Why AI Agents Are Not “Create Once and Forget” Systems

AI agents are becoming a regular part of modern workplaces. They send emails, read documents, update systems, and automate tasks across Microsoft 365. But unlike traditional applications, AI agents are not static.

They evolve.
They change behavior.
They gain new abilities.
And sometimes, they are forgotten.

This is why Agent Lifecycle Management is one of the most important topics in Microsoft Agent 365.

Without proper lifecycle management, organizations quickly lose control of their AI agents—leading to security risks, compliance failures, and operational confusion.

In This Article

In this article, we explain:

What agent lifecycle management really means
How AI agents are created and approved
How updates and changes are controlled
How agents are paused, disabled, or retired
How shadow and unused agents are identified and handled
Why lifecycle management is critical for secure AI adoption

Previously in This Series

In the previous article, we explored agent identity and learned how Microsoft Entra Agent ID gives every AI agent a clear identity, permissions, and accountability.

Once identity is established, the next challenge is managing what happens to the agent over time. That is exactly what lifecycle management addresses.

What Is Agent Lifecycle Management?

Agent lifecycle management is the practice of controlling an AI agent from its creation to its retirement.

It ensures that:

Every agent is known
Every agent is approved
Every agent is monitored
Every agent is eventually cleaned up

In simple terms:

Agent lifecycle management prevents AI agents from becoming invisible, over-powered, and dangerous.

Why AI Agents Need Lifecycle Management

Traditional software applications:

Are deployed once
Change slowly
Have predictable behavior

AI agents are different.

AI agents:

Learn and adapt
Interact with multiple systems
Make decisions automatically
Run continuously

Because of this, unmanaged agents can:

Accumulate permissions over time
Continue accessing data long after they are needed
Operate without ownership
Break compliance silently

Lifecycle management keeps AI agents under continuous control.

The Complete Lifecycle of an AI Agent

An AI agent typically goes through the following stages:

Creation
Approval
Active usage
Updates and changes
Pause or disable
Retirement

Each stage requires governance. Skipping any stage introduces risk.

Agent Creation: Where Everything Begins

AI agents can be created in multiple ways:

A developer builds an agent for automation
A business team requests an AI assistant
A pilot project introduces a temporary agent

When an agent is created:

It is registered with Microsoft Agent 365
It receives an identity through Microsoft Entra
Initial permissions are requested

At this point, the agent exists—but it should not be active yet.

Why Approval Is Mandatory for AI Agents

AI agents should never be allowed to operate without approval.

Approval ensures:

The agent has a clear business purpose
Permissions follow least-privilege principles
Ownership is assigned
Security and compliance teams are aware

Approvals are typically handled through the Microsoft 365 Admin Center.

Without approval:

Anyone can create agents
Shadow agents multiply
Security teams lose visibility

Active Usage: When the Agent Starts Working

Once approved, the agent becomes active.

During active usage:

The agent performs tasks
Accesses Microsoft 365 data
Interacts with users or systems

This is where monitoring and observability become critical.

Admins need answers to questions like:

What is the agent doing?
How often is it acting?
Is it behaving as expected?

Lifecycle management ensures agents are continuously observed, not trusted blindly.

Managing Updates and Changes

AI agents are not fixed systems.

Over time, agents may:

Gain new capabilities
Use additional tools
Access new data sources
Change behavior logic

Every change increases risk if not managed properly.

Lifecycle management ensures that:

Changes are reviewed
Changes are approved
Changes are logged
Changes are auditable

This prevents silent permission creep.

Why Version Control Matters for AI Agents

Without version control:

No one knows what changed
Rollbacks are impossible
Security incidents are hard to investigate

With lifecycle management:

Each update is tracked
Older versions can be disabled
Risk is controlled

Version awareness is essential for trust.

Pausing or Disabling an AI Agent

Not every issue requires deleting an agent.

Agents may need to be paused when:

Security investigations are ongoing
Unexpected behavior is detected
A business process is temporarily stopped

Pausing an agent:

Stops all actions immediately
Preserves identity and logs
Allows safe investigation

This is safer than deleting and recreating agents.

Retiring an AI Agent Safely

Every AI agent eventually reaches the end of its life.

Common reasons:

Project completion
Process redesign
Replacement by a better agent

Safe retirement includes:

Revoking all permissions
Disabling the identity
Preserving audit logs

Lifecycle management ensures retired agents do not leave behind active access.

Shadow Agents: The Hidden Risk

Shadow agents are agents that:

Were created without approval
Are no longer actively used
Still have access to data

They are one of the biggest AI risks in enterprises.

Lifecycle management helps by:

Identifying inactive agents
Highlighting unused permissions
Allowing admins to review or disable them

Security teams often rely on Microsoft Defender to detect abnormal or unused agent behavior.

Compliance and Auditing Across the Lifecycle

Lifecycle management is essential for compliance.

With proper controls:

Every agent action is logged
Every permission change is recorded
Every decision is traceable

This integrates with Microsoft Purview for:

Audit logs
Data Loss Prevention
Regulatory reporting

Without lifecycle management, compliance becomes guesswork.

What Happens Without Lifecycle Management?

Organizations that ignore lifecycle management often face:

Over-permissioned agents
Forgotten identities
Data exposure incidents
Failed audits
Loss of trust in AI systems

Most AI failures happen not because AI is wrong—but because no one managed its lifecycle.

Why Agent Lifecycle Management Is Non-Negotiable

Agent lifecycle management:

Keeps AI under control
Protects enterprise data
Simplifies security reviews
Makes compliance achievable
Enables safe AI scaling

Without it, AI adoption cannot mature.

What’s Coming Next

Now that we understand how agents are created, updated, paused, and retired, the next step is to understand how governance is applied proactively.

Next Article:
Governance by Design: Policies, Blueprints, and Guardrails in Microsoft Agent 365

Final Thoughts

AI agents behave more like employees than applications.

Employees:

Are onboarded
Are monitored
Change roles
Eventually leave

AI agents deserve the same discipline.

Agent lifecycle management is not overhead.
It is the foundation of responsible AI adoption.

Agent Lifecycle Management in Microsoft Agent 365: From Creation to Retirement