SRE Copilot: Multi-AI Agent Solution for Autonomous Reliability скачать в хорошем качестве

SRE Copilot: Multi-AI Agent Solution for Autonomous Reliability

Petar Rusev Area Technical Lead SAP Petar Rusev, PMP®, is an Area Technical Lead at SAP with a strong background in software and cloud development. With over a decade of experience as a full stack engineer and technical lead, he has worked on building scalable, resilient enterprise solutions and holds several U.S. patents in cloud reliability. Petar is passionate about combining innovation and reliability to create impactful software at scale. Lyubomir Nedyalkov Senior DevOps Engineer SAP I’m a passionate Site Reliability Engineer focused on automation, reliability, and leveraging AI to drive smarter operations. I specialize in building intelligent workflows. Whether it’s predicting failures before they happen or optimizing performance with data-driven insights, I’m all about making systems faster, smarter, and more reliable. As modern systems grow in complexity, even experienced Site Reliability Engineering (SRE) teams struggle to coordinate across service boundaries-especially during high-stakes, time-sensitive incidents. While each team may effectively manage its own services, the growing web of interdependencies demands faster, smarter, and more connected decision-making. SRE Copilot introduces a collaborative multi-agent AI system where each agent represents an SRE team, equipped with contextual knowledge of the services it covers. These agents communicate and reason together across domains-sharing insights, diagnosing issues collectively, and coordinating responses in real time. This agent-to-agent collaboration is at the core of SRE Copilot’s power: enabling distributed teams to operate as a unified force during incidents, significantly reducing recovery time and boosting operational resilience. To streamline integration and enhance contextual interoperability, SRE Copilot supports multiple modes of integration with services-ranging from direct API interactions to standardized protocol-based approaches. Among these, the Model Context Protocol (MCP) offers a standardized interface layer that sits above existing service APIs. When services adopt MCP, they expose a consistent, structured protocol describing their operational context, telemetry, and behavioural models. This allows Copilot agents to seamlessly interact with service endpoints regardless of underlying implementation, enabling faster onboarding, richer diagnostics, and more effective coordination across systems. Designed to work with human-in-the-loop interaction, the system supports teams by offering actionable recommendations, automating repetitive tasks, and enhancing decision-making while keeping engineers in control. It fits seamlessly into existing communication platforms like Slack and Microsoft Teams, ensuring minimal disruption and immediate value. To support usability, SRE Copilot includes a custom tool canvas-a user-friendly interface that presents the outputs of tools and agent activity in a clear, actionable way. It helps teams visualize the ongoing incident landscape and collaborate efficiently and complements the intelligence and coordination driven by the agents themselves. Importantly, security is not forgotten. All capabilities are built with access controls, auditability, and safe execution boundaries to ensure that the system remains trustworthy and compliant, even in high-pressure scenarios. By connecting teams through intelligent agent collaboration-and enabling seamless service integration via MCP-enabled context-aware APIs or other supported interfaces-SRE Copilot bridges the gap between siloed operations and unified incident response, empowering SRE and Development teams to diagnose, respond, and resolve issues faster and more effectively than ever before. Attendees will get a firsthand look at how AI-powered agents can transform incident response through real-time collaboration, contextual awareness, and seamless service integration. The session will showcase SRE Copilot in action, demonstrating how it enhances coordination, shortens recovery times, and fits naturally into existing workflows.