Agentic AI and KPIs: What Do You Measure When AI is Working?

As artificial intelligence becomes more and more autonomous making decisions, taking decisions, and optimizing processes with fewer and fewer human touches, we’re stepping into a new paradigm for measuring performance. Agentic AI, or systems that act with some amount of autonomy and goal-orientation, disrupts our deeply ingrained assumptions about measures of business success.

In fact, AI agents are already improving developer productivity by doing repetitive coding tasks and according to McKinsey, AI could automate up to 30% of working hours by 2030 and leave developers free to address more complex problems and focus on innovation.

If AI is not just assisting but acting , then naturally the question comes up: What do we measure when the AI does the work?

The Shift from Output to Outcomes

Traditional Key Performance Indicators (KPIs) were built around human behavior: calls made, time on task, units produced, leads generated. These were effort and efficiency proxies. But agentic AI does not measure time on task, it optimizes outcomes, learns from feedback, and adapts in real time.

Take customer support. In a human-driven model, you’d measure average handle time or tickets resolved per agent. But what happens when an AI handles 80% of support queries autonomously, 24/7, and constantly improves based on customer sentiment?

In the world of agentic AI, the output is less important than the effect.

New KPI thinking looks more like:

• Customer friction reduced
• Speed to resolution
• Learning velocity (how fast the AI improves)
• Net impact on revenue or satisfaction

One of the leading e-commerce websites installed an agentic AI in their help center. Rather than tracking agent productivity, they began tracking “frictionless resolution rate” the proportion of queries resolved with no human effort needed. This metric served as a north star to improve customer experience over the course of 12 months and resulted in a 20% reduction in churn.

Also, in communications, AT&T leverages AI to manage network operations and tailor service through autonomous chatbots. The outcome? A 15% decrease in operational expenses, fueled by AI acting independently throughout touchpoints.

Measuring the Behavior of the System, Not Just the System’s Results

Agentic AI has a degree of self-governance. It’s not merely carrying out instructions, it’s selecting routes to achieve objectives. That means transparency and traceability are crucial.

Emerging KPIs must account for:

• Decision Quality: Did the AI make optimal choices given the data available?
• Autonomy Levels: How independently did the AI act? Did it escalate where needed?
• Alignment with Goals: Was the AI’s behavior aligned with business values and outcomes?

We’re not just looking at what the AI did, we’re asking why and how it made decisions.

The Rise of Meta-KPIs

To manage AI effectively, we also need KPIs for the KPIs,meta-metrics that tell us whether we’re measuring the right things. As AI becomes increasingly accountable, we need to monitor what we’re measuring, not the results. These meta-KPIs are a feedback loop for the KPIs themselves, so that we don’t optimize the wrong behavior. These might include:

• Metric relevance over time (are your KPIs still aligned as AI evolves?)
• Bias detection (are your KPIs inadvertently optimizing the wrong behaviors?)
• Impact scope (is the AI creating value across functions or narrowly improving one metric at the cost of others?)

Agentic AI can optimize to the letter, but not the spirit of a goal. Meta-KPIs steer before that happens.

Human-AI Collaboration KPIs

Even in highly automated environments, the human doesn’t disappear, roles adapt. Individuals now watch over, manage, and audit agentic systems. New KPIs will need to measure the effectiveness of human–machine teamwork.

Consider:

• Intervention frequency: How often does a human need to step in?
• Escalation effectiveness: When AI flags a case, does it matter?
• Trust metrics: Do users trust AI decisions in practice?

What Does Success Look Like in an AI-First Company?

In AI-powered organizations, success is not just about optimizing human productivity, its about building smart systems that produce scalable, sustainable outcomes.

Future KPIs will likely be:

• Model adaptability rate (how quickly can your AI respond to new data?)
• Scenario handling breadth (how wide a range of situations can the AI handle?)
• System sustainability (resource use, compute cost, carbon footprint)
• Ethical alignment (fairness, transparency, and compliance built in)

Success isn’t just that the AI isn’t just getting things done but doing the right thing, in the right way, at scale.

Final Thoughts: Measuring in the Age of Machines That Think

As systems become more agentic, we need to rethink what we’re measuring not because measurement no longer applies, but because measuring the wrong things can mislead the system (and the business).

Agentic AI calls for KPIs that go beyond activity and focus on purpose, outcomes, learning, and alignment. It’s no longer just about what we’re doing, it’s about what we’re enabling.

How Portera Can Help

Here at Portera, we help companies reimagine performance systems in the age of AI. We work with you not only to roll out agentic systems but have them governed by KPIs that are connected to your true goals; measurable, moral, and designed for a brighter future.