The Holiday Season means it is time for APMdigest's annual list of predictions, covering Observability and other IT performance topics. Industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how Observability, AIOps, APM and related technologies will evolve and impact business in 2026.
APMdigest covers a variety of related technologies related to IT performance, and this year's predictions series offers an equally broad scope of topics. In addition to Observability and APM, the related technologies covered include AIOps, OpenTelemetry, DEX and User Experience Management, NetOps and more.
Some of these predictions may come true in the next 12 months, while others may be just as valid but take several years to be realized. Still others may be wishful thinking or unbased fears. But taken collectively, this list of predictions offers a timely and detailed snapshot of what the IT industry and the Observability market is thinking about and planning, expecting and hoping for 2026.
The predictions will be posted over the next two weeks, with separate lists of predictions for NetOps, Cloud, DataOps and AI to follow after the holidays. Meanwhile, DEVOPSdigest is posting a series of DevOps and development predictions for 2026.
A forecast by the top minds in Observability today, here are the predictions:
AGENT-FIRST OBSERVABILITY
Agent-first observability is the new norm, and context will become the new signal: In 2026, observability will shift to an agent-first model. We are moving beyond simply collecting data and are now enabling autonomous agents to begin decision making, leveraging unified signals across logs, metrics, and traces as the core context they will use to investigate and remediate issues. Platforms that deliver built-in context engineering, not just data collection, will define the next generation of observability and APM. The next year will mark a move from isolated logs, metrics, and traces to context-rich observability that is powered by agents that can automatically correlate signals.
Baha Azarmi
GM, Elastic Observability, Elastic
REPORT: The Landscape of Observability in 2026: Balancing Cost and Innovation
Driven by AI and cloud adoption, the global data sphere is expected to surpass 180 zettabytes of data created in 2025 alone, mandating a shift from reactive monitoring to AI-driven autonomous IT operations while keeping it human-centric. Gartner® predicts that by 2030, AI-native development platforms will enable 80% of organizations to transition from large software engineering teams to smaller, more nimble AI-augmented teams. Amid these industry shifts, it is imperative for IT operations teams to adopt AI-assisted practices in areas like observability to tune their IT stack to deliver a fast, stable, and secure experience.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine
ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring
The next wave of observability, or Observability 3.0, will be an AI-first experience, shifting the primary interface from complex dashboards to agent-driven analysis embedded directly within tools and workflows. Investigation and analysis will become the default for these agents, which will be supplied with data optimized for LLMs. This new paradigm is fueled by the continuous supply of optimized and refined streams of data.
Tucker Callaway
CEO, Mezmo
AI-READY BY DESIGN
"AI-first" operations will become the default for modern enterprises: Infrastructure and application operations will be designed as AI-first, not with AI added in later. That means observability data, logs, traces and metrics will be instrumented from the outset to feed ML models, with AI embedded in incident management, release governance and capacity planning. By 2026, if a new platform isn't "AI-ready by design," it will be considered a legacy investment on day one.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys
AGENTIC AIOPS
Organizations will see the first significant deployment wave of agent-based AIOps architectures in 2026, with specialized detection, triage, remediation, and governance agents coordinating under shared policy models. These multi-agent systems will boost decision accuracy and reduce operator burden by distributing tasks across purpose-built AI components. Success will depend on tuning reward functions, enforcing guardrails, and managing agent collaboration at scale. Organizations that master agentic AIOps will set a new benchmark for operational velocity and resilience.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)
AIOps will move from "assistive" to truly agentic: By 2026, AIOps will evolve from dashboards and recommendations to agentic AI that can autonomously diagnose, act and verify fixes across complex technology environments. Human operators will increasingly move into a supervisory role, approving actions, setting guardrails and handling edge cases, while multi-agent systems quietly execute most of the operational heavy lifting in the background.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys
AIOps is going to make a big move (automated incident resolution using AI), leading to more resilient modern DevOps.) The rise of AIOps is about to change DevOps through intelligent, agentic automation of incident management. Many organizations have already begun adopting AIOps tools — over half of IT teams now leverage AI in their observability stack, with almost half of those using it to automate root-cause analysis and incident remediation. In 2026, we'll see DevOps workflows augmented by AI "agents" that monitor systems, analyze telemetry, and resolve issues autonomously. Based on what we are witnessing, we can assume that the next generation of engineers will rely on a team of AI assistants collaborating to diagnose root causes and suggest fixes when problems arise. By offloading routine detection and resolution tasks to AI, organizations can achieve faster incident response and more resilient operations, with downtime minimized through self-healing systems. This proactive automation ultimately enables DevOps teams to focus on strategic improvements rather than firefighting issues.
Sam Suthar
Founding Director, Middleware
PREDICTIVE OBSERVABILITY
The 2025 State of Observability report by ManageEngine reveals that 67% of organizations prioritize visibility into distributed IT environments, and 56% aim for proactive, predictive issue resolution. Despite maturity challenges, 81% reported over 100% ROI from observability investments, underscoring its strategic value. In 2026 and beyond, large enterprises are likely to deploy unified observability and autonomous, self-healing AIOps systems to predictively manage their infrastructure, moving from firefighting mode to IT resilience. AI-enabled root cause analysis, generative AI summaries, and business-context enrichments, such as workflow automation, will help turn observability into a strategic operational IT asset.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine
By 2026, observability will evolve beyond dashboards and logs. Predictive AI will anticipate system behavior before issues occur, turning observability from a reactive process into a living, self-aware layer of infrastructure.
Dr. Hema Raghavan
Head of Engineering and Co-Founder, Kumo
In 2026, AIOps will move from reactive log correlation to true predictive observability. We'll see increased integration of causal AI models and neuro-symbolic reasoning in observability platforms, enabling them to not just detect anomalies but actually understand why they occur. This will reduce false positives and allow earlier, intent-aware remediation especially in complex microservice and multi-cloud environments. The most advanced AIOps tools will serve as tier-1 support agents, autonomously resolving repetitive incidents before human escalation is needed.
Ensar Seker
CISO, SOCRadar
AIOps will shift from noise reduction to business impact prediction: The first wave of AIOps was about filtering out excessive alerts and connecting related issues. The next wave will predict business impact, not just technical incidents.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys
DISCOVERING UNKNOWN UNKNOWNS
AI will go beyond analyzing metrics and doing simple correlation, to actually aiding discovery. By helping users dig further, into data and conditions they didn't anticipate, we'll actually start to be able to discover issues where we didn't know to look (discover unknown unknowns). This was previously reserved for "power users" because it was quite challenging for casual users to have the expertise using tooling and databases to do this, but AI greatly simplifies that.
Andrew Tunall
President and CPO, Embrace
AUTONOMOUS OBSERVABILITY
Most of the AIOps and Observability vendors are embedding AI into their solutions. These are designed initially to act as AI assistants helping domain specific IT operational staff cut through the noise and resolve issues faster. Over the next few years this embedded AI will become more autonomous and instead of acting alongside the human, it will replace many human activities.
Roy Illsley MBA CEng MIET
Chief Analyst, Omdia
The Shift from AIOps to Autonomous Observability: By the end of 2026, AI-driven observability will pass a critical inflection point, shifting the focus from minimizing Mean Time to Resolution (MTTR) to maximizing Mean Time to Autonomy (MTTA). At least 40% of major cloud-native organizations will run Autonomous Observability systems that detect and remediate low-risk issues — like scaling pods, clearing caches, or auto-adjusting resources — without human intervention. This will be essential for managing massive, distributed architectures and hybrid AI/GenAI pipelines that move too fast and operate too opaquely for traditional human-led troubleshooting.
Sebastian Krahe
VP Product, Checkmk
Agents will gain autonomy and initiate work based on events. For instance, one agent discovering a performance issue will automatically communicate with a development agent, instructing it to analyze, fix the problem, and perform testing without human prompting.
Dan Fernandez
VP of Product for Developer Services, Salesforce
AI evolves from copilot to collaborator: In 2025, 84% of organizations reported exploring or piloting AI in observability. And as they begin to see the value of "actually useful" AI in their systems, more will move from prototypes to practice in 2026. But beyond powering query generation, incident detection, automated triage, and reducing alert noise, we'll begin to see the introduction of autonomous agents that act on intent, helping investigate incidents, summarize context, and recommend fixes before a human ever opens a dashboard. Gartner projects that by 2028, one-third of generative AI interactions will involve autonomous agents, and observability is following a similar path. Rather than replacing human judgment, AI will amplify it, automating the routine while surfacing insights faster than ever. The best AI won't feel like a feature; it'll feel like a teammate.
Dmitry Filimonov
Principal Software Engineer, Grafana Labs
I believe 2026 will be the inflection point where the promise of a fully autonomous, closed-loop software delivery system begins to be realized. There is a convergence of two trends — AI is now matured enough to provide meaningful, contextual intelligence, and a unified observability layer built on OpenTelemetry standards that brings all our signals together across the entire delivery lifecycle. The next chapter of DevOps and AIOps will make insight and action inseparable.
Shubha Govil
CPO, Sauce Labs
By 2026, AIOps will evolve from reactive monitoring to proactive, autonomous remediation. We'll see the rise of agentic observability, systems that not only detect anomalies but diagnose and self-correct using inference pipelines. AI-native data infrastructure will merge with traditional DevOps tooling, enabling real-time correlation between model behavior, data drift, and infrastructure health. The result: fewer manual playbooks, faster incident resolution, and a world where infrastructure teams spend less time firefighting and more time improving reliability. AIOps won't just keep systems running, it'll make them continuously smarter.
Yoni Michael
Cofounder and CTO, Typedef
AGENTIC DIFFERENTIATORS
The IT Operations Management story for 2026 will be all about agentic AI — but that's not the prediction. Rather, as all ITOM vendors adopt AI agents, the challenge will become building defensible differentiators in the agentic ITOM market. The winners will be those vendors that can build agentic platforms that go beyond basic agent functionality to more sophisticated capabilities that offer the vendors a barrier to entry.
Jason Bloomberg
Managing Director, Intellyx BV
Using AI to explain alerts and errors will become table stakes across the observability vendor space — anyone who doesn't have these features will struggle, both as a vendor or as a customer seeking to maintain reliability and meet customer expectations. Differentiation will come in how broad and deep this AI's functionality is — Can it understand when AWS has an issue? Can it take preventative action for you? How often does it generate a false positive? What does it factor into the analysis other than observability signals?
Ian Smith
Head of Strategy, PlayerZero