March 2026 Summaries

The Checkly CLI is increasingly being used by AI agents to manage monitoring setups, with a trend showing that agents will likely dominate its usage by 2026. The CLI serves as the primary interface for AI agents, enabling them to automatically handle tasks such as monitoring configuration, incident resolution, and outage communication. The integration of Checkly Skills equips agents with the necessary knowledge to perform these tasks efficiently without requiring external documentation, while the CLI's structured output allows agents to access and process monitoring data directly. Guardrails are implemented to ensure safe autonomy, requiring human confirmation before executing critical actions, thus maintaining human oversight in incident management. These features underline the shift towards AI-driven operations within the Checkly ecosystem while ensuring that the system remains robust and manageable for both human and agent users.

The rapid evolution of AI agents, as demonstrated by the Opus 4.5 release, is transforming the tech landscape by enabling agents to perform complex tasks traditionally done by engineers, such as writing code and managing system reliability. This shift is likened to a major technological leap, with AI adoption outpacing historical trends like DevOps. As AI agents thrive in command-line interfaces (CLIs) rather than traditional user interfaces (UIs), platforms like Checkly have adapted by emphasizing Monitoring as Code (MaC), which seamlessly integrates with agents' capabilities to manage and optimize software systems. This code-first approach allows agents to autonomously handle tasks such as setting up monitoring, investigating failures, and managing incidents, leveraging structured data and specialized skills to enhance their efficiency. The increasing use of AI agents in development workflows signifies a paradigm shift, with tools unable to integrate with agent-driven processes likely being replaced by more adaptable solutions like Checkly, which are designed to support and enhance agentic capabilities.

In 2025, the hiring process at Checkly was characterized by a substantial volume of applications and a commitment to transparency and communication. With 12,479 applications across 12 job postings, the company maintained a rigorous selection process, progressing only roughly 1 in 58 applicants to the interview stage. Engineering, product, and design roles attracted the most interest, while the Senior Backend Engineer position was particularly competitive. Despite the tight selection funnel, every applicant received a response, highlighting Checkly's dedication to avoiding common industry pitfalls like ghosting. The majority of hires came from active applications, supported by team referrals and recruiter sourcing. Feedback from candidates was generally positive, with an average recommendation score of 4.25 out of 5, reflecting the company's emphasis on professionalism and integrity. The median time from application to offer was 22.5 days, with an 83% offer acceptance rate, indicating a successful and efficient hiring process. Looking ahead, Checkly plans to continue its transparent hiring practices and expand its go-to-market team in 2026, inviting interested candidates to explore open roles or join their talent community.

Checkly has expanded its uptime monitoring suite to include ICMP monitoring, allowing engineering teams to enhance visibility across various network layers. ICMP monitors send ping requests to hosts or IP addresses to assess reachability, latency, and packet loss without requiring open ports or application logic, making them ideal for monitoring infrastructure like databases and internal services that lack HTTP endpoints. This feature is particularly useful for private networks, incident triage, and regional latency comparison, allowing teams to quickly identify network issues. ICMP monitoring is fully integrated into Checkly's Monitoring as Code environment, supporting automation tools like Terraform and Pulumi, and is available on all plans at a flat-rate pricing model. Users can configure and manage ICMP monitors through Checkly's dashboard or by using the CLI, facilitating seamless integration into existing workflows.

Rocky AI, a new AI agent developed by Checkly, has been designed to integrate into their SaaS product to streamline the process of triaging failed Playwright Check tests. Over approximately six to eight months, Checkly developed this tool, initially facing challenges such as managing large data sets and guiding the AI to focus on relevant information. The team realized that a focus on data wrangling was crucial, as Playwright trace files and network PCAP files can be extensive, thus requiring pre-parsing and filtering to make them manageable for large language models (LLMs). Rocky AI's ability to perform root cause analysis involves codifying human skills, such as those used in ICMP and PCAP analysis, into structured formats that the AI can interpret. The team also discovered that while AI model updates have improved performance significantly, implementing a multi-model approach presents difficulties in maintaining quality control. Additionally, they found that while chat interfaces are popular, they are not always the most efficient way of interacting with AI; instead, automating the initial analysis and delivering results to users directly can be more effective. Rocky AI's development continues, with plans to expand its capabilities and features further.

Rocky AI, now generally available to all users, is Checkly's AI agent designed to enhance application reliability by offering continuous analysis of test and check failures. It provides AI-powered root cause analysis, impact assessment, and more, helping both large teams and smaller engineering groups address issues efficiently by analyzing logs, traces, and other vectors to identify the causes of failures. Rocky AI also assesses user impact during outages, determining the extent of disruption to user activities, which aids in prioritizing fixes. For teams with extensive alert systems, Rocky AI can integrate findings directly into alerts via email or Slack, streamlining the workflow for quicker resolutions. Users can begin utilizing Rocky AI's error analysis through Checkly Resolve, with future plans to expand its capabilities to include AI-powered test generation and alert configuration within the Checkly dashboard.