Key takeaways
A personal AI agent is an autonomous software tool that executes multi-step workflows across applications without direct user prompting, using persistent background permissions and credentials.
Unlike on-demand generative AI tools that wait for input, personal agents execute tasks on a schedule.
Okta for AI Agents is designed to integrate with CrowdStrike Falcon to detect these untrusted agents across endpoints and link them to workforce identities to help you enforce governance policies.
Agents that don't wait to be asked
For the last few years, AI agents have run on prompts. An employee asks ChatGPT to draft a document or analyze a spreadsheet, while a developer asks Cursor to build a feature. These tools have become remarkably capable, and many can now work through complex, multi-step tasks on their own. But every session starts the same way: with a person and a request. When the work is done, the tool waits for the next request.
Now, we are seeing how a new generation of agents work differently. Personal AI agents don't wait to be asked. The difference isn't how capable they are; it's how much they're trusted to do on their own. With on-demand AI tools like Cursor, the user defines the task and the tool finishes it. With a personal agent, the user sets a goal, and the agent figures out the steps and which user apps and accounts to use along the way. To do that, personal agents use their own computer or browser to work, sign in to the user's apps and accounts with the user's credentials, and continue working on a schedule, even while the user sleeps. They reach out when they need a decision.
Meta Muse, from Meta, helps manage everyday tasks such as schedules, bookings, and purchases. Grok Bot, from xAI, gives users a team of named bots that sign in to apps and pass work to each other.
What's the difference between on-demand AI tools and personal AI agents?
| Capability/attribute | On-demand AI tools (ChatGPT, Cursor) | Personal AI agents (Grok Bot, Muse) |
|---|---|---|
| Execution trigger: How work starts | The user opens the tool and gives a task | The user sets a goal; the agent decides when to act |
| Operational schedule: When it works | During the user's session | Continuously, in the background, on a schedule |
| Credential scope: What it reaches | Mostly the task at hand | The user's apps, accounts, and services, to act across them |
| User interaction: How it talks to the user | Waits for the next prompt | Reaches out when it needs a decision |
| Execution environment: Where it works | On the user's device, in the user's session | On the user's device or in its own computer or browser |
The line is blurring fast: with enough setup, some On-demand AI tools can be turned into a personal agent. But for most users, that setup is the big difference. Personal agents come ready to work out of the box, which is what makes them so powerful but also so risky.
Personal agents are becoming increasingly popular and may already be available across the organization through existing plans, such as SuperGrok and Cursor's paid individual and team plans. This means employees could already be using personal AI agents with no new purchase, procurement review, or security assessment, leaving them invisible to IT and security teams and outside the reach of identity controls.
Most organizations can't say which agents exist, who owns them, or what they're allowed to do. Securing them starts with treating AI agents as first-class identities: each one known, tied to an accountable owner, and governed by policy, like any other identity in the workforce. This security approach begins with a simple question: Where are my agents?
How does shadow AI agent discovery work on enterprise endpoints? According to the blueprint for the secure agentic enterprise, every organization should be able to answer four fundamental questions about its agents to become a secure agentic enterprise:
- Where are my agents?
- What can they do?
- What are they doing?
- How do I respond?
Okta’s Shadow AI Agent Discovery for Endpoints is designed to help answer the first question: Where are my agents? The tool is built to pull shadow AI into the open, import agents built on the platforms you use, and help you manage discovered agents in one place, regardless of your identity provider. And if an incident occurs, the tool is designed to help you answer key questions: which agent, whose, and on which device. The work to secure your agents starts with two moves: find the agents, then register them with accountable owners and specific policies.
Three-step detection architecture
The Shadow AI Agent Discovery for Endpoints feature in Okta for AI Agents discovers personal AI agents, such as Grok Bot and Muse, using the CrowdStrike Falcon integration. It works in three steps:
- Okta reads the software inventory and events that CrowdStrike Falcon already collects from managed devices. This uses read-only access, so there's nothing new to deploy on endpoints.
- Okta matches that inventory against its catalog of known AI agents. The catalog covers coding assistants and agentic IDEs, desktop AI apps, local LLMs, and now personal AI agents.
- Okta ties each finding to a person. Because Okta knows your workforce, each agent is linked to the employee who runs it, not just a hostname.
The result is an inventory of endpoint AI agents across your enterprise. You have one centralized directory with human owners and policies assigned to each agent.
An inventory only helps if it stays up to date, and new AI agents launch every week. Security teams can't track them all, and they shouldn't have to. Okta maintains the detection catalog, which now covers more than 30 endpoint AI agents. As new personal AI agents launch, Okta aims to continuously update the detection catalog, helping ensure they appear in your inventory without requiring custom rules.
Discovery and the resulting inventory are only as good as what you do with it. Because Okta serves as a neutral identity layer, each agent you find becomes a managed identity in a single place. From there, Okta for AI Agents manages the rest of the agent lifecycle: governing what it can access with scoped permissions, providing real-time visibility into what it's doing, and revoking access when something goes wrong.
How should security teams govern discovered AI agents?
Once agents are in the inventory, the work of managing them begins. Security teams should take these steps:
- Review which agents are running and who uses them.
- Talk to the users. Most adoption is well-intended. Find out what problem the employee is trying to solve.
- Decide on a policy. Approve, offer a trusted alternative, or block.
Personal AI agents are becoming part of how people work. The first step to securing them is knowing they're there.
Get started with endpoint agent discovery
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Just getting started with securing your AI agents? Learn more about how Okta for AI Agents can help securely manage your AI agents from a single control plane.
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