Incident management is a high-stakes function. IT operations teams and SRE teams may play different roles, but when a priority incident surfaces, it is often all-hands-on-deck to ensure it is resolved in minimal time. 

That’s because of the high impact of incidents-if not resolved in time, they can cascade and impact other IT systems, leading to downtime, business disruptions, monetary losses, and impacting brand value, compliance, and regulatory rules. 

To meet these challenges, most IT teams have adopted a variety of tools, runbooks and workflows and roles. Yet, incident management remains a challenge and needs to adopt the latest technological advancements to be effective. 

What’s needed is a paradigm shift — one that blends intelligence with automation to move from reactive firefighting to proactive, adaptive resolution. This is where Agentic AI comes into play, where intelligent systems can perceive, reason, act, and learn continuously. It unites cognitive reasoning with dynamic automation, allowing AI Agents to go beyond static scripts or rule-based workflows. These agents can understand the root cause of an issue, determine the best course of action, and even execute remediation — all while learning from each interaction to improve future responses. 

By bringing together intelligence and automation, Agentic AI is redefining incident management — enabling IT teams to focus less on firefighting and more on driving resilience. Read more about what Agentic AI means for IT Operations here. 

This post explores how Agentic AI is reshaping incident resolutions, and the various ways IT teams can leverage this new technology to ensure IT resilience. 

Challenges in traditional incident resolutions  

In enterprise IT landscapes, incident management is a complex operation involving multiple teams each with their own tools and processes.  

One major issue is siloed communication across teams – customer support, command centers, SRE, DevOps, and infrastructure operations teams often work in isolation, leading to delays in resolution. 

Enterprises try to resolve this issue with incident playbooks and runbooks, which are meant to guide incident response, but they often fall short in real-world scenarios. They are typically static, outdated, and hard to adapt to evolving system architectures or unforeseen failure modes. Often IT teams waste time in searching for the right documentation—or worse, following an outdated script that no longer fits the evolving technology landscape. 

Similarly, tools like monitoring dashboards, event management tools, ticketing platforms, and runbook automation tools offer fragmented solutions to incident resolution. They surface data, but not actionable insights, forcing responders to piece together root causes manually and require constant context-switching. These tools are reactive, lacking the intelligence to correlate signals across systems or take meaningful action autonomously. 

Ultimately, traditional methods and tooling fail to keep pace with the complexity and speed of modern systems, resulting in longer Mean Time to Resolution (MTTR), reduced customer trust, and engineer burnout. 

The complexity and scale of modern incident management demand Agentic AI that can adapt, reason, and make decisions — not just follow instructions, but also continually learn to stay relevant in evolving scenarios. 

Understanding how AI Agent for Incident Resolution can transform incident management 

The AI Agent approach for incident management is most effective when the AI Agent is flexible enough to take different approaches depending on the complexity of incidents. An ideal AI Agent for incident resolution includes both autonomous capabilities like fixing regular, repetitive incidents; and collaborative resolution capabilities that surfaces useful analytics to subject matter experts in case of more complex incidents for assisted triaging and resolutions. 

At the heart of the AI Agent is the capability to independently Perceive, Reason, Action, and Learn from current and past incidents, leveraging multiple internal dedicated agents, data stores and tools, and orchestrating them into a unified flow.  The AI Agent is built on the ability to independently Perceive, Reason, Act, and Learn—orchestrating multiple internal dedicated agents, data stores, and tools into a unified workflow. 

Unlike previous systems, AI Agents are purpose-built to meet key incident management objectives, and they have the inbuilt agency to continually and proactively work at the backend to meet those objectives, as well as provide natural-language interactions at the front-end to have meaningful exchange of information with human experts.  

ignio™, Digitate’s SaaS platform built on an agentic architecture, is a prime example of this. ignio, leverages a combination of cutting-edge technologies to power the following capabilities:  

  • Patented AI and Machine Learning (ML) algorithms to detect anomalies, perform root-cause analysis, and identify the optimal corrective actions.
  • Large Language Models (LLMs) for collaborative learning solutions using generative AI and LLMs to augment machine intelligence with expert knowledge to resolve exceptions and unknown situations. 
  • Agentic orchestration that activates the right agents and workflows, seamlessly integrating with internal and external tools and data stores. 

Together, these technologies enable ignio to deliver holistic incident management capabilities that reduce MTTR and ensure resilient IT operations. 

To unlock the full potential of these capabilities, organizations must design clear collaboration and escalation strategies between AI Agents and human experts. 

How ignio AI Agent for IT Incident Resolution works 

On observing an incident, ignio AI Agent for Incident Resolution orchestrates a series of activities by choosing the right mix of specialized agents. Each sub-agent plays a distinct role, working together like members of a well-coordinated team. 

  • Perception Agents discover the potential influencers that can cause the incident. These agents also infer the normal behavior of these influencers.  
  • Reasoning Agents localize the root cause and recommend fixes. These agents perform incident investigation by analyzing the health influencers from metrics, events, and logs. These agents also mine similar incidents in the past to identify commonly observed causes and fixes for such incidents.  
  • External Augmentation Agents use LLMs to address exceptions and unknown cases through conversations with domain experts. These agents also provide conversational interfaces for the users to converse on incidents data.  
  • Internal Control Agents ensure that responsible AI practices are followed while performing root-cause analysis. These agents also ensure the safety and conformance of the fixes as well as ensure human oversight where required.  
  • Action Agents apply the fix and complete the ITSM process.   
  • Learning Agents learn and adapt to changing system behavior.  These agents also learn, adapt, and generalize from expert conversations.  

How AI Agents help in Incident Resolution: Real-world use cases 

ignio AI Agent has been purpose-built to aid IT operations teams, Incident Managers and SRE teams in a variety of tasks. Here are some use cases where ignio AI Agent for Incident Resolution improves the day in the life of different teams.  

Use Case 1: Identification of critical incidents that need attention 

In the daily flood of incidents, IT teams often struggle to prioritize which incidents need their attention. ignio AI Agent for Incident Resolution simplifies this using perception and reasoning agents that monitor incoming incidents to: 

  • Assess the impact of the incident. 
  • Leverage its knowledge of the IT ecosystem to identify and act on the most critical incidents. 
  • Infer the context of the human persona to share only those incidents that are relevant to the person. 

ignio then provides a list of priority incidents, explains why it’s important and links relevant information and screens, reducing a lot of manual effort in categorizing and routing incidents and speeds up resolution of key incidents, ensuring that the right team is engaged early. 

Use Case 2: Automated triaging of an incident and Root Cause Analysis (RCA) assistance 

For many incidents, the issue can be diagnosed by following a standard process. ignio AI Agent for Incident Resolution autonomously performs these steps, mixing action with intelligence to quickly triage every incident that enters its workstream. This includes: 

  • Infer and perform automated health check of associated infrastructure components 
  • Hierarchical health check of the IT components – often successful in locating the IT layer that is the actual cause of incident 
  • Surface possible root causes of incidents based on such steps 

For more complex incidents, where the exact issue cannot be located easily, ignio AI Agent for Incident Resolution leverages patented AI/ML algorithms to assist SREs and other experts with the right intelligence. This includes: 

  • Check deviations from normal behavior of the impacted node and other IT nodes 
  • Conduct multi-variate analysis of metric anomalies across IT components to find probable causes 
  • Present a side-by-side comparison of the time-series data showing the behavior of key metrics, highlighting potential issues for manual visual triaging 

For instance, by using this capability, an SRE can quickly detect that a performance issue in an application was predated by an anomaly in a database minutes before, and may be the actual cause even though they did not map directly, helping them investigate such complex issues. 

IT operations teams now no longer need to start from scratch – as most of the incident diagnosis has been done already by the AI Agent, reducing MTTR massively.   

Use Case 3: Incident resolution recommendation 

After the incident is diagnosed to locate the source of the issue, the ITOps teams need to find a solution to the problem. These require tacit knowledge of individuals or teams or experience from past incidents. ignio AI Agent for Incident Resolution assists the ITOps teams in this process by providing pointed information on: 

  • Common causes of the incidents from its factual knowledge of the IT system 
  • Recommended fixes based on the AI-based learning and factual knowledge 
  • Summarization of past incidents to find similar issues and fixes that have worked in the past 

ignio AI Agent for Incident Resolution is also a continually learning system, and it takes feedback from human experts to continually refine and improve its recommendations. This not only reduces incident resolution time but ensures smooth sharing of tacit information with the enterprise, reducing trial-and-error, and ensures consistent handling of recurring issues. 

Use Case 4:  Automated incident resolution 

In case of regular, repeated incidents where the issue has been successfully diagnosed and known fixes are available, ignio AI Agent for Incident Resolution doesn’t stop at providing recommendations, it can autonomously resolve them as well. This capability is unique-leveraging 200+ fault-fix models that are already present in ignio’s automation library. 

While performing these fixes, ignio’s Internal Control Agents ensure safety and conformance with organizational policies, as well as validates the fix, eliminating any risk from this process. 

This drastically reduces the number of incidents that need human intervention,  improving average resolution times, increasing resilience and it also reduces the cognitive load on human teams.  

Benefits of using AI Agents in IT teams  

With the AI Agent for Incident Resolution, SRE and ITOps teams have the right information available to eliminate issues within minimal time, and the always-on nature of the AI Agents and proactive fixes reduce the volume of incidents that need human attention.  Benefits include: 

  • Reduced MTTR for key incidents: With automated triaging and intelligent incident insights, ignio AI Agent for Incident Resolution helps IT teams reduce MTTR of incidents by more than 80%. 
  • Automated incident resolution: Approximately 35% of incidents have been resolved autonomously by ignio.  
  • Better team productivity:  ignio ensures critical FTEs like SRE and L3 teams are freed from repetitive tasks and incidents that don’t need their involvement. ignio also reduces the volume of manual tasks, improving employee experience. 
  • Improved efficiency: AI Agents present the right incidents to the right teams to cut MTTD with better intelligence.  

ignio™: A step toward the autonomous enterprise   

As IT complexity increases, incident management needs to evolve to ensure resilience of IT systems and minimize business productivity losses from IT issues. 

ignio transforms this challenge into an opportunity— with an agentic approach towards incident management that provides the team with the right tools to meet these goals. The ability to understand, reason and act autonomously have a tremendous impact in unlocking the efficiency of operations, while the continual learning capability ensures tacit knowledge is captured and future proofs operations against growing complexity. 

With ignio, enterprises are able to embrace the shift towards autonomous operations, they move closer to a future where operational intelligence is not only automated but it’s also autonomous—where systems don’t just notify teams about issues, but actively prevent them, enabling humans to focus on innovation instead of firefighting.   

Ready to transform your IT operations? Schedule a demo with us today. Â