AI Agents Should Be Treated Like Hackers

AI Agents Should Be Treated Like Hackers

Author: Demetrios July 6, 2026 Duration: 31:35

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.


We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data creates entirely new challenges around governance, access control, and risk management.


Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.


Apollo GraphQL: https://www.apollographql.com


Matt DeBergalis: https://www.linkedin.com/in/debergalis

Alex Salkever: https://www.linkedin.com/in/alexsalkever


Timestamps:

[00:00] AI, APIs, and Trust

[01:16] MCP API Lessons

[06:16] GraphQL and MCP Integration

[12:55] API Security for MCP

[16:10] Linux Kernel Security Concerns

[19:09] API Design and Controls

[21:52] Trust in Autonomous Systems

[25:06] MCP GraphQL Wish List

[27:13] API Access Patterns

[28:44] GraphQL API Perspective


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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