Observability in the agentic era: What’s breaking, and how teams are fixing it

Fewer than 1 in 10 enterprise applications are fully observable, according to a 2026 report from consulting firm Neurones IT. This statistic points to a long-brewing problem: Traditional observability processes weren’t designed for the complexity of today’s AI-powered workflows.

Consider an AI-powered travel assistant where response times suddenly spike. Traditional observability tools might flag increased latency at the service level, forcing teams to manually sift through logs, traces, and dashboards to determine whether the issue stems from the model itself, a downstream API, or an agent making inefficient tool calls. This reactive approach can leave teams guessing, especially in fragmented and dynamic modern IT environments.

“More and more customers don’t want a dashboard. They don’t want something to tell them, ‘Hey, this is how your system is behaving,’” Vikram Murali, VP of Product Development for IBM Automation, said on IBM’s AI in Action podcast. Instead, they want to know, “What actions can I take to make the system perform better?”

Beyond static recommendations, ITOps teams need solutions that can self-predict and self-adjust—anticipating errors and downtime in advance and autonomously responding to them. Because agentic AI can continuously evaluate system behavior, performance and context, teams can move beyond surface-level signals to understand root causes, investigate symptoms and identify dependencies with limited human oversight. Together, these capabilities can cut ITOps costs by up to 35% due to reduced human effort, downtime and operational overhead, according to the Neurones study.

However, AI integration also introduces new operational challenges, and there’s a widening gap between AI-powered capabilities and teams’ ability to monitor and shape agentic behaviors.

In response, ITOps teams are redesigning their observability processes from the ground up, deploying agents to optimize resource usage, automate tasks and proactively spot errors—all while grappling with AI’s distinct governance, interpretability and data challenges. AI-powered monitoring tools can accelerate agentic processes, while agents, in turn, can enhance observability—together contributing to a more agile, efficient and secure IT environment.

Security teams receive on average 4,500 alerts each day yet are only able to respond to roughly a third of them, leaving organizations vulnerable to attacks and misalignments, according to a report from cybersecurity platform Vectra.

But excessive alerts can be a symptom of a larger problem. Microservices, hybrid architectures and distributed systems can overwhelm traditional monitoring mechanisms, and many organizations struggle to separate signal from noise. Agents exacerbate this challenge by introducing entirely new datasets (such as end-to-end decision trees, tool interaction logs and memory usage metrics) that teams must collect and analyze.

Further complicating observability efforts, data from AI sources is often harder to decipher: AI models can generate inconsistent or inaccurate data (hallucinations) or hide outputs behind opaque decision trees (the black box problem). Models can also unintentionally reveal sensitive information (data leakage), which can be a concern for enterprises in highly-regulated industries, such as healthcare and finance. As a result, a 2025 IBM Institute for Business Value (IBV) study found that 45% of executives cite lack of visibility as a major roadblock to agentic integration.

These visibility gaps can result in compliance risks, leaving teams unable to maintain a detailed, auditable record of agent behaviors. Nearly 70% of executives expect their company will face a regulatory fine related to GenAI integration, according to the IBV, suggesting that internal governance frameworks haven’t kept pace with multi-agent workflows.

LLMs and other AI tools can also place strain on budgets. Teams’ token usage can vary widely from one month to the next, making it difficult for observability tools to anticipate demand. And because AI vendors routinely upgrade, retrain and redeploy models, IT teams must repeatedly reconfigure observability pipelines and building environments to accommodate new releases.

While LLMs add complexity to observability processes, they can also be part of the solution. By equipping observability platforms with agentic capabilities, organizations can not only respond to the challenges of enterprise AI adoption but also advance their monitoring capabilities beyond what’s possible with traditional observability tools. Agentic observability tools can support and improve IT performance, resiliency and security by:

• Traditional observability tools send alerts based on predefined traffic, memory, latency and error rate thresholds.
  
  
• This approach often results in alert fatigue, where IT and security teams are bombarded with notifications and can no longer distinguish noise from urgent threats.
  
  
• By analyzing historical data and evaluating the context surrounding an event, AI can surface relevant, high-priority events while suppressing non-urgent notifications.

• AI-powered observability tools can provide context-rich root cause analyses that detail why an incident occurred, how it affected dependent services and how to prevent similar disruptions in the future.
  
  
• AI-driven solutions can dynamically suggest automations and repairs based on real-time conditions, with human developers providing guardrails and oversight.
  
  
• Together, these features reduce operational toil and lead to faster mean time to repair (MTTR).

• Traditional observability tools can struggle in modern IT environments, where errors rarely stem from a single failure and might instead involve complex interactions between multiple services and automations.
  
  
• Because agentic platforms are designed to spot patterns and reason across disparate architectures, they are often better-suited for interpreting model-specific metrics, such as model drift, response quality and token usage.
  
  
• End-to-end visibility also enables AI-powered observability tools to anticipate downstream impact and dynamically scale resources.

• AI-enabled observability tools can improve end-to-end performance and optimize resource usage by contextualizing traffic routing, CPU availability, throughput and other variables.
  
  
• Advanced platforms can also perform automated remediation, reducing operational strain and accelerating troubleshooting timelines.
  
  
• AI-powered observability platforms can track token spikes, tool calls and other AI-specific metrics to ensure that LLMs remain performant and usable for both customers and internal teams.

The future of observability might be defined as much by a strategic shift as a technological one. As observability responsibilities shift from mere data collection to decision support, and finally, to preemptive action, organizations are responding by:

AI observability can support a higher-quality user experience, where customers can trust that services will run as expected, with minimal latency, predictable behaviors and fast and transparent error resolution.

IT teams and developers, in turn, can dedicate more time to product innovation and high-level optimization. Rather than being less involved in the observability process, humans obtain a deeper understanding of system behaviors and are empowered to make smarter decisions to improve security, uptime and performance.

Register for the “Observability for AI and AI for Observability: From Hype to Hands-On” webinar