Articles

  • You Can’t Prompt AI to Determinism

    ByAlan Knox

    Everyone who builds with AI eventually asks the same question: “Why did it do that when I told it not to?” And almost everyone’s next move is the same too. They go rewrite the prompt, add a sentence, bold a word, and treat the next version as the fix that will finally make the thing behave. I understand the instinct (I have it too), but it quietly leads people astray. You cannot rewrite a prompt well enough to make a probabilistic system behave like a deterministic one. The words are not the problem. This article digs into why that rewrite-the-prompt loop disappoints you, why a stubbed-out function is the perfect illustration, and where reliability actually has to come from if you are building anything automated.

  • | |

    Full Stack Development: Engineering for Vibe Coders

    ByAlan Knox

    One of the biggest changes in software development is that a single developer can now build complete applications that once required entire teams. Modern AI-assisted development makes it easier than ever to create user interfaces, APIs, databases, authentication systems, cloud infrastructure, and AI workflows. But building across the stack also means understanding how all of those layers interact. In this article, we explore full stack development for vibe coders, including system thinking, integration complexity, AI-generated code, data flow, operational responsibilities, and the engineering habits that help developers build complete systems without losing sight of how the pieces fit together.

  • The Quiet AI Agent Problem

    ByAlan Knox

    Ask people what they like about AI agents and you will hear some version of the same answer. They work in the background. You hand off a task and later it is done. The quiet is part of the appeal. But the same property that makes agents pleasant to use is also what makes them hard to trust, and the two cannot really be separated. Here is what happens when the quiet you wanted turns into the quiet you did not.

  • | |

    Software Development Lifecycle (SDLC): Engineering for Vibe Coders

    ByAlan Knox

    One of the biggest misconceptions in vibe coding is thinking software development ends when the code works. Modern AI-assisted development makes it incredibly easy to move from idea to implementation quickly, but software still exists within a larger lifecycle involving planning, testing, deployment, monitoring, maintenance, iteration, and operational reliability. In this article, we explore the Software Development Lifecycle for vibe coders, including feedback loops, maintenance thinking, operational planning, lifecycle management, and the engineering habits that help systems remain stable and adaptable as complexity grows.

  • | |

    DevOps: Engineering for Vibe Coders

    ByAlan Knox

    One of the biggest misconceptions in software development is thinking the work ends when the code runs locally. Modern AI-assisted development makes it incredibly easy to build and deploy applications quickly, but operational reliability introduces an entirely different layer of engineering complexity involving deployment, monitoring, automation, recovery, and infrastructure management. In this article, we explore DevOps fundamentals for vibe coders, including deployment strategy, observability, operational automation, infrastructure assumptions, recovery planning, and the engineering habits that help systems remain reliable in real-world environments.

  • |

    AI Agent Failure Handling

    ByAlan Knox

    Traditional software fails loudly: exceptions, stack traces, crashes. AI agents don’t work like that. They fail quietly and confidently, handing you a finished-looking answer to a question they couldn’t actually solve, while downstream steps treat that answer as ground truth. The mature agent systems aren’t the ones that fail least often; they’re the ones that fail visibly, by policy, with a name attached to what went wrong. This piece is about treating failure handling as a first-class part of agent design: how failures cluster by who can detect them, why detection has to live outside the agent itself, and what a real response policy looks like when something breaks at three in the morning.

  • | |

    Linting: Engineering for Vibe Coders

    ByAlan Knox

    One of the easiest ways for software quality to slowly degrade is allowing small inconsistencies, risky patterns, and avoidable mistakes to accumulate unnoticed over time. Modern AI-assisted development makes it incredibly easy to generate large amounts of code quickly, but speed also increases the risk of inconsistent structures, weak assumptions, duplicated logic, and hidden technical debt spreading across a project. In this article, we explore why linting matters for vibe coders, including AI-generated code consistency, readability, automated feedback loops, maintainability, and the engineering habits that help systems remain understandable as complexity grows.

  • |

    AI Agent Contracts: The Missing Primitive in Agentic Systems

    ByAlan Knox

    Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, and 78% of executives say they couldn’t pass an AI governance audit in the next 90 days. The standard explanations (better models, tighter prompts, more guardrails) keep falling short because they’re aimed at the wrong target. The real gap is structural: agents are operating without contracts. Not policy documents, not system prompts, not permissions matrices, but enforceable, bilateral agreements that define what the work is, what the agent is allowed to do, and what the agent and the surrounding system owe each other. Here’s why contracts are the missing primitive in agentic systems, and what builders and buyers should be doing about it right now.

  • | |

    Type Systems: Engineering for Vibe Coders

    ByAlan Knox

    One of the easiest ways to create fragile software is building systems where data structures constantly change without clear rules or expectations. Modern AI-assisted development makes it incredibly easy to generate large amounts of code quickly, but without strong type discipline, inconsistency and ambiguity spread just as fast. In this article, we explore why type systems matter for vibe coders, including data contracts, runtime validation, AI-generated code risks, shared workflows, and the engineering habits that help systems remain understandable and maintainable as complexity grows.