Splunk State of Observability 2026: Avoiding the Hidden Costs of AI Agents

News Summary

• The global Splunk State of Observability 2026 report reveals agentic AI is creating unpredictable outcomes and costs, leaving observability practices to rein them in.

• Agent observability is increasingly important, with nearly 9 in 10 respondents saying monitoring AI is a priority, but over half still can't effectively measure agent quality.

• The AI cost crisis isn't just the size of the bill. It's about the surprise charges, with over half saying they cannot accurately control AI costs.

• Shared data across observability and security teams is essential to AI trust, with more than three quarters of respondents saying monitoring AI has increased their collaboration with security teams.

SAN JOSE, Calif., October 20, 2026 - Cisco (NASDAQ: CSCO) today released the Splunk State of Observability 2026: Restoring Predictability in the AI Era report, marking its sixth analysis of the observability industry. This year's research, which surveyed 2,050 ITOps and engineering respondents across 10 countries, exposes a significant disconnect: the urgency to deploy AI agents is rapidly outpacing the ability to monitor them.

With AI costs becoming more volatile, organizations are struggling to maintain both agent quality and fiscal predictability. This year's report highlights how observability is helping to restore stability and financial predictability of agentic AI systems, providing the governance to build trust with agents and ensure AI spend is tethered to business value.

In addition, with 83% of respondents saying they're concerned about the unintended consequences of AI agents, there's an urgent need for "agent observability," or the ability to monitor AI agents and put guardrails in place. This need is driving ITOps and engineering teams to collaborate more closely with security teams, as agents gain access to the entire tech stack.

"Most companies will soon have far more agents at work than people," said Jeetu Patel, President and Chief Product Officer at Cisco. "Managing agents at scale requires reimagining both security and observability. Have our agents been compromised? Are they doing their job well? In the world of agentic AI, these questions are inseparable, and they're moving at machine speed. This report shows that observability and security data are converging, and that teams need to collaborate more than ever. Common data platforms that help us detect and respond to issues in real-time are the key to deploying agents we can trust at scale."

"For today's enterprises, the excitement of AI is clashing with the reality of the bill," added Kamal Hathi, SVP and GM, Splunk at Cisco. "We believe observability is the essential bridge to restoring predictability and confidence to the agentic AI era. By providing the necessary guardrails and incorporating observability into an AI tokenomics framework, leaders can transform AI from an unpredictable expense into a measurable and strategic long-term business investment."

Agent observability emerges as a business priority and a gap

As organizations add AI agents into workflows like software development and customer experience, an incorrect agent action can evolve into a critical business failure. Unlike monitoring traditional applications, visibility into AI agent behavior requires a new class of behavioral metrics that are tied to business outcomes and agent decision-making. While many see the value of agent observability technology, respondents still struggle with how to execute or measure the tools effectively.

• 87% confirm monitoring AI is a priority for their organization; however 54% don't have clear business outcomes tied to it.

• 56% reveal they're unable to measure AI workload metrics like LLM response quality, a core metric to tracking the success of an AI agent.

• While 85% say it's important to have visibility into AI hallucinations, only 58% claim they're effective at doing so.

AI is driving unpredictable costs

Observability is what makes AI spend visible, attributable, and controllable. When today's organizations run dozens of agents across multiple models, AI costs and usage can become convoluted. In addition, AI is exacerbating a data volume problem for many enterprises, making sifting through mountains of data almost impossible.

• 55% say they can't control AI costs, as AI use has become more complex with both consumption increasing and usage remaining hard to predict.

• 60% say unexpected billing spikes cause more operational strain than high baseline costs.

• 57% say their telemetry costs eclipsed the cost of the infrastructure being monitored, making financial predictability more critical than ever.

• 67% of respondents use a single observability platform, providing a unified view to pinpoint exactly where telemetry costs are coming from.

The convergence of observability and security ensures AI trust

As AI agents gain more access to operations in every layer of a business, monitoring them requires collaboration between ITOps, engineering, and security teams, breaking down walls that have historically kept them apart.

• 81% say monitoring AI workloads has increased the collaboration between observability and security teams, and 73% can accurately trace performance issues to security root causes, up from 65% last year.

• More observability teams are troubleshooting (77%) with security teams, up from 62% in 2025. Data sharing between teams has climbed from 74% to 84%.

The leader's edge: restoring predictability in the agentic era

In the research, an advanced group of organizations emerged as leaders who are achieving concrete business outcomes with their observability practices. These leaders follow three best practices:

1. Rely on AI agents to fix and prevent issues frequently. Leaders use agents to perform and execute key remediation tasks often.
2. Closely monitor the factors that keep AI in check. Leaders focus closely on monitoring model quality, cost, usage, security, and privacy.
3. Connect signals to business impact. These advanced respondents agree that telemetry is a key factor in their planning process and helping to shape strategic decisions.

These leaders are seeing transformative results - they are far more likely to accurately attribute AI costs (91% of leaders vs. 69% of non-leaders), by pinpointing which agents, models, and workloads drive spend. They are also 2.4 times more likely than non-leaders to say the ROI of their observability platforms significantly exceeds expectations, and 54% say observability has yielded at least $5 million in annual business revenue (versus 28% of non-leaders). Leaders are realizing the benefits of observability and security teams working closely together - with 94% saying monitoring AI has increased collaboration with security teams (compared to 80% of non-leaders,) and 86% are more likely to accurately trace performance issues back to their security root causes (in contrast with 71% non-leaders).

Global highlights

This year's report showed the topics around prioritizing monitoring AI agents, unpredictable costs, and concern of agentic AI remained key themes of the 10 countries surveyed. Indian organizations stand out as advanced for their agent observability capabilities, with 51% calling their practice very effective at monitoring AI model quality, in contrast to 35% globally. The UK reflected both impressive adoption and concern of agentic AI, with 32% using AI agents all the time to diagnose root cause (vs. 20% globally), and 55% strongly worry about the unintended consequences of AI agents. Saudi Arabia stood out as more advanced and AI-forward, with 98% saying AI enabled them to focus more on innovation, well above the global average at 84%. Germany leads in data sensitivity, with 74% rating visibility into AI privacy as very important.

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Methodology

Oxford Economics researchers surveyed 2,050 ITOps and engineering professionals from practitioners to executives (including developers, SREs, systems engineers, infrastructure operations professionals, CTOs, and CIOs) from February through March 2026. Respondents resided in Australia, France, Germany, India, Japan, New Zealand, Singapore, Saudi Arabia, the United Kingdom, and the United States. Respondents represented 16 industries: business services, construction and engineering, consumer packaged goods, education, financial services, government (federal/national, state, and local), healthcare, life sciences, manufacturing, technology, media, oil/gas, retail/wholesale, telecom, transportation/logistics, and utilities.

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