AI and Code
AI tools, IP ownership, attribution, and compliance for enterprise teams.
AI is reshaping how software gets built. These guides help engineering leaders and procurement teams navigate the practical realities: which AI architecture to choose, how to track and attribute AI-generated code, and what readiness looks like across data, people, process, and governance.
Whether you are evaluating RAG pipelines versus fine-tuning, preparing for the EU AI Act, or building harnesses for coding agents, each guide is written by practitioners who use these tools in production every day.
The Risks of AI-Augmented Development
AI-augmented delivery introduces specific risks across IP, attribution, regulation, security, quality drift, and skills. Eight risks and the controls that contain them.
AI-Augmented Development for Public Sector and GDS
How AI-augmented delivery aligns with the GDS Service Standard, Technology Code of Practice, and UK public-sector procurement. Practical guidance for delivery teams and buyers.
The AI-Augmented Software Development Lifecycle
Stage by stage walkthrough of AI's role in discovery, specification, build, test, review, deploy, and operate. Where the human keeps the lead and the artefacts that change.
AI-Augmented vs AI-Assisted Development: The Difference
AI-assisted development uses AI as an editor helper. AI-augmented development integrates AI across the lifecycle, with measurable delivery gains. The distinction in 2026.
What is AI-Augmented Development? Meaning, Practice, and Evidence
AI-augmented development integrates AI tools across every stage of the software lifecycle, with human engineers in the lead. A practical definition, the practice behind it, and the evidence.
Harness Engineering for Coding Agents
What harness engineering means for AI coding agents: inner vs outer harnesses, guides, sensors, and deterministic tools for Cursor and Claude Code.
Harness Templates for AI Coding Agents
How model-driven templates and code generators become a deterministic outer harness for AI coding agents in Cursor and Claude Code.
The EU AI Act and Custom Software: What UK Businesses Commissioning AI Need to Know
When you commission custom AI-powered software, the EU AI Act determines who carries which obligations. This guide explains provider vs deployer, risk classification, and what UK businesses must do before August 2026.
Using AI to Meet the GDS Service Standard
How AI tools, agent rules and skills (in tools like Claude Code and Cursor), and the GOV.UK Design System help delivery teams meet the GDS Service Standard across research, design, build, and operation, and where human effort still concentrates.
AI Code Attribution for Enterprise Procurement Teams
A practical framework for tracking and documenting AI-generated code. Repo-level model logs, PR attribution notes, CI licence gates, SBOM integration, and what procurement teams should require from suppliers.
Is Your Organisation Ready for AI? A Practical Readiness Checklist
Most AI projects stall before they deliver value. This guide provides a structured readiness assessment across five dimensions: data, people, process, infrastructure, and governance.
RAG vs Fine-Tuning vs Prompt Engineering: Choosing the Right AI Architecture
Three approaches to getting your data into AI, each with different costs, timelines, and trade-offs. A practical comparison for enterprise teams evaluating AI architectures on Azure.
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Explore other guide categories
API and Integration
REST, GraphQL, gRPC, Azure API Management, and integration patterns.
Build, Buy, or Replace
Decision frameworks for build vs buy, SaaS replacement, and migration planning.
Development Practice
DevOps, CI/CD, testing, estimation, and delivery patterns.
Legacy Modernisation
Recognising hidden costs, choosing a modernisation strategy, and planning migration from legacy systems.
Teams and Support
Choosing partners, managing handovers, and outsourcing application support.
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