Streamlining Incident Response: How AI can reduce on call engineer's burden

A little bit about me

With around 15 years of experience in software engineering, primarily in the data and AI space, I have worked extensively on large-scale systems, monitoring solutions, and AI-driven automation. At Microsoft, I have been deeply involved in big data, telemetry and observability, leading efforts to improve system reliability and operational efficiency. My expertise spans data engineering, AI, machine learning, and open telemetry, and I am passionate about leveraging emerging technologies to optimize workflows. Having witnessed firsthand the challenges of incident management and the strain it places on on-call engineers, I see AI as a game-changer in streamlining incident response. You can learn more about me here.

Disclaimer

In this blog, I’ll explore how agentic AI can reduce the on-call burden by automating critical steps in issue diagnosis and resolution. While I have implemented some of this at my current company, I'm sharing generic information on all the possibilities using Agentic AI, without sharing anything proprietary.

Incidents or Support tickets

In today's fast-paced digital landscape, on-call engineers play a pivotal role in maintaining system reliability and swiftly addressing incidents. However, the traditional workflow—from customer support identifying an issue to engineers diagnosing and resolving it—often involves multiple back-and-forth communications, leading to delays and increased workloads. Enter AI: autonomous systems capable of making decisions and performing tasks without human intervention. By integrating AI into incident response processes, organizations can streamline operations, reduce on-call burdens, and enhance overall efficiency.

The Traditional Incident Response Workflow

Typically, when a field issue arises, the process follows these steps:

1. Issue Identification: A customer encounters a problem and contacts the support team.
2. Information Gathering: Customer support collects details about the issue.
3. Escalation: If unresolved, the issue escalates to an on-call engineer.
4. Diagnosis: The engineer seeks critical information:
   • When did the issue occur?
   • Is it ongoing?
   • Are all necessary information available to query logs or debug further?
5. Information Gaps: Missing details require reverting to customer support, who then contact the customer again.
6. Resolution: With complete information, the engineer analyzes logs to identify and rectify the root cause.

This iterative process can cause significant delays, increased workloads, and frustration for both customers and support teams.

Introducing Agentic AI into Incident Response

Agentic AI systems autonomously perform tasks, make decisions, and adapt to changing environments without human input. In the context of incident response, agentic AI can revolutionize the traditional workflow by:

1. Automated Issue Detection and Classification:

   • Proactive Monitoring: AI-driven tools continuously monitor systems, identifying anomalies before they escalate into significant issues. By analyzing patterns and deviations, these tools can detect potential problems early, reducing the frequency of critical incidents.
   • Intelligent Triage: Upon detecting an issue, AI systems can classify its severity and potential impact, ensuring that critical problems receive immediate attention while filtering out false positives.

2. Enhanced Data Collection and Analysis:

   • Contextual Data Gathering: Agentic AI can automatically collect relevant data—such as timestamps, system logs, and user actions—at the moment an issue is detected, ensuring that on-call engineers have all necessary information upfront.
   • Root Cause Analysis: By analyzing aggregated data, AI can identify patterns and pinpoint the underlying causes of issues, providing engineers with actionable insights.

3. Automated Communication and Resolution:

   • Customer Interaction: AI-powered virtual assistants can engage with customers in real-time, gathering essential details about the issue through natural language processing, reducing the need for multiple back-and-forth communications.
   • Automated Remediation: For known issues, agentic AI can execute predefined solutions, resolving problems without human intervention and only escalating to on-call engineers when necessary.

Benefits of Agentic AI in Reducing On-Call Burden

Integrating agentic AI into incident response workflows offers several advantages:

• Reduced Response Times: Automated detection and data collection expedite the initial phases of incident management, allowing for quicker resolutions.
• Decreased Workload: By handling routine tasks and minor issues autonomously, AI frees up engineers to focus on more complex problems, reducing burnout and improving job satisfaction.
• Improved Accuracy: AI systems can analyze vast amounts of data without fatigue, leading to more accurate diagnoses and reducing the likelihood of recurring issues.
• Enhanced Customer Satisfaction: Faster response times and proactive issue resolution lead to a better customer experience, fostering trust and loyalty.

Implementing Agentic AI in Your Organization

To effectively integrate agentic AI into your incident response processes, consider the following steps:

1. Assess Current Workflows: Identify repetitive tasks and common pain points in your existing incident response procedures that could benefit from automation.
2. Select Appropriate AI Tools: Choose AI solutions that align with your organization's specific needs. For instance, platforms like Merlinn offer open-source AI assistants designed to handle system alerts and incidents autonomously.
3. Integrate with Existing Systems: Ensure that the chosen AI tools can seamlessly interface with your current infrastructure, including monitoring systems, communication platforms, and databases.
4. Train AI Models: Utilize historical incident data to train AI models, enabling them to recognize patterns and make informed decisions.
5. Monitor and Iterate: Continuously monitor the performance of AI systems, gathering feedback from on-call engineers and support staff to refine and improve AI-driven processes.

Challenges and Considerations

While agentic AI offers numerous benefits, it's essential to be mindful of potential challenges:

• Data Privacy and Security: Automated systems must handle sensitive information responsibly, adhering to data protection regulations and ensuring that customer data remains secure.
• Transparency and Trust: Maintaining transparency in AI decision-making processes is crucial to build trust among employees and customers. Clear documentation and explainable AI models can aid in this effort.
• Continuous Learning: AI systems require regular updates and training to adapt to evolving threats and system changes, necessitating ongoing investment in AI development and maintenance.

Conclusion: Key Metrics to track - Building Sustainable Incident Management

Streamlining customer incident response requires a holistic approach combining technical innovation, process optimization, and cultural evolution. By implementing the strategies outlined – from AI-powered triage systems organizations can achieve:

• 63-75% Reduction in on-call engineer workload
• 55% Faster mean time to resolution (MTTR)
• 89% Improvement in engineer job satisfaction

The path forward demands continuous investment in both technology and people. Organizations that master this balance will not only improve operational reliability but also create engineering environments where talent thrives amidst increasing system complexity.

Source/Citation

Images used in this blog are generated by Microsoft copilot.

In my next post, I plan to cover some of these topics in detail!
Stay tuned! Feel free to leave a comment! Get in touch on Linkedin for any collaboration!