Observability Summit North America 2026

The outages that hurt most start across multiple vectors: compiled languages, third-party applications, legacy services, hard-to-instrument areas, and latency-sensitive workloads. In this session, Odigos co-founder and CTO Eden Federman will talk about how eBPF-based instrumentation with OpenTelemetry output delivers full distributed tracing across every service in your cluster — in minutes, with no code changes and <1% overhead.

The pitch for autoremediation is hard to resist: AI doesn't just surface issues faster — it fixes them on the spot, leaving you to kick back, validate, and observe. MTTR doesn't just shrink; it becomes a relic. Problems vanish before anyone even notices they existed.

But rush into it without solid data, proper curation, and clear policy, and you're pulling a tap with too much pressure — nothing but foam, no beer.

Closed-loop remediation isn't a shortcut. It's the payoff at the end of a disciplined, AI-driven observability practice.

In this talk, we'll walk through the three things that make autoremediation actually work:

1. System coverage that holds up at real scale
2. Data that's clean, navigable, and actionable
3. Ground rules for what AI is — and isn't — allowed to do
   
   

You'll walk away with a practical readiness checklist and a clear framework for deciding where autoremediation belongs in your stack, and where it definitely doesn't.

No hype. Just the work that earns AI the right to act in production.

Reddit processes over 25 billion tracing events per hour across thousands of services. In this talk, we share how we scaled our OpenTelemetry-based distributed tracing platform by 67% in one year—and what broke along the way.

We'll cover our architecture: OpenTelemetry instrumentation across Python, Go, and JavaScript baseplate libraries feeding into Kafka pipelines and ClickHouse storage. You'll learn how we handled an incident that spiked ingestion to well over 10 million spans per second, the sampling strategies we developed to balance cost with debuggability, and why instrumenting three language runtimes simultaneously is harder than it sounds.

Key takeaways:
- Practical patterns for multi-language OTel instrumentation at scale
- Remote sampling strategies that adapt to traffic patterns
- ClickHouse schema design for sub-second trace queries
- Building adoption through cross-functional partnerships, not mandates

Whether you're starting your tracing journey or scaling an existing platform, this talk provides battle-tested lessons from running distributed tracing infrastructure serving one of the world's largest online communities.