AI
When I first encountered agentic AI systems in 2022, I recognized a shift in what software can do—but also how easily teams can ship unstable demos. The difference between value and theatre is execution: clear goals, permissions, evaluation, and monitoring.
Agentic systems aren't magic. They are workflows—planning, tool use, and decision logic—and they need the same rigor as any production system: access control, audit trails, failure modes, and operational ownership.
Key Insight: Agentic AI is useful when it can take actions inside defined guardrails. The work is in the guardrails: permissions, evaluation, monitoring, and governance.
What Makes AI "Agentic"?
Working with dozens of companies on AI implementation, I've found that many business leaders struggle to distinguish between various AI capabilities. Let me clarify what truly makes an AI system "agentic," based on my experience:
1. Autonomous Decision-Making
Unlike traditional automation systems that follow rigid rules, agentic AI can make contextual decisions based on its understanding of goals and constraints. The critical distinction is that these systems can determine how to achieve an objective, not just execute a predefined sequence.
Conclusion: The Strategic Imperative
Agentic AI can remove manual steps and reduce cycle time—but only if you treat it as a production system: clear ownership, guardrails, evaluation, and monitoring.
Teams that do well with agentic workflows build the boring parts first: data access, tool permissions, evaluation, and governance. That is what lets you ship safely and iterate.
The question isn't whether agentic AI will show up in your industry. It's whether you'll ship it with controls and measurable outcomes—or ship a demo that becomes a liability.
