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.

AI is accelerating software development at an exponential pace, but we have no idea what our AI systems are actually doing. Agents operate across distributed frameworks. One request spawns dozens of hops with zero visibility. The OpenSearch Observability Stack closes that gap—built for open source contributors, with a growing focus on developers and operators using these systems every day. Open source. Linux Foundation-governed. One pipeline. Every framework. Every model. Every hop visible. The agentic era deserves open infrastructure, and we’ll share how this is a step towards building it together.

Today, many organizations are pushing beyond existing limits for telemetry volume. Systems are ever-more distributed and generative AI workloads produce enormous amounts of data. As telemetry volumes grow, observability pipelines must become more efficient.

At scale, telemetry egress directly impacts observability spend. Cloud providers charge per gigabyte of data transferred across regions or providers, and those bytes add up quickly. The protocol used to encode telemetry determines how much data is sent over the network. Even modest improvements in encoding efficiency (i.e. the protocol) can translate into significant cost savings. However, the OpenTelemetry Protocol (OTLP) was not initially optimized for performance. Instead, it prioritized interoperability and easy adoption.

Today the OpenTelemetry community is exploring OTAP, a new stateful protocol for transmitting OpenTelemetry data based on Apache Arrow. By using columnar encoding and maintaining state throughout a stream, OTAP avoids repeatedly sending the same metadata, reducing payload size and network transfer. However, because OTAP relies on long-lived stateful streams rather than independent requests, there is additional architectural and operational complexity in its implementation. There are further challenges to larger adoption by the community; for example, Apache Arrow support varies significantly across languages.

Protocol design today is critical to efficiently scaling your systems. In this talk we will explore how protocol design affects telemetry egress and overall observability cost. We will go over some strategies for improving encoding efficiency, compare stateless and stateful approaches, and discuss the potential benefits and drawbacks of adopting a protocol like OTAP. Join us to learn more about how your protocol decisions can influence your costs over time.

Production AI agents make thousands of tool-calling decisions daily, yet observability stops at the model boundary. OpenTelemetry's GenAI semantic conventions capture token counts and latencies—what the LLM processed—but not why an agent selected a specific tool. Research (McKenzie et al., 2023) demonstrates inverse scaling: more capable models exhibit unpredictable tool selection patterns. This gap leaves engineers guessing during critical production failures.

We present gen-ai-otel, an open-source OpenTelemetry extension introducing decision-level telemetry for MCP agents. A new attribute namespace (gen_ai.agent.*) captures tool selection confidence, session context, permission scope validation, and baseline deviations. The zero-sidecar architecture routes telemetry through standard Collector pipelines to existing backends—Jaeger, Prometheus, or graph databases—with low overhead and cardinality-aware attributes.

A live demo reconstructs an agent's decision chain, revealing anomalies invisible to token metrics—reducing decision-debugging time. Attendees leave with: 1) Collector configs, 2) Grafana dashboards for confidence tracking, 3) demo code and repo—all Apache 2.0 licensed.