Honeycomb
Observability platform built around high-cardinality event data and distributed tracing.
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Honeycomb at LDX3 NYC: AI-Native Observability for Agents and Distributed Systems
Honeycomb at LDX3 NYC (September 15-16, 2026) showcases AI-native observability for production systems where AI agents, distributed services, and rapid changes collide. Traditional debugging approaches fail at this scale; teams must understand, investigate, and learn from production with the same speed they ship.
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Honeycomb Donates Adaptive Tail Sampling Processor to OpenTelemetry Collector
Honeycomb is contributing its adaptive tail sampling processor, built on years of Refinery experience, to the OpenTelemetry Collector project. This open-source contribution enables better trace sampling strategies for high-volume observability environments without vendor lock-in.
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Honeycomb Publishes August 26 Webinar and Observability Engineering 2nd Edition with AI/LLM Focus
Honeycomb hosted a webinar on August 26, 2026 focused on AI & LLMs as part of its observability engineering practitioner program, coinciding with release of Observability Engineering 2nd Edition containing 27 new chapters addressing current observability challenges including agent observability and AI system visibility.
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Honeycomb Intelligence: The Future of Observability in an AI-Native World
Honeycomb launched Honeycomb Intelligence suite with MCP Server (GA), Canvas AI-guided investigation workspace, and Anomaly Detection, positioning observability directly into developer workflows rather than siloed dashboards. MCP enables AI coding assistants to query telemetry directly; Canvas automates multi-step investigations via natural language; anomaly detection learns behavioral patterns.
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Honeycomb GA Metrics and MCP Integrations for Agent-Native Observability
On March 11, 2026, Honeycomb announced general availability of Honeycomb Metrics and expanded Model Context Protocol (MCP) integrations to position itself as the first observability platform purpose-built for AI agents as primary contributors and consumers of production software. Introductory pricing starts at $2 per 1,000 time series monthly through June 2026, enabling teams to scale observability affordably as AI workloads generate increasing telemetry volumes.
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Honeycomb Releases Second Edition of Observability Engineering for the AI Agent Era
Honeycomb published the second edition of Observability Engineering: Achieving Production Excellence, completely rewritten to address challenges of AI-accelerated development. New insights cover why most companies cannot safely validate AI-generated code in production, and how shipping faster with AI leads to organizations learning slower—core operational challenges for teams deploying agentic systems at scale.
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Honeycomb Releases Second Edition of Bestseller Observability Engineering to Redefine the Practice for an AI World
Honeycomb published the second edition of Observability Engineering (O'Reilly), nearly doubled in length with 27 new chapters addressing AI-world challenges including instrumentation for AI-assisted development, debugging LLM applications in production, and telemetry pipeline management. The book reflects how software development lifecycle compression into rapid intent-validation loops demands new observability strategies when AI accelerates shipping velocity while complexity grows.
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Honeycomb Releases Second Edition of Observability Engineering to Redefine Practice for AI World
Honeycomb announced the publication of Observability Engineering: Achieving Production Excellence, 2nd Edition, co-authored by Charity Majors, Liz Fong-Jones, George Miranda, and Austin Parker. The book was almost entirely rewritten to address how AI is reshaping observability practice, emphasizing that while fast feedback loops remain essential, production feedback loops haven't kept pace with AI-accelerated shipping. The release coincides with Honeycomb's May launch of Agent Timeline for agentic workflow visibility and Canvas investigation workspace redesign.
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Honeycomb Releases Second Edition of Observability Engineering to Redefine the Practice for an AI World
Honeycomb released the second edition of Observability Engineering: Achieving Production Excellence, completely rewritten by Charity Majors, Liz Fong-Jones, George Miranda, and Austin Parker to address observability challenges in the AI era. The 600+ page book covers production validation of AI-generated code, shipping velocity paradoxes, and evolving practices for agentic systems—marking a fundamental shift in how observability must support human-AI collaboration.
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Honeycomb Releases Second Edition of Observability Engineering for AI-First Operations
Honeycomb announced the second edition of 'Observability Engineering: Achieving Production Excellence,' updated to reflect that AI agents are now both producers and consumers of observability data in production systems. The revised edition, co-authored by founder Charity Majors with Austin Parker (Director of AI Strategy), addresses how observability platforms must be designed for autonomous agent reasoning and debugging.