Agentic AI has dominated industry conversations since 2025. Adoption across enterprises is on the rise, and investment plans remain aggressive. Use cases are expanding rapidly, from simple employee assistants to advanced agentic workflows for insurance renewals or support ticket triage, to name just a few. The shift has been undeniably fast.
The momentum notwithstanding, a question remains: Are AI agents actually delivering value?
Commonly used productivity metrics — like theoretical hours saved or daily active users — often fail to capture the reality of agentic AI. To understand if an agent is truly pulling its weight, we have to examine six specific signals of maturity.
1. Velocity
The first signal of value is the reduction in end-to-end workflow. A model that responds in milliseconds is useless if it doesn’t shorten the actual business cycle. In a retail engagement, for example, we ignored standard response rate and measured the “time to first meaningful response.” Similarly, for a non-profit client, we looked beyond how fast the agent could ingest documents and focused on the time taken to assess project viability and impact.
2. Accuracy
Efficiency gains cannot be at the expense of accuracy or decision quality. If an agent hallucinates, users will simply stop using it. For an insurance client automating the brokering process, we validated accuracy by running agents in parallel with human brokers over an extended period. We moved to production only after comparing outcomes side by side and confirming that the agent’s decisions consistently matched human judgment.
“Correctness of output” apart, accuracy is also about when an agent should intervene versus defer to humans. In an SRE incident-response implementation, early versions of the agent were technically correct, but the agent escalated too many minor issues, leading to alert fatigue. This required refining the metric to focus on escalation precision.
(innni/Shutterstock)
3. Cost Per Successful Outcome
As LLM costs become a visible line item, we have to look at the unit economics of the agentic solution. A busy agent can still be a money pit if it isn’t solving problems. That means shifting the lens to cost per successful outcome. Tracking this will help enterprises assess actual effectiveness and identify spending on failed or abandoned tasks. In a retail support deployment, for instance, tracking the cost per resolved interaction gives a much clearer picture of ROI than simply measuring the total number of conversations the agent handles.
4. Satisfaction Score
A frustrated user is a churning user. If the agent creates friction, people naturally revert to manual channels, and the expected efficiency gains will evaporate. In high-volume retail scenarios, we treat abandonment rates and repeat contacts as the real indicators of satisfaction. They tell you if the user is getting value or just giving up. In one insurance implementation, we noticed that while the agent was technically accurate, employees were double-checking its work. Identifying this early allowed us to address the issues that were silently blocking adoption.
5. Trust And Explainability
Explainability is a prerequisite for AI adoption in regulated and high-stakes environments. We advocate for “Glass Box AI,” where every autonomous agent has a clear provenance and can show the work and data lineage used to reach a decision. For example, while using agents as digital assistants for SRE engineers, teams value a clear, operator-friendly rationale behind recommended actions, especially during post-incident reviews. We saw the same requirement with an insurance broker. It wasn’t enough for the agent to generate a quote. The system had to link every output back to a specific business rule or policy document to prove its accuracy.
(Shutterstock AI Image)
6. Compliance and Risk Posture
The biggest barrier to production often isn’t the model’s capability, but the organization’s risk tolerance. There is an inherent data risk in feeding sensitive information to a third-party model, and an operational risk that it might violate policy. We see many agents that look perfect in a proof-of-concept fail to launch simply because the business cannot accept the liability of a stochastic system. If one cannot bound the risk of a wrong decision, the agent should stay in the lab.
Agents as “Digital Workers”
Ultimately, adopting these maturity signals forces a fundamental shift in perspective. Since agents operate autonomously, they effectively function as “digital workers.” They require the same onboarding you would give a human employee: defined territories, strict boundaries, and active supervision. This is effectively an exercise in capturing tacit knowledge: teaching the agent the unwritten ways of navigating the business environment. To scale this safely, enterprises need an “Agent Bus” or a coordination layer where agents register, obtain permissions, and have their actions monitored by supervisory agents. As humans step back from execution to oversight, this layer becomes the difference maker.
About the Author: Arun Kumar Ramchandran (Rak) is the CEO of QBurst, a ‘High AI-Q’ digital engineering company. Rak brings 25 years of leadership experience in technology driven digital transformation and consulting. He is particularly focused on emerging technologies and AI, and their role in shaping the future of business.
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Author: Arun Kumar Ramchandran
