Abstract

A developer deploys a service in five minutes. The platform team celebrates another adoption win. Three weeks later, the service degrades in production. The SRE on call has no idea who owns it, what healthy looks like, or why the alerts aren't useful. The platform worked. The deployment succeeded. The SRE is still doing archaeology instead of debugging. Developer platforms reduce friction for developers — golden paths, self-service workflows, automated deployments. All stop at deployment. Ownership, SLOs, meaningful alert thresholds — none are required outputs. So they don't exist when someone needs them most. With AI-assisted development, this gap is getting worse. More services ship faster, with less tribal knowledge baked in. The developer who wrote it may not fully understand it. The SRE inheriting it definitely doesn't. The fix isn't better tooling or more runbooks. It starts at the platform — treating operational context as a required output of every deployment, not an optional annotation added later. Attendees leave with a concrete view of what that looks like, and a starting point for closing the gap between developer experience and operator experience in their own platform.

Bio

Known across the cloud-native community as Maharaj, I make complex technology accessible - from KubeCon stages to community meetups to client boardrooms. What started as a manual QA engineering career over a decade ago has evolved into leading developer relations at scale as a Principal Developer Advocate. I combine engineering depth with narrative instinct - debugging a Kubernetes operator in the morning and crafting a developer content strategy by afternoon.

Abstract

Addressing the gap between Site Reliability Engineering (SRE) theory and practical outcomes is highly critical. It directly determines whether SRE acts as an engine for velocity and resilience or decays into a glorified "ops team". Failing to address this divide leads to rampant team burnout, ballooning technical debt, and misaligned business goals.

Bio

Chakravarthy (Chakri) is a seasoned technology leader with 27 years of extensive experience in the IT industry, spanning project delivery, product engineering, technology solutions, and large-scale practice development.

Abstract

PROBLEM On-call SREs drown in alerts. A single Kubernetes incident throws signals across pods, app runtimes, dependencies (Kafka/Redis/DB), traces, recent deploys and history and a human has to correlate all of it under pressure, while most pages are noise. Single-LLM "AIOps" tools hallucinate confident root causes from one slice of data and erode trust. SENTINEL SENTINEL is an autonomous, multi-agent SRE brain that reasons about production incidents the way a senior on-call engineer does but in parallel and with discipline. A fleet of SEVEN specialist agents each independently investigates ONLY its own data slice and votes on the failure signatures it can justify: • Kubernetes (pods, OOMKills, CrashLoops, scheduling, probes) • App Runtime (logs, JVM/Go/Node runtime errors) • Dependency (Kafka lag, Redis/DB saturation, downstream APIs) • Observability (golden signals, SLO burn, traces) • Change Correlation ("what changed right before this started?") • Runbook (matches vetted procedures) • Historical Memory (recurrence of past incidents) THE MULTI-SIGNAL RULE (the core reasoning idea) An Evidence Correlation Engine confirms a signature ONLY when ≥2 INDEPENDENT telemetry agents agree on it. One agent shouting is noise; two independent agents agreeing is signal worth paging a human for. This single rule is what makes the system trustworthy and kills false positives every confirmed cause carries its corroboration count and the agents that voted. It then ranks root-cause hypotheses with a precise confidence qualifier (confirmed / most-likely / possible / needs-more-evidence), proposes remediations pulled ONLY from a vetted knowledge base (never free-form), risk-classifies them, and forces human approval for anything medium-risk or above. The agent proposes; the human disposes. SENTINEL never auto-closes or runs high-risk changes alone. MICROSOFT IQ - FOUNDRY IQ Foundry IQ is SENTINEL's knowledge & grounding layer. The Historical Memory and Runbook agents ground their reasoning through Foundry IQ's agentic retrieval over the organization's incident history, runbooks and service docs, so diagnoses are grounded in what THIS org has actually seen and fixed, not generic web knowledge. Every resolved incident is written back, so the system compounds institutional knowledge: the next similar incident is recognized instantly. HOW IT'S BUILT (real, running system not a mock) • A distributed microservices system: a gateway, an orchestrator that runs the diagnostic saga, a 7-agent fan-out over a Redis Streams event bus (one consumer group per role), and a stateful memory tier deployable as a right-sized 14-container stack or a full per-stage split. • Full OpenTelemetry instrumentation across every service → an LGTM stack (Tempo/Loki/Prometheus/ Grafana). Trace context propagates across the async bus, so one /analyze is a single connected distributed trace; per-agent latency, anomalies and corroboration are live Grafana metrics. • Event-driven ingestion: point Alertmanager or a Kubernetes event stream at it and the fleet auto-triages, debounced against flapping. • A live "mission-control" UI: a real incident lights up each component of the pipeline as it executes every stage activates, stays lit, shows its real findings, and is clickable for a full drill-down (agent evidence, the correlation table, the verdict, the gated remediations, or the failure error). An interactive knowledge graph of services, dependencies, teams and incidents. A system-architecture view of how it all connects.

Bio

A Multi Cloud, AI & Observability Architect specializing in cloud-native observability solutions with a deep understanding of distributed systems and modern DevOps practices. Key contributions in automating infrastructure operations, improving self-service workflows for developers, extending platform functionalities and accelerating software delivery velocity through effective toolchain and workflow enhancements.

Abstract

Scaling AI agents in production without strict observability is a recipe for runaway costs, unpredictable behaviour, and compliance nightmares. Moving from AI experimentation to a governed, production-grade enterprise platform requires a robust foundation. In this session, we will explore the essential observability journey for standardising and governing AI agents, even in highly regulated environments. We’ll discuss how to leverage tools like Datadog’s LLM Observability from day one to track token usage, monitor agent behaviour, enforce guardrails, and debug production issues in real time. You’ll walk away with a practical framework to build, monitor, and scale AI agent programs safely, securely, and cost-effectively, drawing on proven industry patterns that manage dozens of live applications and drive millions in value.

Bio

Sagar Utekar is a Site Reliability Engineer at CrowdStrike, Docker Captain, CNCF Ambassador, and Kubernetes community leader with deep expertise in cloud infrastructure, observability, platform engineering, and DevOps. A former VMware SRE, he is an active open source contributor, CNCF project maintainer, educator, and content creator who helps engineers build practical skills across Kubernetes, cloud, and reliability engineering.

Abstract

Are we building SRE systems for AI, or are we using AI to build better SRE workflows? The answer is both. This session dives into the symbiotic relationship between Site Reliability Engineering and Artificial Intelligence. Discover how AI-driven tools like agentic incident response and automated SLO copilot are reducing day-to-day toil for engineering teams.

Bio

Deepanshu Kalra is a Principal Site Reliability Engineer at Google, specializing in enterprise cloud transformations, disaster recovery architecture, and production readiness for large-scale AI workloads. With a strong focus on reliability engineering and cloud operations, he helps organizations build resilient, scalable platforms capable of supporting mission-critical systems and emerging AI technologies.

Abstract

The way we write software has changed in the past couple of years. Completely. The way we operate it in production has had to play catch up. Toil is still a thing. Pager alerts too, although those are happening on the phone. Email notification about outages are still filtered and routed to a folder which no one checks. Atleast some thing remains unchanged. In the face of dramatically new ops workflows, what's the role of an SRE? Are they finding ways to cope or reaching the end of the proverbial rope? Come to this talk to see how each AI "black box" is being instrumented and operated in production. Let's explore new tools and techniques together in this new AI-driven landscape. Let's learn from each other about how AI is helping SREs in ways we hadn't thought of before. Let's all develop a shared understanding about what the future is expected to bring for the SRE community.

Bio

Ram Iyengar is an engineer by practice and an educator at heart. He was (cf) pushed into technology evangelism along his journey as a developer and hasn’t looked back since! He enjoys helping engineering teams around the world discover new and creative ways to work. He is a proponent of product development and engineering teams that put the community first.

Abstract

The need for a symbiotic relationship between app and infra: Application aware infrastructure goes beyond simply provisioning resources and instead focuses on optimizing the infra to meet the specific needs of each app. This involves monitoring the performance, understanding the dependencies, and dynamically adjusting infra specifications to ensure optimal application performance and availability. Infrastructure aware applications are designed and built to be aware of the underlying infra on which they run. They can dynamically adapt their behavior, resource consumption, and performance based on the infra's capabilities, limitations, and current state. They actively monitor and respond to changes in the infrastructure, such as network bandwidth, storage capacity, CPU availability, and latency. This session explores the strategies and tools used to optimize infra responsiveness to app needs, and application's elasticity based on infra, to create a more resilient and efficient system.

Bio

Principal Architect and Lead SRE at Freshworks

Abstract

AI agents are showing up in on-call workflows. They triage alerts, pull runbooks, correlate metrics and forget everything the moment an incident closes. That's a problem. SRE agents need more than fast inference. They need context that persists across incidents, spans multiple tools, and doesn't bloat every prompt with the entire history of your system. That's memory management, and most teams aren't thinking about it yet. In this talk, I'll walk through how memory works (and breaks) in agentic SRE workflows, the difference between short-term context, episodic recall across incidents, and shared memory across a team of agent. You'll leave with a mental model for designing memory into your SRE agents from day one, not bolting it on after your agent confidently recommends the same wrong fix three incidents in a row.

Bio

Dipesh has been building software for over 14 years across Enterprises and startups. He was head of engineering at a $500M company for 5 years before starting his own venture in monitoring space.

Abstract

We’re sold AI as magic. In a demo, it looks like exactly that. But the moment it leaves the slide deck and meets a real, paying customer, the truth gets a lot more uncomfortable — and reliability engineers feel it first. In this 10-minute talk, a 24-year-old founder shares the unfiltered reality of building on AI in production: why it predicts rather than understands (and confidently invents things that don't exist), why a working demo is nowhere near a working product, and why AI quietly breaks the core promise SREs are built on — predictability. Same input, different output. No clean stack trace for "the model got dumber today." No obvious way to write an SLO for correctness or roll back a silent degradation. No hype, no fear-mongering — just the brutal, practical truth about where AI genuinely shines, where it lies to you, and why the future belongs to the people who understand exactly where it breaks.

Bio

Santosh Kumar Thota: Founder and CEO of Jatayuv.ai, Santosh Kumar Thota is an entrepreneur, AI mentor, and startup advisor focused on applying AI to real-world challenges. He is building AI-powered travel solutions, mentors students and early-career professionals, and actively supports the startup ecosystem through education, content creation, and community initiatives. Ravindar Kothapally: Senior SRE with 14+ years of enterprise IT experience across large-scale banking and financial systems — shares how the core principles of Site Reliability Engineering became the unexpected blueprint for building and scaling a successful EdTech business. Drawing from real production incidents, high-pressure war rooms, and the discipline of operating systems at 99.95% SLA, Ravindar walks through how SRE thinking — error budgets, blameless postmortems, observability, and structured incident response — directly shaped his decision to leave a stable corporate career and launch RKIT Labs full-time in January 2025. The session explores how SRE-inspired frameworks have been applied beyond engineering — driving operational reliability across sales pipelines, mentor quality SLAs, customer feedback loops, payment monitoring, and business growth metrics, enabling RKIT Labs to train and mentor thousands of IT professionals across India, USA, and Canada. In the final segment, Ravindar addresses the critical college-to-corporate skill gap facing today's engineering graduates, offering a practitioner's perspective on what industry actually demands versus what classrooms deliver — and how structured mentorship bridges that divide. This talk is equal parts technical, entrepreneurial, and motivational — designed for SRE practitioners at every stage who want to understand how reliability thinking scales far beyond the terminal

Abstract

Most production outages don’t start with a failed deployment, a hardware crash, or a traffic spike—they begin much earlier. An untested failover. A retry policy that amplifies load. Hidden dependencies. Alerts teams learn to ignore. Capacity assumptions left unchallenged. Individually, these decisions accelerate delivery. Collectively, they accumulate into Reliability Debt—silent operational risk that compounds until it surfaces as an incident. In today’s distributed systems, outages are rarely caused by a single failure. They emerge from the interaction of architecture decisions, operational practices, and organizational behaviors—creating failures that feel sudden but are years in the making. This session introduces Reliability Debt as a practical framework to identify and manage these hidden risks. Through real-world patterns—dependency debt, observability debt, failover gaps, capacity misalignment, and operational blind spots—we’ll explore how small decisions increase blast radius, delay recovery, and degrade customer experience. We’ll also cover how leading reliability teams proactively reduce risk using: - SLO-driven engineering - Dependency intelligence - Resilience testing and game days

Bio

Sravanthi Naga is a Senior Engineering Manager at Pegasystems, leading initiatives across performance engineering, resiliency, DevSecOps, and platform reliability. A KubeCon speaker, CNCF community volunteer, and experienced engineering leader, she is passionate about Kubernetes, observability, cloud-native technologies, and building high-performing engineering teams.

Abstract

AgentSight uses eBPF to provide zero-instrumentation observability for LLM agents, correlating high-level intents from intercepted TLS traffic with low-level system calls, file operations, and subprocess execution. This boundary tracing approach delivers tamper-resistant monitoring with <3% overhead, revealing prompt injections, reasoning loops, and multi-agent bottlenecks across any framework. Lightning talk demo shows live tracing of Claude solving a complex task.

Bio

I am a systems-focused open-source contributor working on FreeBSD and QEMU, with a deep interest in virtualization, kernel internals, and system performance. My work involves exploring low-level systems behavior and building practical solutions around it, along with experience in scalable systems using Elasticsearch. I actively share my work and insights through talks and community events, and have presented at conferences and meetups including ChennaiFOSS and DevConf. My talks focus on making complex systems concepts accessible, with an emphasis on real-world experimentation and learning.

Abstract

Debugging containerized applications often requires intrusive access, restarts, or adding instrumentation—impacting production systems. In this talk, I’ll demonstrate how eBPF enables deep, real-time visibility into Docker workloads without modifying containers or code. We’ll explore how to trace system calls, monitor network activity, and diagnose performance issues directly from the host. Through practical scenarios, you’ll learn how SREs can debug faster, reduce downtime, and gain production-level insights without disrupting running services.

Bio

Rajani is a DevOps and Site Reliability Engineer (SRE) with over 4 years of experience in cloud infrastructure, automation, and platform engineering. She is a Google Developer Expert (GDE) for Google Cloud, a Docker Captain, and a Women Techmakers (WTM) Ambassador, actively contributing to the global tech community. Rajani is a frequent speaker at major cloud and DevOps conferences, where she shares practical insights on modern infrastructure and emerging technologies. Passionate about knowledge sharing, Rajani aims to inspire and empower developers to build scalable, reliable systems.

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A comprehensive look at the reliability landscape from four critical vantage points: the Fortune enterprise, the platform builder, the service integrator, and the academic trainer. This conversation dives into the human element of SRE—how to foster a culture of reliability, bridge the gap between building cutting-edge infrastructure at a massive scale, and nurture the mindset required to sustain it.

Bio

Vinita Jacob is a senior technology leader with more than two decades of experience leading cloud engineering, site reliability engineering, infrastructure operations, and digital transformation initiatives. Currently Senior Director of Cloud Engineering at Optum, she has built and scaled high-performing engineering organizations, with deep expertise in cloud adoption, DevOps, AIOps, and enterprise operations. Prior to Optum, she spent over 20 years at Verizon leading large-scale SRE and cloud transformation programs.

Vishnupriya Raghavan is Senior Vice President and Business Head of Enterprise IT Business at NIIT StackRoute. With a career spanning enterprise education, consulting, research, and workforce transformation, she specializes in helping organizations build future-ready talent and technology capabilities. She combines leadership experience across industry and academia with expertise in enterprise transformation, strategic leadership, and AI-enabled workforce development, and is also a published researcher in artificial intelligence and emerging technologies.

Jyothsna Kuchimanchi is a global technology executive with nearly three decades of experience driving enterprise transformation, platform engineering, digital innovation, and AI adoption. As Senior Director at UnitedHealth Group, she leads large-scale platform modernization and AI-driven initiatives across healthcare. Her career spans leadership roles across Asia Pacific, Europe, and the United States, with a strong focus on building global engineering organizations, customer experience transformation, data strategy, and technology-led business growth.

Siva Bagavathi is a seasoned, forward-looking professional with around two decades of experience in Site Reliability Engineering, Solution architecting, Performance engineering, Capacity planning, Chaos Engineering, product development, automation and setup SRE for large enterprise applications in various business domains. Consistently recognized as competent individual, skilled at coordinating with cross-functional teams in a fast-paced environment to steer timely completion of project with budgetary constraints.

Nagarjun Malladi is an AI and technology leader with more than 28 years of experience delivering large-scale enterprise solutions and driving innovation across global IT services organizations. At Tech Mahindra, he leads AI capability development and has held senior delivery leadership roles overseeing complex programs across multiple industries. A strong advocate for industry-academia collaboration, he is also a keynote speaker and board member focused on advancing technology talent, AI adoption, and enterprise transformation.

Abstract

For years, site reliability engineering has struggled with the friction of operational toil and the challenge of disseminating institutional knowledge across siloed teams. This session explores a transformative shift from reactive, manual practices to a proactive era driven by specialized, shareable AI agents. We will examine real-world problem statements to demonstrate how Utility, Support, and Platform Lifecycle Management (PLM) agents can left-shift observability and absorb heavy operational burdens. As we launch SRE Day Hyderabad, this talk serves as a blueprint for building and sharing intelligent agents. You will learn how to codify complex SRE patterns, scale your team's collective expertise, and join a new wave of "Agentic SRE" practitioners. Join us to move beyond the toil and learn how to automate the foundation of your reliability strategy—making operations smarter, faster, and truly scalable.

Bio

Sr. Principal SRE & Director at Fortune 3 Company with ~20 years of experience. Founder of SRE@Hyderabadi. I believe SRE isn't just about uptime—it's about culture. My mission is to simplify the complex and elevate the human, blending the chaotic beauty of Hyderabad with the structured discipline of Reliability Engineering to create a unique ecosystem for builders.