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The Agentic Software Development Guide

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A practical guide for engineering leaders setting intent, verifying AI output and governing agentic delivery at scale.

Download the Field Guide Free PDF • No fluff • Real production examples

Why this field guide exists

AI makes it easy to ship more code. It does not make that code easier to trust. Most teams don’t fail because their developers can’t use AI. They fail because the dev’s job changed and nobody redefined it.

  • Under AI, cracks appear:
  • Reviews weaken while output multiplies
  • Code looks clean, but nobody actually understands it
  • Verification gets bolted on after the fact, if it happens at all
  • Speed goes up while accountability quietly goes missing

This field guide shows how high-performance teams set intent, verify AI output and govern agentic delivery without slowing down.

Agentic Software Development Field Guide

What you’ll learn

  • How to set intent through specs and plans that AI cannot quietly reinterpret
  • How to build a context engineering harness that keeps agents focused
  • Structuring verification & risk-based review for quality to scale with output
  • How to threat model the tooling itself, not just the systems it builds
  • How to measure adoption, speed and quality together, not adoption alone
Everything inside is grounded in real production incidents, real audits and real failure modes, not theory.
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What’s Inside

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The AI frontier and where the line sits: Covers what AI can already do today, from Cloudflare’s AI rewrite of Next.js to StrongDM’s fully autonomous dark factory, and the professional ceiling that separates hobby projects from regulated software.
Verification, risk and threat modelling: Covers risk-based review, deterministic checks, keeping builder and verifier agents separate, and threat modelling the tooling itself.

Vibe code drift: Names the core problem: the widening gap between what a developer intended and what an agent produced, and why clean-looking AI code creates false confidence.

Team collaboration and governance:
Covers shared understanding, accountability, traceability and the operating model teams need at organisational scale.

Setting intent and context engineering: Covers contracts, specs and plans, and the harness of context that shapes what an agent actually builds.

Measuring what matters, and Nissy in practice: Covers the three pillars worth tracking together, and a worked example of Nissy, Cleverbit’s own tool for catching drift before it ships.

Who this is for

This field guide is written for:

CTOs, Engineering Managers & Technical Leads

Teams adopting AI agents who are worried about quality, drift and accountability

Regulated and enterprise teams that cannot treat speed and control as a trade-off

If you’re responsible for what ships, this is for you.

about the authors

Eman Zerafa
CTO

Eman Zerafa​

Eman has led teams through high-stakes deliveries, always pushing for accountability and ownership as the core of performance. He believes the best teams act like partners, not order-takers, a principle he has carried from client trenches to boardroom strategy. As CTO of Cleverbit, he extends this philosophy into the era of AI, exploring how new tools can amplify delivery without losing guardrails, accountability or long-term focus.
Ethan Zammit
AI SOLutions engineer

Ethan Zammit

Ethan is an AI Solutions Engineer at Cleverbit Software, where he architects and builds AI-powered systems with a focus on security, scalability and practical business impact. His work spans generative AI, multi-agent systems and context engineering, with a track record of owning solutions end to end, from initial build through to iterative improvement based on real feedback. Ethan holds an MSc in Artificial Intelligence from the University of Malta, completed with Distinction, and a BSc in Artificial Intelligence with First Class Honours from the same institution.

Get the Field Guide

Practical guidance from the engineers who built the verification tooling and governance model this field guide describes.
Agentic Software Development Field Guide
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