AI Agents Are Turning Credentials Into a New Identity Problem

AI agents are quickly becoming capable of doing more than generating text. They can open business applications, retrieve files, update records, trigger workflows and move between several tools while completing a single assignment. That flexibility is useful, but it is also creating a familiar identity problem. For years, companies have tried to reduce their dependence on reusable passwords and long-lived credentials. The reason is simple: once a reusable secret exists outside the context of the person who originally received it, the system may know that the credential is valid without understanding whether its current use still makes sense. AI agents can recreate that same problem in a new form. An agent may receive access for one legitimate task, complete the work and still retain credentials that remain valid afterward. The assignment can disappear while the permission survives.

Access can outlive the task

Consider an AI agent helping a sales team prepare a customer proposal. It may need temporary access to a CRM platform, product pricing, shared documents, email and a contract management service. During the task, those permissions are reasonable. The problem begins when the agent is connected using a standing service account, reusable API token or another credential that continues working after the proposal has been completed. The agent may no longer need access to those systems, but the credential still exists.

That creates a disconnect between why access was granted and whether access is still appropriate. Traditional applications are often built around predictable workflows. An AI agent can be more dynamic. It may choose which tool to call, which information to retrieve and which sequence of steps makes sense based on the task it has been given. That makes lingering permissions more important than they would be for a simple application performing one fixed operation.

Much of identity security has traditionally focused on answering a straightforward question: Is this user or application allowed to authenticate? AI agents introduce several additional questions. Who asked the agent to perform the task? What exactly did that person authorize? Which systems should the agent be allowed to use? Which actions should it be allowed to perform? How long should that permission remain valid?

An agent might technically possess a valid credential while still attempting something outside the purpose of the original assignment. That is why simply giving each agent its own account does not solve the entire problem. Identity has to remain connected to intent.

Static credentials are a poor match for dynamic agents

Long-lived credentials are convenient because an application can store them and reuse them whenever necessary. That same convenience is what makes them problematic for agent workflows. If an API key, access token or service credential is stored directly inside an agent configuration, the agent effectively becomes the holder of that access. The permission may remain available regardless of who is using the agent, what assignment it is working on or whether the original employee still has the authority to approve that activity.

A better model is to treat access as something the agent requests when needed, rather than something it permanently possesses. Instead of asking, “Does this agent have the credential?” the organization can ask, “Should this agent receive access for this particular action right now?”

Human authentication offers a useful lesson

Modern authentication has gradually moved away from reusable secrets. Passwordless systems, hardware-backed credentials and other identity technologies increasingly rely on cryptographic proof and additional context instead of expecting users to repeatedly present a shared secret. Agent access can follow a similar philosophy.

An AI agent does not need to be treated exactly like a human employee, but its access can still depend on the identity and authority of the person behind the task. For example, an employee could ask an agent to prepare a report using information from several internal systems. Instead of giving the agent permanent credentials to those systems, the organization could issue temporary permissions associated with that specific assignment. When the task finishes, those permissions disappear. If the employee changes roles or no longer has permission to access one of the systems, the agent should not continue operating with authority that the employee no longer possesses.

Delegation is becoming the central identity question

This leads to one of the most important concepts in enterprise agent security: delegated authority. When a person asks an agent to perform work, the agent is acting on behalf of someone else. The organization therefore needs to preserve the relationship between:

the person who initiated the task
the agent performing the work
the systems being accessed
the actions being requested
the policies that allowed those actions

That relationship can become complicated very quickly. An agent may call one tool, which then invokes another service. It may pass part of the assignment to another agent. It may continue working several minutes after the person who started the task has moved on. The further the workflow expands, the more important it becomes to preserve the original authority behind it. A token identifying only the agent may not provide enough context.

Permissions should follow the task

One way to reduce this problem is to design agent permissions around individual tasks. Suppose an agent needs to retrieve three pricing documents and place their figures into a proposal. The permission it receives does not necessarily need to provide broad access to the entire document repository. It could instead be limited to the specific files required for the assignment.

The same principle can apply to actions. An agent that needs to read customer information does not automatically need permission to modify it. An agent preparing a draft contract may not need the authority required to approve or finalize the contract. Separating these actions keeps the agent’s capabilities closer to the task it is actually performing.

Short-lived access is useful, but scope matters too

Temporary credentials are an important part of this approach. An agent could receive access that lasts only for the duration of a workflow or for a short predefined period. But expiration alone is not enough. A credential that lasts only a few minutes can still provide far more access than the task requires.

Good agent access therefore needs limits in several dimensions: Time: How long should the permission exist? System: Which application or service can the agent access? Data: Which information can it retrieve? Action: What operations can it perform? Authority: Which person or role authorized the request? Combining those limits produces a much more precise permission than simply issuing a general token with a shorter expiration date.

Access brokers could become increasingly important

Organizations can also place an access layer between AI agents and important business systems. Instead of placing reusable credentials inside the agent, credentials can remain stored behind a controlled service. When the agent needs to perform an action, it requests access through that service.

The access layer can then evaluate the context surrounding the request before allowing it. That evaluation might consider the employee’s identity, the agent’s assigned task, the target system, the requested action and any relevant company policies. If everything matches, the agent receives a limited credential or the broker performs the authorized action on its behalf. This approach keeps long-lived credentials away from the agent itself and gives security teams one central location where access decisions can be recorded and reviewed.

Sensitive actions may require another confirmation

Not every AI action carries the same level of importance. Reading a public product specification is very different from approving a payment or changing an important business configuration. Agent systems may therefore need different levels of verification depending on what the agent is trying to do.

Routine actions could proceed automatically under existing policy. Higher-impact actions could require another confirmation from the employee, stronger authentication or approval from someone with the appropriate role. The goal is not to place friction in front of every AI workflow. It is to make the level of verification match the importance of the action.

Audit logs need more context

Traditional system logs are good at answering questions such as which account connected to a service and when. Agent workflows need richer records. Security teams may need to understand who started the task, what that person asked the agent to accomplish, which tools were used, which permissions were issued and which actions ultimately took place.

Without that context, an organization might see that a valid token accessed a system without understanding why the access occurred. With agents, the story behind the request can be just as important as the credential itself.

Multi-agent workflows make the problem harder

The challenge becomes even greater when agents begin delegating work to other agents. Agent A may receive authority from an employee and then ask Agent B to complete one part of the assignment. Agent B may call another service or use another tool. If each step introduces another independent credential, it becomes increasingly difficult to determine where the original permission came from.

Future enterprise agent platforms will likely need ways to carry delegated authority through these chains without turning every participating agent into a permanent holder of credentials. The original purpose should remain visible throughout the workflow.

Role changes need to affect agents too

Human identity systems already respond when employees join teams, change roles or leave an organization. Agent permissions need to react to those changes as well. If an employee moves from one department to another, an agent they previously configured should not quietly continue using access associated with the employee’s former role.

The same applies when workflows change. Adding a new tool to an agent can dramatically expand what the agent is capable of doing even if the credentials themselves have not changed. That means agent access reviews need to consider both permissions and capabilities.

What organizations can do today

Companies do not need to wait for an entirely new identity standard before improving agent access. A practical starting point is simply understanding where credentials already exist. Teams can identify which AI agents connect to business applications, which accounts or tokens those agents use and who originally approved each connection.

Long-lived credentials embedded directly inside configurations deserve particular attention. Organizations can then begin reducing access according to the systems, data and actions actually required by each workflow. Temporary permissions, centralized access controls, approval requirements and better audit records can gradually replace the assumption that every agent needs permanent credentials.

AI agents need identity systems built around purpose

The password era demonstrated the limitations of reusable secrets. A credential could confirm that someone possessed the correct password without proving whether the person using it was still the right person, operating from the right context or performing the right task. AI agents risk recreating that separation between credentials and intent.

The difference is that agents can interact with multiple systems automatically and make decisions during the workflow. That makes context even more important. The safest long-term approach is likely one where agents do not permanently own broad permissions. Instead, they receive exactly the authority required for the current assignment, backed by the identity of the person who delegated it and removed when that authority is no longer needed.

AI agents may make enterprise software far more capable, but for that capability to scale responsibly, access needs to remain attached to purpose rather than turning into another collection of credentials that quietly remain active long after the task is finished.