Observability Sessions Boston

Explore fresh survey data with insights from senior observability decision-makers across industries on what's working, what's not, and where the biggest gaps and opportunities lie.

Effective observability isn’t just about tools – it’s about leadership, too. In this session, you’ll learn the six essentials for building and sustaining a strong observability strategy. From aligning teams and securing executive support to avoiding common pitfalls, we’ll show you how these essentials work together to create a successful observability framework that empowers your organization to make better decisions, respond to issues faster, and protect what matters most.

Pega Blueprint can generate an enterprise application in minutes. What happens next — keeping that application running reliably across a global cloud platform for thousands of enterprise customers in regulated, mission-critical industries — is the hard part. Satya Mishra, Sr. Director of Product Management, Cloud at Pegasystems walks through how Pega Cloud, running on AWS and GCP and supporting billions of application interactions annually, uses Grafana as its central observability platform. The session covers their full production journey and the operational disciplines that hold it together at scale. The speakers demo how Pega's engineering and operations teams use Grafana's unified view of infrastructure metrics, logs, traces, and synthetic monitoring to detect customer-impacting issues before users report them, cut incident response time, and continuously tune platform performance across thousands of environments. Attendees leave with a concrete architecture reference, the operational practices Pega found most valuable, and a frank look at the lessons from its observability transformation — including where the team is heading with AI-assisted operations.

For years, monitoring meant people building views, writing queries, and watching screens until something looked wrong — an approach that no longer keeps pace and ties up your most experienced engineers. This session shows what monitoring with AI looks like today. With Grafana Assistant, your team asks questions in plain language and gets straight answers, builds what they need just by describing it, and finds the root cause in minutes instead of hours. Problems get flagged before anyone notices them, and data costs stay in check without anyone watching the meter. The payoff: every engineer works like your most experienced, issues resolve faster, and observability becomes a source of better decisions, not just another cost. We'll close with where this is heading and how to get your data and your people ready.

Every year, cities used to send interns with knee pads, rulers, and clipboards to measure cracks in pavement by hand. Cyvl replaced that with LiDAR sensors and 360-degree camera arrays mounted on vehicles, processing data for more than 500 cities with close to 100 machine learning models, measurements accurate to within two centimeters. But when a city council member demands an answer within weeks, a 12-hour data processing error isn't an inconvenience. It's a missed vote and broken public trust. To make that possible, Cyvl instrumented its multi-stage pipeline with OpenTelemetry on Grafana Cloud, moving from a world where "we only logs and we'd find out about something breaking the next day" to one where issues are detected and acted on in minutes. Adaptive Metrics tamed the cardinality that comes from bursting zero to thousands of Kubernetes nodes per job, cutting Grafana Cloud costs by 73%. Grafana Assistant accelerated dashboard adoption across the organization with product managers, sales, and operations all now using Grafana Cloud to track everything from feature engagement to sensor locations. John Pignato, Senior Product Manager at Cyvl, walks through what it takes to build observability for an ML pipeline that runs on roads, not racks – from instrumenting LiDAR processing to automating incident response – and shares how Grafana Cloud delivered a 30–40% reduction in per-job compute costs along the way.

LLMs and agents are changing how software behaves. Modern AI applications do more than generate text. They call tools, retrieve context, coordinate across systems, and power business-critical applications. That creates a new production risk: a response can pass every technical check and still be wrong. Healthy isn't the same as correct and traditional observability was never built to tell the difference. This session shows why AI observability is now essential for teams building LLM-powered applications or agents. You'll see what AI changes, then how Grafana AI Observability helps you control cost and performance, debug agents faster, protect user trust, and ship better agents through evaluation and production feedback. We'll close with a demo on an enterprise use case: an AI agent that handles user requests, calls tools, and processes sensitive data and how you’d monitor, evaluate, and improve like any production system.

Hear about the future of observability, from AI-driven insights to Adaptive Telemetry for data-driven decision making.