?
Oversight should match what an agent can do, not how it was built. Grouping agents into three risk tiers keeps reviews focused where the impact is highest:
Treat the tiers as a starting point. A read-only agent that reaches highly sensitive data may need higher-tier controls.
Before approving any agent, its owner should be able to answer four questions:
- Does it need write access, or would read-only work?
- Which sensitive data and systems can it reach?
- Who can stop it, and how fast?
- What happens to it if I leave or its purpose changes?
If the team can't answer these, the agent isn't ready for production access.
What Should an AI Agent Inventory Record?
An AI agent inventory should answer the questions an auditor or incident responder asks first. A list of names and platforms is not enough. At minimum, record:
Matching owners against current employee records is the fastest way to find agents that need reassignment. Keep the inventory live by connecting it to discovery sources and making registration part of every launch.
Who Owns AI Agent Management?
IT should own AI agent management. It already runs the identity providers agents authenticate through, the offboarding process agents slip past, and the access reviews agents need to join.
Handing agents to a separate AI team usually creates a second inventory that drifts from the first. Other teams feed decisions in; IT keeps the system of record and acts on it.
What IT Owns
- The agent inventory and the intake gate
- Owner assignment, and reassignment when owners leave
- Access reviews for agents
- Retirement and the kill-switch runbook
- Reporting on agent count, orphaned agents, and spend
Where IT Needs Input From Other Teams
How CloudEagle.ai Manages AI Agents Across Platforms
CloudEagle.ai is an AI governance, SaaS management, and SaaS security platform that gives enterprises one command center to discover, govern, and secure their SaaS and AI stack, including human and non-human identities. RingCentral, Automation Anywhere, and Treasure Data are among its customers.
CloudEagle.ai treats every AI agent as a non-human identity and runs it through the same ownership, access review, and offboarding discipline IT applies to employees. It reads identity, permission, and activity data only, so agents keep running as they do today until an admin acts.
Why a Cross-Platform View Matters
Find Every Agent, Registered or Not
- Reads agent identities from Microsoft Entra ID, Okta, and connected apps
- Shows each agent's source, status, owner, credentials, and last activity in one inventory
- Surfaces shadow agents IT never approved, alongside AI apps and MCP servers
Every agent in one inventory, with its source, status, owner, and last activity.
See What Each Agent Can Reach
- Lists every app and data source an agent connects to, from Salesforce to the HR system
- Shows assigned and inherited roles, which is usually where the extra access sits
- Flags agents with access to sensitive data, so reviews start with the riskiest ones
Shadow agents, sensitive-data access, and ownerless agents flagged in one view.
Keep Every Agent Owned and on Least Privilege
- Flags agents whose owner has left, so IT can reassign or retire them
- Removes roles and permissions an agent doesn't need
- Puts agents in the same access reviews as employees, filtered for inactive, ownerless, over-permissioned, or never-rotated agents
Revoking an over-privileged role from an agent whose owner has left.
Trace Activity and Spend
- Activity logs show what each agent did, changed, and touched, traced back to its owner
- Token usage is tracked by user, team, and project, so a looping agent shows up before the invoice does
Agent activity, from exports to secrets read, traced to the owner.
Stop a Risky Agent From the Same Screen
- Revoke any role, down to a single resource
- Rotate the agent's secret, where the platform supports it
- Deactivate its identity
Nothing happens automatically. Admins decide, and every action is logged for auditors.
CloudEagle.ai governs identity and access rather than blocking individual agent actions at runtime. Teams that need runtime enforcement pair it with a runtime security tool.
The kill switch revokes roles, rotates secrets, or deactivates the agent, with every action logged.
See Every Agent Running in Your Environment
What AI Agent Management Looks Like in Practice
ArmorCode already governed human identities tightly, with structured workflows, regular reviews, and least-privilege controls. Service accounts, API tokens, and AI agents sat outside all of it.
They were created for integrations and projects and rarely decommissioned when those ended, and many held admin-level access nobody had reviewed. The security team estimated it could see about 40% of them.
After bringing them into CloudEagle.ai:
New service accounts, tokens, and agents now enter the same continuous review cycle as employees. Governing non-human identities became just as important as managing human access.
— Karthik Swarnam, Chief Security and Trust Officer, ArmorCode
How Do You Measure AI Agent Management Over Time?
A working AI agent management program shows up in a small set of operational metrics:
Define how each metric is calculated before you use it as a target. Inventory coverage only means something when the discovery sources and known gaps are written down.
A growing agent count is not a problem by itself. It becomes one when ownership, access reviews, and retirement fall behind it.
CloudEagle.ai gives IT one inventory of every agent, its owner, and what it can reach, with a kill switch in the same view.
Book a demo to see the agents running in your environment.
Frequently Asked Questions
What Is the Difference Between AI Agent Management and AI Agent Governance?
AI agent governance sets the rules: which platforms are sanctioned, what data agents may touch, and who approves them. AI agent management applies those rules every day through inventory, ownership, access reviews, monitoring, and retirement.
Are AI Agents Non-Human Identities?
Yes. An AI agent authenticates with credentials such as a service account, an OAuth token, or an API key, so it should be managed like any other non-human identity, with a named owner, scoped permissions, and regular access reviews.
What Is an AI Agent Kill Switch?
An AI agent kill switch is an approved way to stop a risky agent fast by revoking its roles, rotating its secrets, or deactivating its identity. It should name who can trigger it and log every action. Stopping execution alone does not revoke existing credentials, so containment should cover both.
How Do You Find Shadow AI Agents?
Pull agent identities from your identity providers, agent platform consoles, and connected SaaS apps, including the service accounts and API keys agents use. Compare the results with your approved register. Anything unregistered is a shadow agent until its owner and purpose are confirmed.
Can Native Agent Consoles Manage AI Agents on Their Own?
For agents built on that platform, often yes. Copilot Studio and ServiceNow both offer inventory and governance controls. Each console starts from its own platform, though, so organizations running agents in several places usually need a cross-platform inventory as well.
Does CloudEagle.ai Work With Microsoft Entra ID and Okta?
Yes. CloudEagle.ai reads agent identities from Entra ID, Okta, and connected apps, so agents appear whether or not anyone registered them. It then adds the owner, roles, permissions, and activity to each agent record.
Does CloudEagle.ai Slow Down or Interfere With AI Agents?
No. CloudEagle.ai reads identity, permission, and activity data. Agents keep running as they do today until an admin chooses to revoke a role, rotate a secret, or deactivate an identity.
How Quickly Can CloudEagle.ai Be Set Up?
Onboarding takes about 30 minutes once your identity providers are connected. How much of your agent estate the inventory covers depends on which identity providers and apps you connect.
Stop Paying for Shadow AI
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