As enterprises move from generative assistance to autonomous intelligence, Agentic AI is emerging as a defining force in enterprise transformation. In this exclusive interview, Dr. Adnan Masood, Chief AI Architect at UST, unpacks how Agentic AI is evolving from “assist” to “act”—enabling goal-driven systems that plan, reason, and execute under governance.
Q: How do you see the rise of Agentic AI, and what sets it apart from conventional AI in enterprise cases?
A: Agentic AI represents a shift from systems that simply assist to those that can act—goal-oriented agents capable of planning, invoking tools, maintaining memory or state, and executing bounded actions under defined policies. This evolution bridges the insight-to-execution gap, turning LLMs from passive chatbots into active workflow participants.
In practice, enterprises are combining reasoning and acting techniques (such as ReAct and Reflexion) with graph-orchestrated frameworks to ensure determinism, safety, and governance. Gartner identifies Agentic AI as a key 2025 trend, projecting that about one-third of enterprise applications will include agentic capabilities by 2028, with roughly 15% of routine decisions being made autonomously—well beyond the scope of prompted GenAI.
Q: Which major verticals are leading the adoption of Agentic AI and why?
A: The fastest adopters are sectors with structured workflows, rich telemetry, and clear ROI visibility. In BFSI, for instance, the focus is on measurable productivity and risk optimization—JPMorgan reports over 175 AI use cases in flight and a ~30% reduction in servicing costs.
Contact centers are another major front, with vendors like Genesys and Five9 launching “AI agent studios” to automate both customer-facing and back-office operations. In IT operations and cybersecurity, platforms such as ServiceNow now deploy autonomous agents to speed up incident response. In manufacturing, predictive maintenance agents are driving significant savings—Siemens Senseye, for example, detected an impending pump failure early, preventing downtime and saving a six-figure cost.
Q: What cybersecurity risks or governance concerns do you foresee with Agentic AI deployments?
A: Key risks include prompt injection and tool-based data exfiltration, over-permissioned agents chaining unintended actions, supply chain and provenance gaps, runaway loops leading to cost or DoS issues, and weak auditability.
Mitigation involves implementing OWASP GenAI controls (sandboxing, safe tool schemas, response validation), conducting MITRE ATLAS-based threat modeling, and aligning governance with NIST AI RMF 1.0 and the NIST Generative AI Profile (AI 600-1). For multinational operations, map controls to the EU AI Act timeline—with prohibitions and literacy mandates effective Feb 2, 2025, and GPAI obligations from Aug 2, 2025. Enforce least-privilege IAM, human-in-the-loop (HITL) for irreversible actions, signed logs, and red teaming before production rollout.
Q: One piece of advice you’d offer to CIOs planning to integrate Agentic AI into their digital roadmap?
A: Begin with “controlled autonomy” in two or three high-volume, moderately complex processes. Design a loop of goal → planner → tool use with guardrails → reviewer, ensuring each component is bound by least-privilege IAM, sandboxing, and kill switches.
Set up an evaluation harness—including offline tests and canary runs—to measure cycle time, manual interventions, error rate, cost per task, and safety incidents. Align governance with NIST AI RMF and NCSC/CISA secure AI guidelines, and ensure EU AI Act compliance if operating internationally. Treat agents as products with defined owners, SLOs, runbooks, and post-incident reviews. Scale only after achieving proven, sustained performance gains, not based on demos or slideware.
Leader Uncovered:
Education: Ph.D. in Computer Science (AI); Harvard Business School, Advance Management Program.
Certifications: Microsoft Regional Director; Microsoft MVP (AI) Alumnus.
First Job: A web development Startup
Strengths & Weaknesses: Systems thinker; crisp decision‑making; builds durable platforms. Weakness: low tolerance for hand‑waving—insists on data and runbooks.
Biggest inspiration: Academics and Practitioners who make AI usable and safe
Passion/Hobby: Travel, robotics, mentoring STEM students, cricket, hiking, kayaking, running, skiing
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