Observability Sessions Jakarta

Welcome and the big idea for the afternoon: observability is entering a new era shaped by AI, and the leaders who get the strategy right (not just the technology) will pull ahead. A quick look at what's on the agenda.

Explore 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.

At Pintu, one of Indonesia's leading cryptocurrency exchanges, every second of downtime puts user trust at risk. When the platform engineering team decided to consolidate its incident response tooling, the goals were clear: reduce vendor sprawl; cut costs; and connect alerts, incidents, and on-call in the same Grafana Cloud stack they already relied on for monitoring. Engineering Manager Yusuf Hari Putera Perkasa walks through Pintu's four-month migration of 112 on-call engineers to Grafana IRM — a move that projected 38% cost savings on incident management alone. He covers the five-week evaluation, the phased team-by-team rollout, as well as how they handled the unexpected. Yusuf will also share how operations changed dramatically: ops self-service so non-engineers can trigger critical incidents, Slack-native shift swaps for on-call teams, and retiring eight self-managed Grafana instances in favor of Grafana Cloud. Attendees will leave with a practical migration playbook, a realistic timeline, and the gotchas to prepare for.

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.

A candid conversation with observability leaders on what it really takes to build and sustain a strong practice. We'll dig into the organizational challenges that tools alone don't solve — getting executive buy-in, breaking down silos between Dev, Ops, and SRE, controlling cost as data grows, proving the value of observability to the business, and the cultural shifts that separate teams that mature from teams that stall. Real lessons, honest trade-offs, and audience questions welcome.

The No. 1 request our Observability Architects get from teams is for best practices: tried and true paths to successful deployment, laid out step by step, and examples of usable automation. In this session, Grafana Labs experts will walk through real stories and strategies from our customers who have achieved observability maturity at both small and massive scale: how they deploy, how they automate, how they enforce, and how they measure value. They'll also share some new initiatives within our product and educational programs to make realizing maturity much less daunting.

Hear directly from the Grafana Labs Co-Founder on the future of observability and how AI-driven insights and tools such as Adaptive Telemetry lead to better data-driven decision making. Followed by open Q&A to close the day.

Wrap-up of the day’s discussions, key themes and insights.