Most teams begin their agent strategy with a model choice. That is understandable, but incomplete. A capable model can reason over a task; it cannot safely complete useful work ...
Most conversations about AI begin with the prompt. That is understandable: prompts are visible, immediate, and easy to experiment with. But a prompt is only one instruction insi...
Most software agents fail for a surprisingly ordinary reason: they are designed as clever prompts rather than durable systems. A prompt can produce a compelling demo. It cannot,...
Most software teams already have systems that produce evidence about their own behavior: logs, test failures, incident reports, support tickets, review comments, deployment hist...
AI does not make software less important. It raises the bar for what useful software looks like. A capable model can explain an API, draft a query, or suggest a workflow. But it...
An AI agent can produce impressive text from a well-written prompt. That is the easy part. The harder and more valuable question is whether it understands what the text, code, t...
An AI agent does not become useful because it can produce convincing text. It becomes useful when it can operate inside the messy reality of a software stack: repositories, tick...
An AI agent is not a chatbot with a to-do list. It is a system that observes a situation, chooses an action, uses tools, checks what happened, and decides what to do next. That ...
Software used to be built primarily for people: users, operators, and the developers who maintained it. That assumption is changing. Increasingly, an AI agent may be the first “...
The most useful AI agent in a software product is rarely the one that takes over the work. It is the one that makes a capable person faster, more informed, and less likely to mi...
Most software architectures are designed around a quiet assumption: the software will do roughly the same kinds of work tomorrow that it does today. Agents challenge that assump...
Most teams do not need an AI agent that can “do everything.” They need a system that can make a useful change, prove what it changed, and leave the codebase easier to operate to...
Most teams begin their AI journey with prompts. That is sensible: prompts are cheap to try, easy to change, and often reveal useful capabilities quickly. But prompt engineering ...
AI becomes genuinely useful in software when it understands more than a prompt. It needs a working model of the system’s core: what the business means, which rules cannot be bro...
Most software teams do not need an “AI transformation” program. They need a better way to remove friction from work that is repetitive, information-heavy, and easy to verify. Th...
A prompt can produce a clever answer. An agent can turn that answer into work—if the surrounding system is designed with the same care as any other production software. That dis...
Most AI disappointments begin with a category error: treating a system as though it were a prompt. A prompt can produce an impressive answer in a demo. A useful AI system must p...
Most software failures blamed on AI begin with an incomplete idea of what an agent is. A prompt can produce a useful suggestion, a code fragment, or a first draft. An agent that...
Most software failures involving AI agents do not begin with a bad prompt. They begin when a promising demo is mistaken for a reliable system. An agent that can summarize a tick...
AI agents can now draft code, trace a bug through unfamiliar files, write tests, and turn a plain-language request into a working prototype. That is genuinely useful. It is also...
