The Hidden Costs of AI Agents: Why Observability Is Becoming Essential in the AI Era

Artificial intelligence agents are rapidly transforming how organizations operate, helping automate workflows, improve customer experiences, accelerate software development, and support business decision-making.

However, as enterprises race to deploy increasingly sophisticated AI systems, a new challenge is emerging: managing the unpredictability, costs, risks, and performance of these autonomous agents.

According to Cisco's Splunk State of Observability 2026: Restoring Predictability in the AI Era report, businesses worldwide are struggling to balance the promise of AI with the practical realities of monitoring and controlling it. The study highlights a growing need for what experts call "agent observability" - the ability to monitor, measure, and govern AI agents effectively.

AI Adoption Is Outpacing AI Oversight

Organizations are increasingly deploying AI agents to perform tasks that were once handled exclusively by humans. These agents can assist with software development, customer service, troubleshooting, and operational management.

Yet many companies are discovering that while AI deployment is accelerating, visibility into how these systems behave is not keeping pace.

The research found that 87% of respondents consider monitoring AI systems a priority, but more than half lack clearly defined business outcomes linked to those monitoring efforts. Additionally, 56% say they cannot effectively measure critical AI workload metrics, such as large language model (LLM) response quality.

This disconnect presents a serious challenge. Unlike traditional software applications, AI agents can make autonomous decisions, interact with multiple systems, and influence business outcomes in ways that are not always predictable. Without proper visibility, organizations may struggle to identify errors, assess performance, or understand why an AI system behaves unexpectedly.

The Rise of Agent Observability

Observability has traditionally been used to help organizations monitor applications, networks, and IT infrastructure. In the age of AI, observability is evolving into something much broader.

Agent observability focuses on understanding not just whether an AI system is functioning, but how it is making decisions, whether its outputs are accurate, and whether it is delivering business value.

Organizations increasingly recognize the importance of monitoring AI hallucinations and model quality, though many still struggle to do so effectively.

This new discipline requires businesses to move beyond technical metrics and begin tracking behavioral indicators that connect AI actions directly to business outcomes. Such visibility makes it possible to evaluate whether AI agents are contributing to productivity, customer satisfaction, revenue growth, or operational efficiency.

AI's Growing Cost Problem

One of the report's most striking findings concerns the financial impact of AI adoption. The challenge is not simply that AI can be expensive. Rather, organizations are increasingly worried about unpredictable costs and unexpected billing spikes.

More than half of surveyed organizations said they cannot adequately control AI-related expenses. Additionally, 60% reported that surprise billing increases create greater operational strain than consistently high costs.

Key Factors Driving AI Cost Challenges

• AI agents often operate across multiple models and platforms.
• Usage patterns can fluctuate significantly.
• Growing AI deployments generate massive quantities of telemetry and monitoring data.
• Cost attribution becomes difficult when numerous AI services interact simultaneously.

For many enterprises, the volume of monitoring data itself has become a significant expense. In fact, 57% of respondents said telemetry costs exceeded the cost of the infrastructure being monitored.

These findings suggest that managing AI economics may soon become as important as managing AI performance.

Why Observability Matters for Financial Control

The report argues that observability is emerging as a crucial mechanism for financial governance in AI environments.

When organizations have clear visibility into AI activity, they can:

• Understand which agents and workloads are consuming resources.
• Track model usage and efficiency.
• Identify cost anomalies before they escalate.
• Align AI spending with measurable business outcomes.
• Make more informed investment decisions.

Observability effectively transforms AI spending from a largely unpredictable expense into a managed business investment. By connecting operational data with financial metrics, leaders gain better control over AI economics and can justify future AI initiatives based on measurable returns.

Security and Observability Are Converging

Another major theme emerging from the research is the growing relationship between observability and cybersecurity.

As AI agents gain access to business systems, applications, and data sources, the distinction between performance monitoring and security oversight becomes increasingly blurred.

Organizations must not only ensure that agents function correctly but also verify that they have not been compromised and are operating within approved boundaries.

The study found that 81% of respondents believe monitoring AI workloads has increased collaboration between observability and security teams. It also revealed rising levels of data sharing and joint troubleshooting between these functions.

This collaboration reflects a broader industry shift. AI costsTheir monitoring frameworks provide a comprehensive picture of AI operations.

3. They Connect Data to Business Impact

Leaders treat telemetry and observability insights as strategic planning tools rather than purely technical resources. Monitoring data helps guide investment decisions and business strategy.

The benefits are substantial:

• More accurate AI cost attribution.
• Stronger returns on observability investments.
• Greater measurable business value.
• Improved collaboration between observability and security teams.
• Faster identification of performance and security issues.

Global Trends Highlight Different Priorities

The research examined organizations across Australia, France, Germany, India, Japan, New Zealand, Singapore, Saudi Arabia, the United Kingdom, and the United States.

While concerns around AI monitoring and cost predictability were common across all regions, some countries demonstrated unique strengths.

• India stood out for advanced AI monitoring capabilities and stronger effectiveness in tracking AI model quality.
• United Kingdom organizations showed high levels of AI agent adoption alongside significant concern about unintended AI consequences.
• Saudi Arabia demonstrated particularly strong optimism regarding AI-driven innovation.
• Germany placed a high priority on AI privacy visibility.

These regional differences suggest that while AI adoption is a global phenomenon, the challenges and priorities associated with observability vary according to local business environments and regulatory expectations.

The Future: Trust Through Visibility

The rapid rise of AI agents is creating both extraordinary opportunities and significant operational challenges.

Organizations are no longer focused solely on deploying AI. Increasingly, they are asking whether AI systems can be trusted, measured, governed, and controlled.

The findings point to a clear conclusion: as AI systems become more autonomous, observability will become a foundational business capability rather than a purely technical/> In the coming years, the companies that succeed with AI may not be those that deploy the most agents, but those that can understand, monitor, and govern them most effectively. Observability appears set to become the key discipline that turns AI from an unpredictable experiment into a reliable and accountable business asset.