I wrote previously about why AI-generated code needs rules, not rituals.
AI coding agents aren't going anywhere. They're excellent at exploring ideas, generating boilerplate, and moving fast. But speed without reliability just ships bugs faster. And without constraints, AI-generated code is unreliable by default.
Prompting is a ritual. Linting is a rule.
Here is the proof.
Coding agents are here to stay, and I know I’m absolutely right about that. While we're all getting used to workflows using AI-powered coding agents, we now live in the world of dark arts and rituals.
We spend hours tweaking prompts, creating elaborate Claude.md, Agents.md and other files formatted in a particular way, stored in a particular way essentially performing black magic to hope our LLM agent adheres to our team's best practices and coding patterns.
On a good day, it works. On others, the behaviour is random.
Here's the problem: Random is not good enough for production.
We're trying to force a non-deterministic, generative tool to be a deterministic rule-follower. This is the wrong approach.
Instead, we should let AI do what it does best: be creative and generative, helping us achieve and realise our desired outcomes while following instructions and examples for how, what, and where it should generate.
My advice? Stop relying on hope-driven prompting. Start using linters to guarantee your standards.
For most of us, AI still feels like a black box. We send it a prompt and we get back a blob of text. Maybe we write some code to call a tool; maybe we juggle a few callbacks. We tell ourselves that this is just how things work: a model can only generate tokens, and tools can only run in our code.
But what if this mental model is the problem?
In this post I want to argue that the Agent pattern in the AI SDK is as revolutionary for AI development as useState and useEffect were for React. Just like React's client/server directives annotate where code runs across the network, the Agent API annotates where logic runs across the AI/model boundary.
Having recently built an AI Guardrails library for the AI SDK, I wanted to share what I learned along the way. This post will walk you through how you can write your own middleware, and why it's such a game-changer for building robust AI applications.
Design AI features that are safer, faster, and easier to evolve by layering language model middleware. This guide explains how to use AI SDK middleware to transform inputs, post-process outputs, enforce safety rules, cache results, observe performance, and handle streaming using a clean, composable approach aligned with official guidance.
Read More →This is the second post in a series of posts about building reliable AI agents.
When building AI agents, where do your prompts live? If they're hidden inside frameworks or scattered across configuration files, you're missing a fundamental principle of maintainable AI systems: treating prompts as first-class code citizens.
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⚽️ Dropped by then and current manager Sarina Wiegman from the England squad in 2022 amid concerns over her attitude and conduct, she remained outside the national setup for almost two years.
⚽️ Born with strabismus and challenged by depth‑perception issues, she was told by doctors she should not play football.
⚽️ Rebuilt her career at Chelsea, playing an integral role in winning the treble last season.
⚽️ Chosen as England's first-choice goalkeeper ahead of the great and brilliant Mary Earps.
⚽️ Delivered under pressure in the quarter‑final versus Sweden, even with a bloody nose. Named Player of the Match.
Tonight, she starts in a European final for England.
Even when things don’t go your way, never give up.
Read More →This is the first post in a series of posts about building reliable AI agents.
Unlock reliable, testable AI agents by treating your LLM as a parser, not an executor. Learn how converting natural language into structured tool calls leads to predictable, scalable systems.
Read More →After building production AI systems over the past few years, thanks to HumanLayer, I’ve learned that most agent failures aren’t about the LLM, they’re about architecture.
That’s why I’m creating a series of posts sharing the 12-Factor Agents methodology using Mastra.
In each part, I’ll break down one principle that transforms fragile prototypes into robust, production-ready AI agents.
Read More →Last month, I had the fantastic opportunity to run a hands-on Model Context Protocol (MCP) workshop. It was a chance to mentor and coach colleagues, from engineers and testers to product owners, as we explored what MCP is, how it works, and why it is generating so much momentum across AI and developer communities.
My goal was not just to share knowledge. It was to guide the learning journey and build a shared, foundational understanding of this powerful emerging standard.
We covered everything from core protocol basics and prompt crafting to real world patterns such as:
Designing distributed systems has never been more challenging. As teams embrace microservices and event‑driven architectures, a persistent myth has arisen: commands equal orchestration, and events equal choreography. This tidy equivalence often becomes a mental shortcut but conceals the deeper truth of control‑flow patterns versus messaging semantics. As many practitioners have observed, collapsing these separate dimensions can restrict your system's flexibility and resilience.
In this article, we'll demystify these concepts and show how to apply them independently. You'll discover how separating semantics (commands vs. events) from control flow (orchestration vs. choreography) grants you greater architectural freedom and clearer, more maintainable workflows.
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