AI is evolving at an unprecedented pace. In just the past twelve months, the landscape has shifted from early experiments with reasoning models and tool use to a full-blown ecosystem of agentic frameworks — Cursor, Claude Code, Codex, OpenClaw, and many more — that are reshaping how teams write code, manage documentation, triage communication, and coordinate complex work. These aren't theoretical capabilities. Engineering teams are already using coding agents to debug production issues in minutes instead of hours, reverse-engineer legacy systems with minimal documentation, and maintain knowledge bases that stay accurate over time. Beyond the codebase, agentic AI is stepping into meeting facilitation, email triage, and cross-team coordination — tasks that have traditionally consumed disproportionate amounts of human attention. But the promise comes with real traps. The familiar challenges of hallucination, limited context windows, and verbose outputs remain. More critically, as organizations move from pilots to production, new pain points are emerging: service reliability when teams depend on agentic infrastructure, token costs that scale faster than expected, and governance gaps around supervision, accountability, and auditability of AI-driven decisions. This talk takes a candid look at both sides. Drawing on practical experience, it explores what agentic AI can realistically deliver today, where the hidden costs lie, and why the organizations that succeed won't be the ones that bolt AI onto existing processes — but the ones that blend it into every level of their operations, from individual workflows to corporate governance, and allow their processes to evolve alongside the technology. The road ahead is fast, complex, and full of potholes — but it's the most exciting road our industry has seen in decades.