In an opinion piece published in BigDataWire, CEO Arun ‘Rak’ Ramchandran challenges the prevailing wisdom surrounding the true value of AI agents within enterprises.

Rak argues that traditional metrics, such as “hours saved,” are insufficient for evaluating Agentic AI and proposes that enterprises must instead monitor six specific maturity signals to ensure true ROI:

• Velocity: True value isn’t measured in millisecond response times, but in the reduction of the end-to-end business cycle.
• Accuracy & Precision: AI hallucination is a liability. Success requires knowing exactly when to act and when to defer to a human.
• Cost Per Successful Outcome: Enterprises must move past total conversation volume to track unit economics, identifying the cost of resolved tasks versus abandoned ones.
• Satisfaction Score: Low friction is the key to adoption. Monitoring abandonment rates reveals whether users trust the agent or are silently reverting to manual work.
• Trust and Explainability: Adopting a “Glass Box AI” approach ensures every decision has a clear data lineage, which is essential for regulated industries like insurance.
• Compliance and Risk Posture: An agent’s performance in the lab matters less than the organization’s ability to bound the risk of its decisions in a production environment.

Rak concludes that for these agents to scale, they must be treated as “digital workers” rather than mere software tools. This requires a formal onboarding process—defining boundaries and capturing tacit knowledge—supported by an “Agent Bus” coordination layer. This architectural foundation allows humans to move from execution to oversight, ensuring that autonomous agents remain compliant and effective.

Read the full article on BigDataWire.