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Gravel Track to Paved Path
Shipping Cloud-Native Java to Production with AI Coding Agents
An AI coding agent can write a Spring Boot service in minutes. Then it breaks in production. This is the missing manual for shipping cloud-native Java to production with coding agents (Codex CLI or Claude Code) and proving it is correct, safe and operable, governed by a minimum-viable harness rather than blind trust.
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About
About the Book
An AI coding agent can write a Spring Boot service in minutes, and it runs on your laptop. Then you deploy it to a container platform and the cracks show: it drops in-flight requests on every redeploy, exhausts the database's connection limit when it scales out, and reports "healthy" while the JVM is still cold. None of that is about the code the agent wrote. It is about the distributed system the code has to live in.
Gravel Track to Paved Path is the missing manual for that gap: how a small, senior team ships cloud-native Java to production with coding agents, and can prove that what shipped is correct, safe and operable. It is built on one worked delivery you follow from an empty repository to a deployed, evidenced feature, and it insists on two things the hype skips. First, cloud-native is how, not where: running Spring Boot on a managed platform does not make it cloud-native; a set of design choices does, and agents make them only if their environment makes them the default. Second, agents need a harness, not a leash: deterministic gates (contracts, tests, architectural rules, quality and security scanners) that catch weak output automatically, so you move fast and prove correctness.
The book is deliberately short, opinionated, and tool-agnostic across OpenAI Codex CLI and Anthropic Claude Code. You build a minimum-viable harness (the gravel track), run one governed vertical slice on it end to end on AWS, then standardise only what the route proved into a reusable pattern (the paved path). It is written for senior Java and Spring Boot engineers adopting agentic development on real, governed systems; it teaches the coding agent and the cloud runtime as you go.
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About the Author
Daniel Vaughan
Daniel Vaughan is a technology leader and software architect based in the United Kingdom, specialising in agentic AI. He has spent approaching thirty years across enterprise, startup, and academic settings, with a career-long focus on engineering quality and developer productivity. His work now centres on the shift where AI stops being an experiment and becomes core to how organisations build software.
Daniel is Head of Forward Deployed Engineering at HCLTech AI Labs, where he leads a global practice embedding engineers and coding agents inside complex enterprise environments to take production AI from prototype to live systems. He built the practice from the ground up, running lean regional pods in which a shared architect, a small number of engineers, and a team of agents working through tools such as Codex, Claude Code, Cursor, and GitHub Copilot deliver at the output of a much larger team.
Before HCLTech AI Labs, Daniel was director of software engineering at Mastercard in London, leading cloud strategy and architecture for real-time payment products in a highly regulated financial services environment. Earlier, he spent eight years at the European Bioinformatics Institute in Cambridge, moving from software engineer into engineering leadership, working on the same problems of software quality and developer productivity that he now solves with agentic tooling.
He is the author of Cloud Native Development with Google Cloud (O'Reilly, 2024) and Ext GWT 2.0: Beginner's Guide (Packt, 2010), a Google Developer Expert, and a Green Software Champion. He writes about agentic engineering and AI-assisted development at blog.danielvaughan.com.
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