Summary
A practitioner session on what breaks first when you ship agentic systems: security boundaries, privacy constraints, and the real deployment patterns that keep token-heavy automation from turning into risk-heavy automation.
Why it matters
As teams push more work onto agents, the limiting factor is rarely model IQ—it’s controls. This kind of event is where you learn the guardrails (data access, auditability, policy) that keep high-usage AI deployments viable.
Tokenmaxxing angle
Tokenmaxxing in production means sustained, autonomous execution. The right KPI isn’t "more agent work"—it’s bounded agent work: least-privilege tool use, scoped context, and traceable actions per token.
Source takeaway
Expect concrete deployment lessons: how teams segment data, design tool permissions, and manage privacy/security while still getting the upside of agentic throughput.