March 2026 Summaries

Steadybit is enhancing its platform to help organizations improve system reliability through proactive testing and controlled chaos experiments, with the introduction of a new feature called Services. This feature allows teams to manage and prioritize reliability risks by providing a service-centric view that aligns with how modern software teams operate, particularly with the shift to microservices and agentic AI. By defining services within Steadybit, users can easily assess test coverage, run reliability tests, and identify critical business risks. The platform's agents automatically discover potential targets, and users can group them as services using a query language or UI. Integration with existing service catalogs via API is also supported, allowing for enriched context and prioritization based on business impact. Additionally, Service Profiles offer a structured approach to expanding testing coverage by organizing experiment templates into categories like Scalability and Redundancy. This new approach aims to connect chaos experiments to business outcomes more effectively, offering tools like ROI calculators to estimate savings from improved reliability. Steadybit invites users to explore these capabilities through a free 30-day trial and further engagement with their team.

Leading observability platforms like Grafana enhance engineering teams' capabilities by providing powerful visualization and monitoring tools that transform aggregated logs into actionable system intelligence. These tools become even more effective when integrated with other systems, such as Model Context Protocol (MCP) servers, which facilitate connections with LLM tools like Claude and Gemini to create innovative workflows. The integration of Grafana and Steadybit, both having launched MCP servers, enables teams to leverage AI-powered analysis to combine observability data with chaos engineering insights, creating a comprehensive approach to system reliability. By connecting Grafana's visualization capabilities with Steadybit's chaos experiment results, teams can develop workflows that include strategic experiment planning, SLO-based experiment design, and incident-driven experiment creation, ultimately democratizing access to reliability insights. This collaboration allows for proactive testing and reactive monitoring, providing a complete picture of system performance and resilience, enabling engineers to rapidly prototype ideas and lower the learning curve for chaos engineering practices.