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.

AI is changing how your team writes code, ships changes, and investigates incidents; raising the bar on reliability, governance, and trust. If you lead engineering, platform, SRE, or observability, the question is: how do you move faster with AI without losing control? This session covers five pillars for an AI-era observability strategy: tying AI and observability to business outcomes, building trust through evaluation and evidence, getting your platform and data foundation right, adopting AI safely through governance and culture, and building a roadmap from AI-assisted workflows to autonomous operations. We'll ground each pillar in what we're living at Grafana Labs; shipping <20% more PRs per engineer since feeding our own production telemetry into coding agents, and in what our customers are seeing, like MTTR dropping from hours to minutes running AI-driven operations at scale. In this session, we will explore five pillars for observability strategy in the AI era: aligning AI and observability to business outcomes; building trust through evaluation and evidence; creating the right platform and data foundation; helping teams adopt AI safely through governance and culture change; and building a practical roadmap from AI-assisted workflows to more autonomous operations. We will ground the discussion in practical lessons from our own experience at Grafana Labs, as well as from the customers we are helping navigate these changes. We will share what we are learning as a software company building with AI, as an engineering organization operating reliable systems, and as an observability provider helping teams move from insight to action across the software lifecycle.

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.

Hear directly from John Pignato, Sr. Software Engineer at Cyvl as he walks through the successes, setbacks, and next steps in Cyvl's observability journey.

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 directly from the Grafana Labs VP of Product on the future of observability, from AI-driven insights to Adaptive Telemetry for data-driven decision making.