Reading list
Work by other people that we recommend to teams adopting agentic engineering. Each entry first appeared as a spotlight post on LinkedIn.
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Leon van Zyl (opens in a new tab)
Leon van Zyl teaches agent building step by step, in code and in no-code tools, moving between agent frameworks, Claude Code and Cursor. In his build-along tutorials he starts from an empty project, gets the simplest version running, watches where it breaks and fixes that. Teams that stall while designing a whole orchestration up front can copy that order: ship the thin happy path first, then harden where the harness shows a real failure.
Recommended on LinkedIn on 14 September 2026 (opens in a new tab)
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Tidy First? (opens in a new tab)
Kent Beck's newsletter arrives in small, frequent pieces, from deep technical dives to short reflections on the trade. Its title asks whether to clean up messy code before a change, and Beck treats the answer as an economic decision: tidying costs a little now and pays back on every later change in the same spot. With agents producing code at speed, a team that never makes that call on purpose ends up with a codebase that grows fast and changes slowly.
Recommended on LinkedIn on 7 September 2026 (opens in a new tab)
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DevOps & AI Toolkit (opens in a new tab)
Viktor Farcic's channel works through real DevOps, Kubernetes and CI/CD products and talks openly about their tradeoffs, including when something is not worth it. His running thread on internal developer platforms rests on one mechanism: an expert codifies what they know into a service, so nobody has to wait for them again. An agent is one more platform user, and every path that still needs a senior engineer to unblock it stops the agent as well.
Recommended on LinkedIn on 31 August 2026 (opens in a new tab)
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Maggie Appleton (opens in a new tab)
Maggie Appleton, a designer and anthropologist, writes illustrated essays on programming, knowledge and how people think with what they build, kept as a digital garden she tends over years. Her essay on the garden's ethos labels each note by stage, from seedling to budding to evergreen, with the dates it was planted and last tended. Context files for agents rarely say how settled they are, and her stages let an agent tell a validated rule from a guess written under deadline.
Recommended on LinkedIn on 24 August 2026 (opens in a new tab)
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IndyDevDan (opens in a new tab)
A practitioner channel on building with coding agents, with one working pattern a week shown end to end and the reasoning left in. Start with The Library, a skill that keeps a team's other skills in one catalog of references, with commands to add, pull and sync them. Once skills, agents and prompts spread across repositories and people, sharing them becomes a distribution problem, and The Library answers it the way package managers do for code.
Recommended on LinkedIn on 17 August 2026 (opens in a new tab)
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Team Topologies (opens in a new tab)
Matthew Skelton and Manuel Pais give engineering leaders a vocabulary for arranging teams so value flows between them, in a book now in its second edition and in patterns, templates and assessments on their site. The idea to start with is the Thinnest Viable Platform: the smallest platform that removes real friction for the teams on it, grown only when a need pulls it. The harness and automation around a team's agents form a platform too, and the same question keeps them from becoming the bottleneck they were built to remove.
Recommended on LinkedIn on 10 August 2026 (opens in a new tab)
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A Philosophy of Software Design (opens in a new tab)
John Ousterhout's short book grew out of a Stanford course and has one subject: complexity in software and how to fight it. The chapter to read contrasts deep modules, which hide a lot behind a small interface, with shallow ones whose interface is almost as complicated as what it wraps. An agent writes code that passes its tests without feeling that difference, so reviewers need these words to ask for depth and check for it.
Recommended on LinkedIn on 3 August 2026 (opens in a new tab)
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Simon Willison (opens in a new tab)
Simon Willison has documented the practical edges of these models in public, post by post, with no vendor behind him. His lethal trifecta names the combination that makes prompt injection dangerous: an agent with access to private data, exposure to untrusted content and a way to send data out. Count those three for every agent you run, and design out the full set before anything goes near production.
Recommended on LinkedIn on 27 July 2026 (opens in a new tab)
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Building effective agents (opens in a new tab)
Anthropic's essay collects what the company learned building agents with dozens of teams: the implementations that worked used simple, composable patterns instead of heavy frameworks. Its middle section names five patterns, prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer, each with a diagram and a note on when to use it. It also separates steps that follow paths you wrote from an agent choosing its own path, which tells a team where to put a gate.
Recommended on LinkedIn on 20 July 2026 (opens in a new tab)
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DX Research (opens in a new tab)
DX asked the same questions across more than 400 engineering organizations over 16 months while AI tools rolled in. Usage of those tools rose 65 percent, median PR throughput just under 8 percent, and these were mature adopters. DX traces most of the gap to work outside the editor, such as review and integration, and its writing on AI-native engineering organizations points at the fix.
Recommended on LinkedIn on 13 July 2026 (opens in a new tab)
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Tessl builds spec-centric software, and its channel hosts the AI Native Dev podcast with Guy Podjarny and Simon Maple, plus conference talks and practitioner panels. The question under the episodes: once an agent can regenerate code from a clear description of intent, the specification becomes the thing a team protects. Where that intent lives only in tickets and chat threads, an agent rewriting a module works from a guess.
Recommended on LinkedIn on 6 July 2026 (opens in a new tab)
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GitHub's Spec Kit has a team write a spec, a plan and a task breakdown before the agent builds, and it works with most coding agents. It sits between prompting the agent directly, which wins for spikes, small fixes and exploration, and heavier frameworks such as BMAD. Study it for the spec as a contract: when the problem is understood and a lot is at stake, it settles what done means once, before the agent writes a line.
Recommended on LinkedIn on 29 June 2026 (opens in a new tab)
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Ona is an agent platform whose agents run in isolated cloud environments and open pull requests on their own, and its channel carries short demos, getting-started walkthroughs and recorded virtual events. Start with the Background Agents Virtual Summit, where the talks keep returning to one finding: the environment matters as much as the agent. An agent that cannot install packages, run the tests or build the project cannot check its own work, however strong its model.
Recommended on LinkedIn on 22 June 2026 (opens in a new tab)
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Hooks reference (opens in a new tab)
Claude Code's hooks reference documents shell commands the harness runs at fixed points in the agent loop, such as before a tool call or when the agent tries to finish. Read the Stop hook first: it can run the tests and send the agent back to fix failures before it may hand over. That puts a deterministic gate where a prompt could only ask, and the docs also show how a hook exiting the wrong way silently lets the agent stop.
Recommended on LinkedIn on 15 June 2026 (opens in a new tab)
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Software Engineering Productivity Research Group (opens in a new tab)
This Stanford group studies what AI does to software engineering productivity, working from telemetry across many companies instead of surveys. Its method keeps usage, such as seats activated, prompts sent and lines accepted, apart from outcome: working software that reached a user. Read it before the next internal AI report, since a count of logins and acceptance rates says nothing about whether a team ships faster.
Recommended on LinkedIn on 8 June 2026 (opens in a new tab)
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Patrick Debois (opens in a new tab)
Patrick Debois, who coined the term DevOps, now writes on his personal blog about the agentic version of the problem DevOps answered. Start with his essay on self-tuning context, where agents rewrite their own instructions and raise operations questions about review, drift and rollback. Teams now run code an agent wrote and few people read closely, and his writing on ownership, observability and fast feedback applies directly.
Recommended on LinkedIn on 1 June 2026 (opens in a new tab)
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Drew Breunig (opens in a new tab)
Drew Breunig writes long, careful essays on how language models behave: the data, the context and the mechanics under the demo. His essay How Long Contexts Fail names four failure modes: poisoning, distraction, confusion and clash. With those names a team can diagnose a misbehaving agent precisely and fix its context on purpose.
Recommended on LinkedIn on 25 May 2026 (opens in a new tab)
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Anthropic Engineering (opens in a new tab)
Anthropic's engineering blog describes, in engineering detail, how a model lab builds the harness around its models. Its post on harness design for long-running work splits planning, building and evaluating across three agents, so the builder never grades its own output. The post also holds that every harness component encodes an assumption about what the model cannot yet do alone, which gives teams a reason to review and prune their own harness as models improve.
Recommended on LinkedIn on 18 May 2026 (opens in a new tab)