May 2026 Summaries

PagerDuty, traditionally designed for less frequent software deployments, is facing challenges as AI coding assistants accelerate the rate of code shipping, leading to more deployment-triggered incidents and alert noise. As engineering teams increasingly rely on AI tools like GitHub Copilot, the need for platforms that can handle high-velocity incident management in real-time has become critical. PagerDuty's architecture is web-first, creating friction during incidents as it requires manual coordination, whereas Slack-native solutions offer automated, seamless incident responses. The company's financial indicators, such as a net retention rate drop to 98% by January 2026, suggest a decline in customer satisfaction and value perception, largely due to the inability to match the pace of modern engineering demands. Competitors like incident.io are capitalizing on this by offering AI-driven, Slack-native platforms that reduce mean time to resolution (MTTR) significantly, appealing to teams that need efficient incident management without the coordination overhead associated with legacy systems.

PagerDuty contracts often deter teams from switching to other incident management platforms due to financial obligations tied to their multi-year agreements, which can result in dual payments if a switch is made mid-term. The incident.io Rescue Program addresses this issue by offering up to 12 months of their service for free when customers sign a multi-year agreement, effectively eliminating the dual-payment penalty. Staying with PagerDuty can be more costly over time due to coordination overhead and additional fees for add-ons, with a notable difference in uptime guarantees compared to incident.io. The Rescue Program also provides a structured migration process, exemplified by Zendesk's successful transition of 1,200 users, offering significant operational and financial benefits such as projected first-year savings of $700,000. This allows teams to seamlessly transition to incident.io without incurring extra costs and improving their incident management efficiency.

Migrating from PagerDuty to incident.io is a process that can take anywhere from days to a few weeks, depending on the complexity of the existing configuration. The transition is facilitated by incident.io's AI-powered scanner, which provides a detailed analysis of the current setup and offers a phased migration plan, significantly reducing the perceived risks associated with the switch. The migration strategy employs a three-phase parallel-run approach (Test, Trial, Go-Live), allowing both systems to operate simultaneously and catch routing errors before live traffic is fully redirected, thus minimizing the risk of missed alerts. Notably, Zendesk successfully migrated 1,200 users, 150 teams, and 5,000 monitors with just two engineers and experienced no major issues, highlighting the effectiveness of incident.io's tools. The main challenge in migrating is the fear of breaking long-standing, undocumented escalation policies and configurations, but the AI scanner addresses this by mapping dependencies and generating a complexity score beforehand. While some data, like historical incident records, requires manual handling, most configurations are automatically transferred, with specific tools available for migrating complex elements such as Datadog monitors. The entire migration is conducted within Slack, streamlining the incident management process and reducing the coordination tax typically associated with managing incidents across multiple platforms.

The text examines the critical importance of uptime reliability for on-call tools, comparing the 99.9% uptime standard typically offered by PagerDuty with the 99.99% contractual commitment from incident.io. It highlights the significant difference between these two standards, as even a slight increase in uptime can dramatically reduce allowable downtime from hours to minutes, which is crucial for minimizing Mean Time to Resolution (MTTR) and avoiding customer-reported outages. The discussion underscores the unique challenges faced by on-call tools compared to other SaaS applications, as downtime in alerting tools can lead to undetected production incidents, thereby inflating MTTR and eroding customer trust. The document emphasizes the need for contractual SLAs over published ones, ensuring enforceable guarantees of reliability. Incident.io offers a Rescue Program facilitating migration from PagerDuty, complete with a 99.99% uptime guarantee, to address these issues and enhance the reliability of incident management workflows.

PagerDuty, a reliable alerting tool for engineering teams, often necessitates supplementary infrastructure to manage incident coordination effectively, leading to the creation of custom workarounds such as Slack bots, spreadsheets for on-call pay tracking, and manual shift swaps. These workarounds, while solving immediate issues, incur significant costs in terms of engineering time and operational risk, as they require continuous maintenance and often lack formal documentation, making them vulnerable when the original creator leaves the team. Incident.io offers a comprehensive solution by integrating these functions natively into a Slack-based platform, eliminating the need for custom scripts and additional tools, thereby reducing the time and cost associated with maintaining these workarounds. This shift not only streamlines incident management but also addresses issues of on-call burnout and turnover by providing a more structured and reliable system for incident response, escalation, and audit logging, which are crucial for compliance and effective post-mortem analysis.

The blog post by Mike Fisher focuses on how incident.io approaches on-call reliability by prioritizing customer experience over mere technical control. It emphasizes two critical functions of their On-call product: alert ingestion and notification delivery. The company uses Service Level Indicators (SLIs) to measure alert ingestion availability and notification delivery latency, aiming for a monthly Service Level Objective (SLO) of 99.99% for both. Fisher explains how incident.io designs its systems to cope with third-party dependencies and user-configured delays, ensuring that notifications are timely and reliable even in complex scenarios. The post argues against the notion of excusing failures due to factors outside direct control, instead advocating for a proactive approach that considers customer outcomes as paramount. By embracing complexity and designing redundancy into both their systems and those of their customers, incident.io seeks to deliver a superior, reliable customer experience.

Behind the Flame is a series that highlights team members at incident.io, featuring Maggie Baxter, a Solutions Engineer, who shares insights into her role and experiences at the company. Maggie describes her work as dynamic and fulfilling, working closely with various departments and tech companies to improve their incident management experiences. She takes pride in developing genuine relationships with clients, which go beyond transactional interactions. Maggie looks forward to company events like the annual offsite, which energizes her by connecting with colleagues and hearing about the company's future. She values the company culture, which emphasizes authenticity and personal touches in client interactions, and she appreciates the level of care that her colleagues put into their work. Maggie advises potential candidates to be authentic and prepared, as the company's environment thrives on deep care and commitment. Describing life at incident.io as "FUN," she highlights the meaningful work and enjoyable atmosphere that makes her role rewarding.

By 2027, engineering teams are expected to undergo significant transformations driven by AI infrastructure developments, as companies build tailored code agents and centralized AI-native MCP gateways to enhance developer productivity. These infrastructures integrate seamlessly into existing developer environments, such as IDEs and Slack, to streamline workflows and encourage the use of agentic products that can autonomously perform tasks like code changes and incident responses. The focus has shifted from bespoke user interfaces to the ability to compose and integrate information across existing tools, as companies anticipate a future where tools are expected to interoperate smoothly and act autonomously based on human directives. As a result, engineering teams will include human-agent pairs, with specialized agents handling specific domains, emphasizing the importance of choosing interoperable products that align with the company's AI stack. This shift pressures vendors to ensure their products can integrate natively into these systems, as the competitive landscape evolves towards solutions that facilitate seamless collaboration between human and AI agents.

Incident escalation policies are crucial for optimizing alert routing and on-call assignments by ensuring incidents are directed to the appropriate responders quickly and efficiently, thereby reducing Mean Time to Resolution (MTTR). These policies differ from on-call schedules by focusing on how and when to notify the right person and what steps follow if they do not respond, unlike schedules that simply indicate who is available. Automation, such as that provided by incident.io, enhances the process by eliminating manual coordination, reducing alert fatigue, and integrating seamlessly with tools like Slack and Datadog to maintain service ownership maps and deliver multi-channel notifications. The guide emphasizes the importance of a well-configured Service Catalog for accurate alert routing and suggests conducting incident response drills to test the effectiveness of escalation paths. By automating the alert handoff process and utilizing intelligent routing, organizations can significantly reduce the time spent assembling teams and troubleshooting, thereby improving overall incident management efficiency.

In the escalation policy tools comparison, incident.io, PagerDuty, and Opsgenie are evaluated for their effectiveness in managing incident response, specifically focusing on the reduction of Mean Time To Resolution (MTTR) by optimizing coordination during Priority 1 incidents. PagerDuty is highlighted for its deep routing customization, making it suitable for complex, enterprise environments, albeit with layered pricing and a web-based UI that requires training. Opsgenie, now in its sunset phase, is no longer a viable option, with data deletion scheduled for April 2027, prompting current users to consider migration. Incident.io, designed to operate natively within Slack, offers a transparent pricing model at $45/user/month and promises to reduce MTTR by automating routine tasks and enabling rapid team assembly. This tool is particularly advantageous for teams seeking a quick, efficient, and integrated approach to incident management. As Opsgenie phases out, many SRE teams are reconsidering their choices, evaluating the trade-offs between the robust customization of PagerDuty and the streamlined, Slack-centric approach of incident.io, with the latter being especially appealing for teams prioritizing rapid deployment and simplified workflows.

Automated escalation policies, when accurately implemented, can significantly streamline incident response by ensuring that the correct team is paged swiftly during outages, thereby minimizing Mean Time to Recovery (MTTR). Key to their success is the integration of accurate service ownership data, rigorous testing akin to production code, and continuous monitoring for gaps and errors. Common pitfalls such as timezone misconfigurations, stale on-call rosters, and mapping errors can derail these policies, leading to delays and misrouted alerts that exacerbate incidents. Tools like incident.io address these issues by providing real-time escalation status visibility and a Service Catalog that maps alerts to the appropriate team. Additionally, the article emphasizes the importance of maintaining accurate escalation paths through regular audits and testing, ensuring that all changes and overrides are documented and verified to prevent routing failures. By treating escalation policies with the same diligence as software development, organizations can trust that the right engineer will be paged promptly, reducing alert fatigue and ensuring a more efficient incident resolution process.

Tom Wentworth's article on escalation policy anti-patterns addresses how poorly designed escalation protocols contribute to alert fatigue and increased Mean Time to Resolution (MTTR). It highlights common pitfalls such as overly complex escalation paths, premature paging, lack of time-zone awareness, and outdated playbooks that can lead to slow acknowledgments and missed ownership. The text emphasizes the importance of keeping escalation policies current as teams and systems evolve, recommending regular audits to ensure efficiency and clarity. Solutions include limiting escalation levels to three or four, configuring follow-the-sun on-call schedules, and ensuring alerts are routed to specific engineers rather than entire teams. The article also suggests using data-driven metrics and team feedback to identify and rectify policy weaknesses, ultimately aiming to balance reducing coordination overhead while maintaining rapid response capabilities.

Incident.io, an AI-powered incident management platform, has launched the PagerDuty Rescue Program, which aims to ease the transition for companies looking to switch from PagerDuty by offering automated migration, contract buyouts, and a 99.99% uptime guarantee. This initiative is in response to the sentiment among engineering leaders that PagerDuty has become outdated and difficult to migrate from, despite it being a long-standing on-call tool. The Rescue Program addresses these challenges by providing a white glove migration service that uses AI to scan and categorize existing PagerDuty accounts, helping engineers complete the migration quickly. Incident.io’s platform not only replaces PagerDuty's on-call tooling but also offers a comprehensive approach to software reliability with features like real-time incident transcription and AI-driven post-mortem drafting. The program is designed to attract companies that are currently using PagerDuty by offering cost-effective solutions and improved service reliability, with existing customers like Zendesk reporting significant operational and financial benefits from the switch.

Achieving a 99.99% Service Level Agreement (SLA) for system availability presents significant challenges that go beyond mere infrastructural improvements, particularly when human intervention is involved. The article by Norberto Lopes underscores the difficulty in achieving such a high level of reliability, as it requires a system to autonomously handle incidents within a stringent timeframe of four minutes and 23 seconds before human involvement can effectively contribute to recovery efforts. It highlights the necessity for automation, sophisticated operational practices, and infrastructure resilience, including the use of AI for diagnostics and code suggestions, to ensure systems can cope with initial faults without immediate human action. The text suggests that while AI holds promise in diagnosing issues rapidly, the real challenge lies in developing systems and processes that can autonomously manage short-term recovery, thereby allowing human operatives to validate and fine-tune corrective actions. Additionally, the piece emphasizes the importance of a robust underlying infrastructure and operational practices that can sustain minimal downtime even in complex environments with multiple dependencies.

The text compares incident.io and PagerDuty, two platforms designed for managing on-call incident response, focusing on their differing approaches and capabilities. Incident.io is built as a Slack-native coordination tool, streamlining the incident lifecycle entirely within Slack and automating many processes, such as team assembly and post-mortem generation, which reportedly reduces mean time to resolution (MTTR) by up to 80%. In contrast, PagerDuty offers robust alerting and routing features, with flexibility and customization suited for enterprises needing complex alert systems but requiring more manual coordination effort. The article highlights the coordination tax associated with web-first tools like PagerDuty, where engineers face cognitive load and time lost when switching contexts. While incident.io emphasizes fast onboarding and Slack-native workflows to cut down on coordination time, PagerDuty is noted for its mature scheduling engine and web-based features with additional costs for advanced AI capabilities. The discussion includes pricing transparency, integration capabilities, and the benefits of incident.io's AI-assisted automation, which allows for faster post-mortem report generation and reduced operational overhead.

Onboarding new engineers to on-call roles can be streamlined from a chaotic three-week period to an efficient three-day process through structured shadowing and Slack-native automation, as outlined in a comprehensive playbook. The traditional approach often leaves new hires overwhelmed with tool sprawl and undocumented workflows, resulting in costly mistakes and reliance on tribal knowledge. This playbook proposes a blueprint involving a runbook walkthrough, live incident shadowing, and first-incident simulations to reduce cognitive overload and prepare engineers for real-life scenarios. Tools like incident.io facilitate this process by automating incident timelines and reducing coordination tax, allowing new engineers to focus on problem-solving. The use of AI further enhances efficiency by generating actionable incident reports and assisting with triage and root cause analysis, thus minimizing mean time to resolution (MTTR). This approach aims to create a blameless culture of shared responsibility while ensuring that new engineers are prepared to handle critical incidents effectively.

The text provides a comprehensive overview of on-call tool selection for Site Reliability Engineering (SRE) teams, focusing on reducing Mean Time To Resolution (MTTR) by minimizing the coordination tax associated with switching between tools. It emphasizes the importance of integrating on-call management platforms with existing tech stacks for seamless workflow and quicker onboarding of new team members. The guide evaluates leading vendors for 2026, such as incident.io, PagerDuty, and FireHydrant, highlighting features like AI-powered post-mortems, Slack-native interfaces, and pricing transparency. incident.io is noted for its Slack-first approach, offering streamlined incident management through chat-based commands, while PagerDuty is recognized for its sophisticated alert routing capabilities. The text also addresses the impending sunset of Opsgenie in 2027, urging teams to begin migration pilots. It underscores the need for a structured evaluation framework and considers factors like tool integration depth, vendor support, and compliance requirements. Additionally, it advises on conducting a pilot to measure the tool's impact on MTTR and provides strategies for migrating from legacy systems, aiming to enhance incident response efficiency and reduce operational overhead.

Slack-native incident management offers a seamless and efficient approach to handling production incidents by integrating the entire incident lifecycle directly into Slack through slash commands, eliminating the need for multiple tools and reducing coordination overhead. Unlike Slack-integrated platforms that merely send notifications, Slack-native solutions allow teams to declare incidents, assign roles, and resolve issues without leaving the Slack interface, thereby minimizing context switching and cognitive load. The use of AI further enhances this process by automating routine tasks such as drafting post-mortems and identifying root causes, allowing engineers to focus on resolving issues more effectively. This integration significantly reduces the Mean Time To Resolution (MTTR), saves substantial time and cost by reclaiming hundreds of engineer-hours per month, and facilitates faster team adoption. The distinction between Slack-native and integrated tools is crucial, as the former supports a more streamlined and intuitive workflow, enabling teams to manage incidents more effectively within a familiar environment.

Effective on-call scheduling is crucial for Site Reliability Engineering (SRE) teams, with three key rotation models—Follow-the-Sun, Primary/Secondary, and the buddy system—offering structured approaches to minimize fatigue and streamline incident management. The choice of model should align with team size and distribution, but the real focus should be on managing incidents once paged, as manual escalation, tool sprawl, and missing context can cause significant burnout. Automating incident management with Slack-native tools can reduce coordination overhead, ensuring that the chosen rotation model is efficient and sustainable. Practices like using automated escalation paths and tracking key metrics such as Mean Time To Resolution (MTTR) are recommended to optimize on-call performance. The article emphasizes that the quality of incident response processes and tools often outweighs the specific rotation model in maintaining team health and preventing attrition.

The text outlines a structured 30-day onboarding plan for new engineers to efficiently and confidently handle on-call duties, highlighting the importance of reducing tool sprawl and cognitive load during incidents. It emphasizes the phased approach of setup, shadowing, paired response, and solo shifts to prepare new hires, using platforms like incident.io to streamline processes and minimize coordination overhead. Best practices include automated escalation paths, shadow shifts, reverse shadow shifts, and comprehensive post-mortem reviews that leverage AI to enhance learning and reduce Mean Time To Resolution (MTTR). Moreover, the text stresses the significance of clear documentation, blameless debriefs, and the owner-operator model, ensuring that new engineers are thoroughly equipped to manage incidents with minimal stress and maximum efficiency.