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The Definitive Guide to AI for DevOps

Everything your engineers need to learn for your organization to reach the next stage of agentic AI — from guardrails and agentic observability to multi-agent systems and the metrics that prove ROI.

By Jennifer Riggins, tech journalist — featuring insights from leaders at Honeycomb, Netlify, Google, AWS, Nvidia, Oracle, HPE, and more.
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About the Guide

From “how fast can we ship” to “how safely can we ship”

The second half of 2026 marks a definitive pivot in software delivery from “How fast can we write code?” to “How can we safely get code into production?” AI-generated code is shipping faster than humans can review it, and DevOps engineers are now the stewards of scale. This guide shows what modern engineers and their leaders need to know to move from code creation to agentic supervision and operations.

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2/3 of teams
of DevOps teams have integrated AI into delivery pipelines — but only 12% have achieved true AI-driven operations (Thoughtworks)
1.7x more issues
AI-coauthored PRs contain 1.7x more issues than human-only PRs
91% longer
AI-generated code sees 91% longer review times
10x vulnerabilities
Companies with 4x velocity gains saw 10x more vulnerabilities
1 in 4 enterprises
don’t believe they can attract and retain the engineering talent to keep systems running.
What You'll Learn

Key takeaways from the guide

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Why AI velocity is making PR culture obsolete — and how the engineer's role shifts from individual contributor to orchestrator of agentic systems
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How to build guardrails, not gates: golden paths, platform engineering, and machine-readable policies that let teams (and agents) move fast safely
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How to shift ops from reactive to proactive with agentic observability, root cause analysis, and auto-remediation (with humans in the loop)
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What agentic security means for DevSecOps: negative mean time-to-exploit, agentic pipelines as an attack surface, compliance as a continuous output
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How to design and observe multi-agent systems — agent trains, bounded agent roles, shared state, and approval gates
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The metrics that matter: DORA in the AI era, AI confidence metrics, mean time to identify/correlate/innocence, and error budgets as guardrails for autonomous agents
What's Inside

Chapter overview

Part 1

Where We Are Today

The next level of abstraction · More code than ever is a problem · The persistent review problem at AI scale · Choosing the right thing to build · Demand for DevOps is only starting

Part 2

What We Need Now

Guardrails not gates · DevOps drives agentic experience · Your DevOps for AI wishlist · Proactive, preventative DevOps · Agentic observability · Agentic security · The shift to multi-agent systems · Multi-agent observability

Part 3

Measurements That Matter

Metrics for AI in DevOps · AI confidence metrics · Error budgets as guardrails

Expert Voices

Featuring insights from

Charity Majors (CTO, Honeycomb) · Kelsey Hightower (ex-Google) · Grady Booch (co-creator of UML) · Dana Lawson (CTO, Netlify) · Dave Farley · Laura Tacho (AWS) · Gergely Orosz (Pragmatic Engineer) — plus practitioners from Nvidia, Oracle, Google, HPE, Palo Alto Networks, Komodor, Octopus Deploy, Storyblok, Availity, Delivery Hero, and KodeKloud's own Michael Forrester and Nimesha Jinarajadasa.

“AI came for code generation first because it was the easiest problem to solve.”

Charity Majors, CTO, Honeycomb
Who It's For

Built for every layer of the engineering org

Engineering Leaders & DevOps/Platform Managers

Understand the skills gaps to fill and how to prove AI ROI to the business.

DevOps, SRE & Platform Engineers

Plan your own upskilling journey for the agentic era.

Developers Becoming Agent Orchestrators

Learn the ops-side fundamentals AI now demands of everyone.