AI Agents and Identity Management

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Sep 13, 2025

Julianna Lamb, co-founder and CTO of Stytch, delves into identity management in AI, discussing its complexities amidst evolving technologies. She highlights the challenges of permissions and security as AI agents take on human tasks. The conversation covers innovative authentication strategies, including the need for layered verification and adapting systems with robust security. Julianna emphasizes experimenting with AI agents and suggests the importance of feedback mechanisms for seamless integration and optimal performance, all while navigating the future of identity standards.

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INSIGHT

Identity Defined By Actions And Access

Identity is an entity that performs actions and has access to resources, whether human, agent, or machine.
Authentication verifies who the entity is and authorization controls what they can access.

INSIGHT

Agents Sit Between Humans And Machines

Agents sit between machines and humans: they act autonomously but need broad permissions to be useful.
That combination raises unique risks because agents act faster and can cause rapid, large-scale damage if unchecked.

ADVICE

Use OAuth And MCP For Agent Access

Adopt OAuth and MCP server patterns to expose APIs safely so agents can authenticate and obtain scoped access.
Use standard consent flows to let humans grant limited permissions to agents for mass adoption.

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Summary
In this episode of the AI Engineering Podcast Julianna Lamb, co-founder and CTO of Stytch, talks about the complexities of managing identity and authentication in agentic workflows. She explores the evolving landscape of identity management in the context of machine learning and AI, highlighting the importance of flexible compute environments and seamless data exchange. The conversation covers implications of AI agents on identity management, including granular permissions, OAuth protocol, and adapting systems for agentic interactions. Julianna also discusses rate limiting, persistent identity, and evolving standards for managing identity in AI systems. She emphasizes the need to experiment with AI agents and prepare systems for integration to stay ahead in the rapidly advancing AI landscape.

Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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Your host is Tobias Macey and today I'm interviewing Julianna Lamb about the complexities of managing identity and auth in agentic workflows

Interview

Introduction
How did you get involved in machine learning?
The term "identity" is very overloaded. Can you start by giving your definition in the context of technical systems?
- What are some of the different ways that AI agents intersect with identity?
We have decades of experience and effort in building identity infrastructure for the internet, what are the most significant ways in which that is insufficient for agent-based use cases?
- I have heard anecdotal references to the ways in which AI agents lead to a proliferation of "identities". How would you characterize the magnitude of the difference in scale between human-powered identity, deterministic automation (e.g. bots or bot-nets), and AI agents?
The other major element of establishing and verifying "identity" is how that intersects with permissions or authorization. What are the major shortcomings of our existing investment in managing and auditing access and control once you are within a system?
- How does that get amplified with AI agents?
Typically authentication has been done at the perimeter of a system. How does that architecture change when accounting for AI agents?
- How does that get complicated by where the agent originates? (e.g external agents interacting with a third-party system vs. internal agents operated by the service provider)
What are the concrete steps that engineering teams should be taking today to start preparing their systems for agentic use-cases (internal or external)?
How do agentic capabilities change the means of protecting against malicious bots? (e.g. bot detection, defensive agents, etc.)
What are the most interesting, innovative, or unexpected ways that you have seen authn/authz/identity addressed for AI use cases?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on identity/auth(n|z) systems?
What are your predictions for the future of identity as adoption and sophistication of AI systems progresses?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0