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The Field Deployed Engineer's Handbook

Production AI in the Real World

This book is 65% completeLast updated on 2026-05-07
Daniel Vaughan

88 per cent of AI agent projects never reach production, not because the model failed, but because the harness around it was never built. This is the practitioner's handbook for engineers who deploy AI inside real organisations, covering the complete journey from discovery to handover with harness engineering as the core technical discipline.

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About

About

About the Book

The model is the easy part. Making it work inside a real organisation, that is the job.

Forward deployed engineers are the fastest-growing role in AI. Hiring surged 800 per cent in 2025 and continues to accelerate. Yet the people doing this work have no handbook. They learn by trial and error: how to enter a customer environment, find valuable work in the first week, build AI systems that survive contact with enterprise reality, and leave behind teams that can continue without them.

This book fills that gap. It covers the complete lifecycle of AI field engineering: understanding the role, running structured engagements from discovery through delivery to handover, building the harness that makes AI trustworthy inside a customer's environment, and managing a career that sits between engineering and consulting.

Twelve chapters span four parts: The Role, The Method, Production and The Human Side. Each chapter ends with field exercises. A companion GitHub repository provides templates, checklists, architecture decision records, evaluation rubrics and code examples.

Whether you are considering an FDE move, already embedded with a customer, or building an FDE practice, this is the practitioner's handbook for engineers who deploy AI where it actually has to work.

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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.

Leanpub Podcast

Episode 329

An Interview with Daniel Vaughan

Contents

Table of Contents

The Role

Chapter 1: The New Field Engineer

  1. The Production Gap That Created a Profession
  2. What a Forward Deployed Engineer Actually Does
  3. From Palantir Curiosity to Industry Standard
  4. Why AI Makes Field Engineering Essential
  5. FDE vs Everything Else
  6. Real Engineering or Fancy Consulting?
  7. The Pod and the FDE
  8. Do You Need an FDE?
  9. The Harness Engineering Connection
  10. The FDE Mindset
  11. Field Exercise

Chapter 2: The Honest Career

  1. Why Engineers Are Interested Now
  2. Will This Keep Me Technical?
  3. A Realistic Week
  4. Career Paths
  5. Compensation
  6. The Interview
  7. Positioning Your Application
  8. Burnout, Travel, and Red Flags
  9. Negotiating the Offer
  10. Field Exercise

Chapter 3: The Capability Model

  1. The T-Shaped Field Engineer
  2. The Five Capability Families
  3. The Maturity Model
  4. The Minimum Viable Technical Stack
  5. The Learning Path
  6. Field Exercise
  7. The Method

Chapter 4: Discovery in the Real World

  1. The Gravel Road
  2. The First Week Determines Everything
  3. The Five-Day Discovery Protocol
  4. Preparing for the First Week
  5. Stakeholder Interviews
  6. The Discovery Output
  7. Field Exercise

Chapter 5: From Ambiguity to Executable Plans

  1. The Translation Problem
  2. Writing Outcome Statements
  3. From Outcome to Plan
  4. Choosing the Right Delivery Vehicle
  5. Managing Scope Without Losing Momentum
  6. Demo-Driven Development Without Demo Theatre
  7. Writing Plans That Both Sides Trust
  8. Field Exercise

Chapter 6: AI Patterns for Field Engineers

  1. Start by Not Using AI
  2. The Building Blocks
  3. Agent Patterns in Practice
  4. Context Engineering
  5. RAG: When It Helps and When It Distracts
  6. Tool Use and MCP Servers
  7. Keeping Architecture Simple Enough to Ship
  8. Field Exercise

Chapter 7: Data, Integration, and Customer Systems

  1. The Job Is Integration
  2. Integration Patterns
  3. Structured, Semi-Structured, and Unstructured Data
  4. Data Access, Identity, and Least Privilege
  5. Source-of-Truth Problems
  6. MCP: The Integration Standard
  7. The Smallest Useful Vertical Slice
  8. The Last Mile
  9. Field Exercise
  10. Production

Chapter 8: Evaluation and Harness Engineering

  1. The Model Is Not the Product
  2. The Six Harness Layers
  3. The Ratchet Principle
  4. Eval-Driven Development: The OpenAI FDE Method
  5. The Counter-Intuitive Testing Result
  6. Evaluation Infrastructure
  7. Harness Maturity Levels
  8. Building Harnesses That Survive the Engagement
  9. Field Exercise

Chapter 9: Production, Security, and Governance

  1. Why Demos Lie
  2. Production Readiness: Six Dimensions
  3. The AI Threat Model
  4. Harness Security: Engineering, Not Hope
  5. CI/CD for AI Systems
  6. Incident Response for AI
  7. The EU AI Act: What FDEs Must Know
  8. Performance, Cost, and Token Accounting
  9. Field Exercise: The Production Readiness Review
  10. Key Takeaways
  11. The Human Side

Chapter 10: Working with Customers

  1. The Order-Taker Trap
  2. The Trust Spectrum
  3. Explaining AI Limitations Without Undermining Confidence
  4. Handling Sceptics and Champions
  5. Negotiating Trade-Offs
  6. Making Invisible Progress Visible
  7. Field Survival
  8. The “Permanent Crutch” Anti-Pattern
  9. Field Exercise: The Weekly Field Update
  10. Key Takeaways

Chapter 11: Adoption, Handover, and Product Feedback

  1. The FDE’s Real Deliverable
  2. The Autonomy Ladder
  3. Training by Doing, Not by Telling
  4. The Agentic Pod
  5. Handover Milestones
  6. Documentation That Survives
  7. The N-to-1-to-N Lifecycle
  8. Separating Customer-Specific from Reusable
  9. The Field Report
  10. The Harness as Ultimate Handover Artefact
  11. Field Exercise: The Handover Plan
  12. Key Takeaways

Chapter 12: Leading and Scaling the Practice

  1. The View from Above
  2. The Consulting Trap
  3. The FDE Lead Role
  4. Staffing Model
  5. Quality Standards for Field Deliverables
  6. Career Paths
  7. Portfolio Visibility
  8. Building Reusable Harness Components
  9. A Complete End-to-End Scenario: Project Atlas
  10. Field Exercise: The Hiring Rubric
  11. Key Takeaways

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