New insights include why most companies cannot safely validate AI-generated code in production, how shipping faster leads to organizations learning slower, and more

SAN FRANCISCO, June 17, 2026 /PRNewswire/ — Honeycomb.io, the observability platform for the new shape of software, today announced the publication of Observability Engineering: Achieving Production Excellence, 2nd Edition by Charity Majors, Liz Fong-Jones, and George Miranda with Austin Parker. Majors is Co-founder and CTO of Honeycomb, Fong-Jones is Honeycomb’s Technical Fellow and Parker is Honeycomb’s Director of AI Strategy. The book is produced in collaboration with O’Reilly and was almost entirely rewritten to reflect the new challenges facing today’s engineering teams.

“The core argument hasn’t changed from the first edition: fast feedback loops are the beating heart of every high-performing engineering organization, and observability is what makes them possible,” said Charity Majors. “But the stakes have gotten a lot higher. Most teams are shipping faster than ever, accelerated by AI, yet their production feedback loops haven’t kept pace. This means they’re not learning faster, they’re just accumulating risk faster. That tension is why we wrote this second edition. The only way to close that gap, turning production signals into understanding at the speed of AI-assisted development, is observability.”

The Foundations of Observability in the Age of AI

The first edition, published in 2022, established the principles of modern observability for distributed systems. This second edition is not a revision of that book, but rather a near-complete rewrite, as the assumptions the first edition was built on have changed. The multi-stage software development lifecycle that organized the first edition’s argument—write, test, deploy, observe—is compressing into rapid loops of intent and validation, with most of what used to live in pre-production now happening live. The book had to change because the world it described no longer exists.

At more than 600 pages, nearly twice the length of the first edition, the second edition extends the same foundational principles across the full scope of modern software engineering: instrumentation for AI-assisted development, debugging LLM-powered applications in production, telemetry pipeline management, ontologies as a shared language for humans and agents, organizational learning speed as a competitive constraint, and the strategic and financial decisions facing engineering teams navigating the shift.

“The core questions haven’t changed since the first edition: what is your system actually doing, why, and does your code do what you think it does in production. What’s changed is how much of that code humans wrote,” said Liz Fong-Jones. “When agents generate most of your diffs, you can’t validate by reading every line; the proof has to come from production telemetry. So that’s what we wrote: instrumentation for AI-assisted development, debugging LLM applications live in production, telemetry pipeline management, and the organizational decisions that follow from all of it. Martin Fowler told us to make the book shorter. We made it nearly twice as long, because that’s how much the field has grown.”

The latest edition features contributed chapters from practitioners at the frontier of this work, including Boris Tane of Polylane on why observability agents succeed or fail based on context quality; Phillip Carter on building continuous improvement loops for LLM applications using production telemetry; Kesha Mykhailov and Darragh Curran from Fin on the organizational and engineering realities of operating AI at scale; and Hazel Weakly, an Architect at ING and Fellow at the Nivenly Foundation, wrote the foreword and contributed guidance on instrumentation for regulated environments. The ClickHouse engineering team also contributed a chapter that provides an in-depth look at how their open source datastore is architected and tuned specifically for observability workloads.

Meeting the Moment and Continuing the Conversation

The book’s publication coincides with a period of significant product development at Honeycomb. In May, the company launched Agent Timeline, giving engineering teams full visibility into agentic workflows in production, alongside a redesigned Canvas investigation workspace, all built on the same high-cardinality, high-dimensionality foundation the book describes. The thesis of the second edition and the direction of the product are, at this point, the same argument.

Early release editions of the book were shared with practitioners at O11yCon San Francisco in May and LDX3 London earlier this month, where Liz Fong-Jones keynoted and signed copies ahead of the full release. Charity Majors will keynote LDX3 New York in September. Visit the Honeycomb booth to get a signed copy. Additional author appearances will be announced at https://www.honeycomb.io/events.

Observability Engineering, 2nd Edition is available now through O’Reilly Media in print and digital formats. Get your free copy of the book here: https://go.hny.co/4ebtFCQ.

About Honeycomb

Honeycomb is the observability platform for the new shape of software. Built on a decade of distributed tracing leadership and deep roots in the OpenTelemetry community, Honeycomb gives engineering teams—and the AI agents now operating alongside them—real-time, high-cardinality answers about any production system, with no pre-aggregation and no cardinality limits. Learn more at www.honeycomb.io and follow us on LinkedIn.

Media Contact
Ciri Haugh
Press@honeycomb.io

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