After years of building and managing SaaS platforms, we’ve experienced the challenges of being on-call firsthand—navigating the high-pressure, critical moments of incident response. If there’s one thing we’ve learned, it’s that success in these situations depends on a cohesive team with clearly defined roles and a well-structured incident management process. Every person needs a specific role, and each action must serve the overarching goal of resolving incidents as quickly and effectively as possible.
Google's incident response framework provides a great starting point, emphasizing key stages like Prepare, Respond, Learn, and Communicate. The attached diagram outlines the stages: from triaging alerts to resolving issues and ultimately learning from incidents to prevent future problems. It’s a cycle designed for efficiency, collaboration, and continuous improvement.
Effective Incident Response Workflow
Every stage of incident response relies on precise coordination among the team. The On-call Engineer is at the frontline, rapidly triaging alerts and providing immediate context. The Incident Commander orchestrates the response, ensuring the team stays aligned and focused. Meanwhile, SREs (Site Reliability Engineers) and SWEs (Software Engineers) dive into the technical details, diagnosing issues and implementing fixes.
However, the process often lacks a crucial element: augmented knowledge. While teams rely on past experiences, post-mortems, runbooks, and observability data, these resources are typically siloed, static or outdated, or difficult to navigate in high-pressure situations. This slows down decision-making and can lead to repeated mistakes or missed insights.
Knowledge graph, the heart of incident investigation
To address this, we’ve built a knowledge graph that aggregates and organizes all incident-related data, including past incidents, post-mortems, runbooks, CI/CD changes, and observability signals. This graph is not only traversable but also contextually rich, allowing teams to quickly access actionable insights. By providing an interconnected, easily navigable system of record, we ensure that every decision during an incident is informed by the full depth of the organization’s knowledge, accelerating resolution times and preventing recurrence.
NOFire Agentic AI team and workflow
At NOFire AI, we’ve taken a structured incident response approach to the next level by integrating an Agentic AI system to empower SRE and On-call engineers. Here’s how it changes the game:
- Triaging Alerts: AI agents immediately sift through incoming alerts, filter out noise, and prioritize the most critical signals. This reduces alert fatigue and allows SREs to focus their attention on issues that truly matter.
- Identifying true cause-effect relationships: Our architecture uses multiple specialized AIs to analyze observability signals—metrics, logs, traces, and more. These agents collaborate with SREs to uncover the true cause-effect relationships behind incidents, ensuring that teams spend their time addressing root causes, not just symptoms.
- Assessing Incident Impact: The system evaluates how incidents impact user experience and business metrics. By leveraging a comprehensive knowledge graph, it provides contextual insights into dependencies and historical data, equipping SREs with a clearer understanding of the incident’s broader implications.
- Recommending Mitigation Actions: Drawing from a rich repository of past incidents, runbooks, and best practices, the AI system suggests targeted actions for mitigation. This enables SREs to make informed decisions faster, reducing the guesswork and stress of incident resolution.
- Learning and Evolving: At NOFire AI, we’ve developed a data model specifically for incident data, capturing observability signals, post-mortems, runbooks, RCAs, and CI/CD activities. By empowering SREs with this knowledge base, we help them continuously improve their processes and reduce the likelihood of repeated issues.
- Replicating the incident response workflow: Our system works alongside SREs by replicating the key roles of an incident response team. AI agents take on supporting roles—such as On-call Engineer, Incident Commander, SRE, and SWE—but always work in collaboration with the human team. These agents provide insights, surface recommendations, and assist in complex decision-making, while SREs maintain full control and direction.
By addressing the full lifecycle of an incident - from triaging alerts to continuous learning - our system empowers SREs to do their jobs more effectively. We’re not just reducing resolution times; we’re augmenting the expertise of the people on the front lines, allowing them to innovate, reduce firefighting, and build more reliable systems. NOFire AI is here to amplify the impact of SREs, ensuring that their work is more efficient, less stressful, and better aligned with business needs.
An Agentic AI collaboration
The key to effective incident management is the ability to work smarter, not harder. Our Agentic AI system acts as a force multiplier for the team, enabling engineers to:
- Quickly pinpoint the root cause of issues by analyzing interconnected observability signals.
- Cut through alert noise, focusing on the most impactful problems.
- Make decisions based on contextual insights and historical data, reducing reliance on intuition alone.
Incidents should not be about firefighting - they should be about control, precision, and proactive management. With a well-structured team and AI-powered augmentation, every incident becomes an opportunity for improvement rather than a crisis
Our vision: building a system of insights, not just observations
The future of incident management isn’t about building more dashboards or collecting more data. It’s about delivering insights when and where they’re needed most—during the incident. Rather than having engineers stare at metrics and logs, our vision is to transform these into actionable insights that guide decision-making in real time.
At NOFire AI, we’re committed to empowering SREs and on-call teams with the Incident Resolution AI that turn incidents into opportunities for learning and growth. The Agentic AI system doesn’t just help SREs team to resolve incidents faster; it fundamentally changes how teams interact with their systems, helping them stay ahead of the curve and maintain the stability of the services they manage.