The Rise of Autonomous Agents: A New Identity Challenge
Jun 7, 2025
2 mins
Matt (Co-Founder and CEO)
The first in a series of articles focusing on the rise of autonomous agents. Let us know how you are tackling this.
TL/DR
We're witnessing a significant shift from static software to systems that reason, act, and adapt. AI agents—whether they're LLM-powered workflows or task-oriented scripts—are no longer confined to the backend. They're now interacting directly with products, submitting data, and even making decisions on behalf of users.
This fundamental shift introduces a critical identity and access problem. Before we explore solutions, we need to clarify a question most platforms still can't answer: What exactly is an AI agent?
Defining the AI Agent: Agent vs. Bot vs. Script vs. User
To understand what makes an AI agent unique, let's break down the common terms:
Term | Description | Key Traits |
User | A human Browse, clicking, authenticating, and consenting. | Session-based, visible, intention-driven |
Script | Code that executes a specific task, typically backend and system-triggered. | Deterministic, short-lived, predictable |
Bot | An automated responder, such as a chatbot or a cron job. | Often reactive, sometimes exposed to users |
Agent | An autonomous actor capable of decision-making and delegation. | Goal-oriented, multi-system, user-authorized |
An AI agent occupies a semi-autonomous space. It's neither a human nor a pure backend service; it exists in the middle layer of software.
An AI agent may:
Take action on behalf of a user
Access external APIs
Cross product boundaries
Initiate workflows without direct human prompts
These capabilities make it a new kind of identity—one that requires its own set of rules.
Key Properties of Modern Agents
Modern AI agents possess distinct characteristics that set them apart:
Delegated: Their access isn't self-owned; it's granted by a human, often indirectly.
Autonomous: Once launched, they can operate without constant user involvement.
Ephemeral: Many agents are short-lived, spun up for specific tasks or interactions.
External: They might run outside your platform but still interact with your APIs.
Composable: Agents frequently call other agents or trigger downstream actions.
Why Identity Models Must Evolve
Traditional authentication models are designed for human users or fixed services:
The user logs in.
The system verifies the session or token.
Access is enforced via roles or scopes.
With agents, this model breaks down:
There is no user session.
There is no login flow.
The agent may need access on behalf of multiple users.
The platform lacks a built-in mechanism to determine who authorized the agent or why.
This is precisely where traditional identity systems fail.
Where Legacy Systems Fall Short for Agents
Attempting to use human- or service-centric identity tools for AI agents leads to significant issues:
Legacy Model | Why It Fails with Agents |
OAuth login flow | Agents don't use browsers or UIs. |
Long-lived API keys | Shared, over-permissioned, and difficult to revoke. |
Role-based access control (RBAC) | Static roles don't reflect dynamic agent context. |
M2M client credentials | No link to user, no delegation, and no auditability. |
SSO or JWT impersonation | Introduces spoofing risk without a clear delegation chain. |
Ultimately, you end up with one of three undesirable outcomes:
Agents pretending to be users (spoofing)
Agents using overly powerful service accounts (risk)
Agents being blocked entirely (friction)
None of these approaches are scalable or secure.
Agents Aren’t the Future — They’re the Present
If your customers are already:
Automating workflows with tools like OpenAI, CrewAI, or LangChain
Connecting external copilots to your APIs
Building internal AI tools that integrate with your platform
…then agent access is already a reality for you. The crucial question is: Can you secure it effectively?
Takeaway
AI agents represent a new category of actor. They are neither traditional users nor typical services.
They exist in the space between—and they are here to stay.
If you continue to treat them like mere bots or background jobs, you risk either security breaches or stifling the future potential of your product.
Do you have any further questions about securing AI agents or their impact on identity management?