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VIDEO PODCAST

Workato's Bhaskar Roy on Agentic AI Trends and ROI

James Maguire

October 1, 2025

As AI agents move from buzzword to business tool, Bhaskar Roy, Chief of AI Products and Solutions at Workato, explains how agentic AI differs from legacy RPA, why governance and observability matter, and how enterprises can measure ROI quickly.

He outlines Workato’s new platform (Workato One), its enterprise-grade MCP implementation, and a library of prebuilt Genies that are KPI-oriented. Roy also shares a practical 30/60/90-day adoption playbook and a near-term outlook in which high-agency, outcome-driven agents take on core, cross-system business processes—not just lightweight tasks.

Core Takeaways

Agentic AI vs. RPA

RPA deterministically automates tasks; agentic AI adapts, reasons, and orchestrates toward business outcomes using available skills.

Governance first

Treat agents like software with version control, rollback, access controls, and full audit trails; align permissions to the human on whose behalf an agent acts.

Workato's stack

Workato One unifies agent building and orchestration; an enterprise MCP layer enables secure, governed agent-to-tool and agent-to-agent interactions; prebuilt Genies accelerate time to value.

Measuring ROI fast

Pick one KPI-tied use case for day 0–30, harden and scale policies by day 60, and enable multiple ops teams by day 90—after which adoption tends to go viral.

KEY QUOTES

How agentic AI differs from RPA

Think of RPA as deterministic task automation. Agentic AI is oriented around achieving a business outcome. Instead of hard-coding every decision path, agents adapt and reason about the best way to proceed, then execute using the skills you've provided. It's less 'script this step' and more 'reach this KPI.'

Security, governance, and treating agents like software

To run core, high-impact agents, governance is paramount. Set guardrails for what agents can and cannot do, apply software discipline—version control, rollback, SDLC hardening—and make every action auditable. Crucially, an agent should inherit the user's permissions: if I can only update certain records in Salesforce, the agent should only be able to update those, nothing more.

Workato One, enterprise MCP, and prebuilt 'Genies'

—one platform to build agents, orchestrate workflows, and provide the experience layer where people interact with them. We also brought enterprise-grade MCP so agents—on Workato or elsewhere—can operate with security, governance, scalability, and auditability. And to speed outcomes, we shipped 28 prepackaged, KPI-oriented 'Genies' that customers can deploy in weeks, accessible via Workato Go or tools like Slack and Teams.

ABOUT THE AUTHOR

James Maguire

Executive Director

An award-winning journalist, James has held top editorial roles in several leading technology publications, covering enterprise trends in cloud computing, AI, data analytics, cybersecurity and more. He regularly communicates with industry analysts and experts and has interviewed hundreds of technology executives.

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How agentic AI differs from RPA

Security, governance, and treating agents like software

Workato One, enterprise MCP, and prebuilt 'Genies'

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