Companies Reengineer Workflows for Agentic AI Orchestration

Camunda launched ProcessOS at its CamundaCon conference in Amsterdam May 19–21 to reengineer its quote-to-cash workflow and support agentic AI orchestration. The company said it used ProcessOS to shorten the quote-to-cash flow, reduce errors and free roughly 6,000 person hours after the process was shortened from about five hours per deal. The event drew about 1,100 attendees.

CTO Daniel Meyer described ProcessOS as an “agentic operating system” that reengineers workflows and continuously optimizes them for AI-driven operations. CFO Clemens Morgenroth provided the 6,000-hours figure and linked it to faster cycle times and lower error rates.

Barclays presented a live pilot showing agentic workflows for customer onboarding and client due diligence. Lily Wang, CIO for wholesale client onboarding and group financial crime at Barclays, told attendees, “ProcessOS tackles the real reason AI adoption stalls in large enterprises. We can’t build tomorrow’s process using only what we know today. Transformation stalls.” Gautam Verma, head of financial crime core platforms, outlined a three-agent design: one agent collects data from multiple sources, a second evaluates applicable policies and procedures, and a third executes policy-driven actions. Verma said the orchestration layer that governs those agents is deterministic to manage multiple internal systems, handoffs and compliance requirements.

A May Forrester report on adaptive process orchestration found a shift from task-level automation to enterprise-scale process orchestration. The report described vendors consolidating automation tools into orchestration backbones that combine process intelligence, modeling, execution, monitoring and unified data foundations. It also noted increased emphasis on governance, auditability and hybrid execution models that support event-driven automation and human-in-the-loop controls.

Camunda and its customers said ProcessOS centralizes workflow design and governance while allowing AI agents to perform targeted tasks. Company executives emphasized the need for human approval gates and clear audit trails when agents interact with customer data and compliance processes.