Can You Actually Control AI Agents at Scale?

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes.

We explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, update systems, write code, send messages, and operate across workflows at machine speed, but those same capabilities make them difficult to govern with rules written in advance or approval prompts reviewed one at a time.

Dev explains why tool access increases blast radius, why agents can route around controls in surprising ways, and why human-in-the-loop review can become security theater when agents operate at scale. We also discuss what enterprises need instead: better visibility, runtime enforcement, policy-aware governance, agent observability, and recovery mechanisms for when something goes wrong.

Along the way, we dig into MCP and tool sprawl, small language models for policy enforcement, defense in depth, agent rewind, and why AI may be needed to help secure AI.

🗒️ Full show notes: https://twimlai.com/go/770.

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📖 CHAPTERS
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00:00 – Introduction
02:04 – Barriers to enterprise AI adoption
04:38 – Rubrik and defining agents
07:03 – Limitations of human-in-the-loop and legacy security
09:05 – Zero trust in agents
15:07 – Three pillars of agent security
19:28 – SAGE
20:54 – Recovery and resilience
25:20 – SLMs vs. LLMs
26:28 – Preventing agents from hacking guardrails
30:18 – Real-world examples of security incidents
34:27 – Importance of AI-in-the-loop system
37:50 – MCP and A2A protocols
40:55 – Observability for developers vs. security
44:22 – Developer workflows vs. cowork agents
46:25 – Post-training SLMs and inference time customization
48:02 – Rubrik security cloud and Rubrik agent cloud
51:33 – Future directions

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