Three essentials for live agentic AI operations at scale

Done well, agentic AI becomes an engine for sustained business transformation, generating higher revenue, better customer experience (CX) and lower churn. Research by McKinsey found that businesses using agentic AI to deliver personalized omnichannel customer interactions achieve 15-20% higher customer satisfaction levels and 20-30% cuts in cost-to-serve. 

Yet sitting alongside that success is another reality, with statistics that reflect what our team at Konecta is witnessing in the CX space.

Data from Deloitte shows that while 38% of organizations are actively testing agentic AI, only 11% have agents successfully running in production. According to BCG, 60% of companies are generating little to no material business value from their AI investments. McKinsey's 2026 AI Trust Maturity Survey found that only around 30% of organizations have reached a mature level of agentic AI governance, with companies who assign explicit ownership and accountability scoring significantly higher on maturity than those who don't.

Put simply: while vendor demos and pilots go well, the journey to deploy, run and scale agentic AI is often much slower, riskier or more difficult to measure than expected.

The fact is that deploying a live AI agent is only the start of a journey which in large part depends on how rigorously agentic AI is managed after go-live. This is the challenge of "Day 2" operations. Can you see exactly what your agents are doing (visibility/observability). Do you understand exactly why they are doing it (explainability)? And can you be sure that it's correct and compliant (trust)?

Based on Konecta’s experience running millions of AI-powered CX interactions across multiple sectors every day, here are three critical success factors for managing agentic AI robustly from Day 1. 

The cost of AI computing and storage is an emerging issue. Right now, unpredictable cloud costs can be one of the fastest ways to lose executive confidence in an AI program. A recent study by Vanson Bourne (for Tangoe) found that cloud costs have increased by an average of 30% due to AI-related technologies, and 72% of leaders say their cloud spending is becoming increasingly unmanageable.

The fact is, not every task needs the most expensive available model. Many of our use cases show only a 1-5% performance difference between a smaller, cheaper model and a newer, pricier one. A +200% cost increase for a 5% quality gain is rarely a trade worth making. (While there are genuine exceptions where only a frontier model will do, these represent a distinct and very small minority.)

Without visibility into cost and quality together, organizations risk either overspending or missing an easy win where a small budget increase would meaningfully lift quality. Using built-in FinOps dashboards to track exact real-time token consumption and compute cost per model is part of the equation, the true value lies in tying that together with tracking the quality of the answers. That level of visibility means workloads can be dynamically routed to the most cost-effective model for the job, preventing budget overruns before they happen.

AI’s "black box" problems persist. Traditional monitoring was designed to measure IT platform health: latency, speed, throughput, resilience and so on. In contrast, an agent can look perfectly healthy using these metrics while confidently giving customers completely wrong answers. So observability for an agentic platform needs to operate on a different level entirely, using data that turns agentic AI from a black box into a system that can be improved in real time. 

Organizations may be concerned about closed-source AI models from vendors that aren’t explainable. These may sometimes behave unpredictably or inappropriately, either due to model upgrades or the nature of the training data. Externally, the governance landscape has evolved with a swathe of policies and laws with which organizations must comply. These include the launch of the UN AI Advisory Body, the signing of the AI Executive Order in the US, and the EU AI Act.

That’s why every AI decision should be traceable, logged and fully auditable. Things worth tracking include:

• Prompt effectiveness. A small wording tweak can produce a significant swing in performance, and a prompt that works well on one model may perform poorly on another.
• Accuracy and grounding. Did the answer match reality, and can the agent actually point to what it's based on?
• Hallucinations. An agent that produces confident nonsense will lose people’s trust fast and can create serious quality and reputational problems for the business.

Research indicates that despite growing customer comfort with AI in customer service, an overwhelming majority (87%) deem it essential to have the option to speak with a human agent when necessary.

The answer is to position AI as a genuine co-pilot for your frontline teams. Routine transactions can be fully automated. For anything more complex, AI can do the heavy lifting, such as synthesizing context and drafting a response, but a human should always be the one who hits ‘send’.

Think of the human role as a "trust anchor": setting boundaries, handling exceptions and ensuring that AI behaves in ways both customers and regulators can rely on. People’s value lies in their judgment, empathy, and the ability to step in exactly where automation reaches its limits.

Done well, this kind of continuous learning loop can push automation rates, while automatically routing emotionally sensitive or complex cases back to specialist human agents. 

The gap between pilot and production is actually about operating discipline. Organizations that treat Day 2 as an afterthought will keep re-running pilots. Those that build observability, governance and human oversight into secure enterprise-ready agentic AI from Day 1 will be the ones who move past that 11% currently running in production and stay there.

The true vision for agentic AI is a self-optimizing system: a feedback loop where every resolved, escalated, or refined interaction makes the next one better. Rich data on what works, brought together in one integrated view, is what turns today’s effective agent into tomorrow’s even smarter one. This ultimately shows up in the one metric that matters most: happy customers, many of whom will return again and again.

Ready to dive deeper? Discover our 3-step guide to scaling agentic AI in CX.