How agentic ITOps overcomes observability tool gaps
As enterprise ITOps teams monitor increasingly complex, cloud-based, containerized systems, traditional observability practices are struggling to keep up. As IT infrastructure complexity increases, the typical response is to layer on more monitoring, logging, and instrumentation.
However, this approach is failing to deliver the desired results. Despite investing billions annually into observability and monitoring tools, enterprises are still struggling to identify issues before they cause outages. The average BigPanda customer uses more than 20 observability and monitoring tools. Still, the sheer volume of alerts these tools produce, combined with siloed data and workflows, makes it extremely difficult to identify vital, actionable alerts manually. In fact, more than half of the organizations report an alert actionability rate of less than 20%.
The Monitoring and Observability Tool Effectiveness for IT Event Management report reveals a disconnect between the belief that comprehensive observability coverage (high event volume) equates to more actionable alerts and proactive incident response.
Additionally, while observability and monitoring are critical, they cannot automate tasks, reduce alert noise, prioritize alerts, or automate incident resolution responses. That’s where agentic ITOps platforms come in. According to ESG, 55% of organizations use AI alongside their observability tools to streamline IT incident management by automating tasks and managing alerts.
Agentic IOps enhances the value of observability investments by analyzing data to spot unusual patterns, enabling IT teams to focus on top-priority issues. It also provides early alerts for potential problems and suggests proactive solutions. In short, agentic ITIOps makes observability, monitoring, and event management more efficient.
In this article, we’ll examine how agentic ITOps complements observability tools to dramatically enhance the effectiveness of observability tools dramatically.
Understanding the key objectives of observability tools
Observability tools help you understand the internal state of IT systems by analyzing the data they produce. As modern IT environments become increasingly complex, observability provides the visibility needed to keep operations running smoothly. Instead of waiting for problems to happen, IT teams can proactively identify issues, understand their causes, and resolve them before they impact users.
Observability relies on three main components:
Logs capture event data,
showing details of specific actions within an application or system.
Metrics provide quantitative data points to track performance,
such as CPU usage or memory consumption, providing a high-level overview.
Traces follow requests as they flow through services,
revealing dependencies and bottlenecks in distributed systems.
Observability plays a critical role in quickly identifying the root cause of issues in distributed IT environments. Combining data from logs, metrics, and traces gives you a complete picture of your system’s health, making troubleshooting faster and more accurate.
However, observability alone has limitations. It shows what’s happening but doesn’t automatically fix issues or prevent incidents. Acting on observability still requires strong analytical skills and tools. It also doesn’t cover areas like security or compliance on its own. You should combine observability with other strategies to achieve a complete IT solution.
How agentic AI helps overcome observability tool gaps
When incidents occur, no single observability and monitoring tool can provide the full context of what is happening. Observability teams struggle to gain visibility into services and applications across the IT stack. In contrast, operations and incident response teams face a relentless deluge of noisy, unactionable alerts and contextless tickets that require significant manual effort to understand.
Agentic AI-powered ITOps fills the gaps of observability and monitoring tools. Agentic AI can centralize all observability and monitoring data into a single platform and enrich alerts with vital context to fill in the missing pieces, giving responders a full view of what’s happening.
Agentic IT operations from BigPanda use AI to detect, respond to, and prevent IT incidents at machine speed.
When paired with observability tools, Agentic ITOps provides complementary capabilities to support observability, process large data volumes, identify irregularities, and automate responses. As a result, you get a better perspective on system performance and potential issues, helping maximize uptime, improve alert actionability, and minimize downstream incident impact.
By applying agentic ITOps to their observability and monitoring tools, enterprises can:
Ingest, normalize, and contextualize ITOps data
BigPanda ingests and normalizes data from observability tools into a cohesive, unified view, which reduces noise. Agentic ITOps platforms can enrich incidents with additional data from sources such as CMDB, change, and topology, providing more context for end users.
Eliminate blind spots and drive smarter insights and actions
Observability and monitoring tools, while essential, provide only one view of what’s happening in your environment. Designed to enable an AI-first data strategy, The BigPanda IT Knowledge Graph continuously ingests and connects data from across fragmented systems and silos to build an intelligent model of your IT environment. This allows enterprises to evolve from reactive IT operations to proactive, agentic AI-powered decisions.
Agentically automate L1 workflows to accelerate detection, triage, and resolution
Agentic triage uses AI agents to instantly gather and analyze relevant data from various sources, streamlining manual validation and triage tasks that bog down the incident response process for L1 teams.
By automatically gathering and summarizing relevant incident information from multiple data sources, agentic ITOps platforms can deliver end-to-end visibility and dramatically accelerate incident detection, triage, and resolution.
How agentic ITOps complements and enhances observability tools
Agentic ITOps provides complementary capabilities that enhance observability tools and improve the efficiency of IT operations. Observability tools provide system visibility by collecting metrics, logs, and traces, but they generate excessive alert noise. Alert fatigue is a serious problem; ITOps teams often face an overwhelming volume of notifications, many of which are false positives or low-priority alerts. In this noisy, chaotic environment, your teams can easily miss critical issues, leading to system failures or prolonged downtime.
Agentic ITOps platforms can significantly reduce alert noise. Most (82%) of the BigPanda customers included in the study achieved at least 97% noise reduction, while more than half reduced noise by 99.5–99.9%. This statistic shows the effectiveness of agentic AI-powered ITOps platforms for filtering, deduplicating, and correlating events.
By filtering out non-essential alerts, your teams can identify critical issues faster and reduce response time by up to 50%. Your organization benefits from reduced downtime, a more resilient IT infrastructure, and happier customers.
AIOps also enhances the overall observability strategy by providing insights and suggesting optimizations to improve the effectiveness of observability platforms. Agentic ITOps can also pinpoint IT changes that cause incidents—a major cause of downtime in today’s complex hybrid IT environments—helping IT teams resolve issues faster.
Accelerating root cause analysis:
Analyze your IT environment and identify the root cause of issues, accelerating troubleshooting and mean time to resolve (MTTR), and reducing downtime.
Providing advanced analytics:
BigPanda Unified Analytics includes multiple dashboards to improve actionability and help ITOps and DevOps teams to anticipate future issues based on historical data and address problems before they affect system performance.
Contextualizing data:
Enrich observability data by combining it with other sources, like CMDB and topology data, to improve actionability.
Helping consolidate observability tools:
BigPanda unifies fragmented observability and monitoring data so you can assess tool performance using metrics like alert quality, noise reduction, and more. You can compare tool productivity, identify gaps and overlapping coverage, and evaluate their relative value to the organization.
By combining agentic ITOps with observability, IT teams gain the intelligence and automation needed to proactively manage complex IT environments, improve performance, and reduce downtime.
Learn more about how agentic ITOps bridge the gaps in observability and monitoring tools
Agentic ITOps leverages advanced generative AI and machine learning to enhance observability by analyzing system data, identifying anomalies, and enabling automated responses. This helps IT teams monitor system performance and catch issues early.
Combining agentic ITOps with observability tools provides enterprises with greater visibility, enabling them to solve problems and improve operations.
The BigPanda agentic IT operations platform ingests alert data from observability and monitoring tools, normalizes it, and enriches it with operational, contextual, and topology data from available CMDBs. Our platform delivers accurate, up-to-date, real-time visibility into your applications, services, and infrastructure while reducing noise, correlating multi-source alerts, and enabling powerful workflow automations.
To learn more about the value agentic ITOps brings to observability and monitoring investments, you can check out our latest e-book linked below or schedule a demo to see BigPanda in action today.
