Bhagyasri Ghosh

Autonomous operations break down when exceptions have no defined path. Learn how retries, durable waits, escalation, recovery and terminal states set the safe unattended limit for AI-driven workflows.

AI agents can choose tools, change sequence and coordinate work without owning the entire workflow. See how durable process state, waits, retries and recovery stay explicit outside the agent loop.

Mixed automation can fail even when every human, bot and AI agent completes its task correctly. Learn how explicit process state, structured handoffs and current case facts help work continue reliably across different participants.

Generative AI can interpret emails, documents and conversations before deterministic workflow logic runs. Learn how to use AI around business rules without making thresholds, approvals and policy decisions harder to test or govern.

Process mining can reveal whether an automation candidate is genuinely repeatable, observable and stable. Learn how event logs expose variants, rework, weak data and fragmented paths before engineering begins.

Automation debt can grow even when bots continue to run successfully. Learn how to assess maintenance, exception work, fragile dependencies, ownership gaps and current business value across an enterprise RPA estate.

RPA, workflow engines and AI agents solve different automation problems. See how enterprises can combine predictable execution, durable process state and contextual decision-making without blurring responsibility.

Go-live does not end automation governance. This article explains how process owners, technical teams, operations and CoEs should divide decision rights so business rules, controls and workflows can change safely after deployment.