AI is everywhere at the moment. It writes emails, summarizes meetings, makes pictures, writes code, searches through documents, and occasionally gives you an answer with the confidence of someone who has never been wrong in their life.

That is what makes it so interesting.

Welcome to Parsed Prompt. This is my first post, and this blog is going to be about AI in the real world: models, prompts, tools, agents, automation, and all the weird little details that show up once you actually try to use the stuff.

Why this blog exists

There is no shortage of AI hype, and there is no shortage of very serious technical writing either. I am aiming for the space in between.

I want to figure out which tools are genuinely useful, which ones are mostly good at making a demo look impressive, and what happens when a prompt meets a messy, ordinary workflow. I am also curious about the less glamorous questions: how much access should an agent get, where do the logs go, and what happens when it confidently does the wrong thing?

So this will be a mix of experiments, small tutorials, tool notes, prompt ideas, security rabbit holes, and postmortems when something breaks. Hopefully the useful kind of breaks.

The things I am watching

Models that can actually do things

The interesting jump is from a model that talks to a model that can do something.

Give it a browser, a shell, a database, a calendar, or a folder full of documents and suddenly the conversation is less about getting a nice answer and more about deciding what the model is allowed to touch.

That is the bit I keep coming back to. A prompt describes what we want. A tool describes what the model is allowed to do. Those are very different things.

OpenAI’s Agents SDK documentation and Anthropic’s guide to building effective agents are good places to start if you want to see how people are putting these systems together without turning them into an unobservable ball of magic.

Prompts as tiny programs

I have started to think of good prompts as tiny programs.

They have inputs, assumptions, rules, examples, and an expected output. They also break when you change one small thing, which is another way of saying they should probably be versioned and tested instead of living forever in a notes app called final-prompt-v7-really-final.

The prompts I find most useful are not necessarily the longest ones. They are the ones that make it easier to tell what the model knows, what it is guessing, and where I still need to make the call.

A shared language for tools

As models start connecting to more services, it would be nice if every tool did not have to invent its own language. The Model Context Protocol specification is one attempt to make that world a little more consistent.

I am less interested in whether MCP becomes the standard and more interested in the underlying idea. Tools should tell us what they can do. A tool called search is not enough. Can it see private data? Does it change anything? How expensive is it? Does it need approval first?

The security bits

This is where the fun gets slightly less fun.

Giving an agent access to a tool also gives it an attack surface. Instructions can be manipulated. A document returned by a search can contain instructions the model was never meant to follow. Secrets can end up in logs. A perfectly normal-looking chain of actions can still lead somewhere unsafe.

The OWASP Top 10 for Large Language Model Applications is a useful map of the recurring risks, including prompt injection, insecure output handling, and excessive agency. Simon Willison’s writing on prompt injection is another practical source, especially for seeing how these failures appear in ordinary workflows.

My default assumption is that an agent is software, not a trusted employee. It should have a narrow identity, limited permissions, a visible trail of what it did, and a big red button somewhere.

Open models and local experiments

Hosted models are convenient. They are also somebody else’s computer, somebody else’s pricing decision, and somebody else’s data-retention policy.

Local and open-weight models make some experiments possible that would be awkward or expensive otherwise. They are not automatically better, of course. Hardware, speed, maintenance, and evaluation still have a habit of showing up at exactly the wrong moment. The Hugging Face Agents Course is a useful way into that ecosystem.

What makes a good AI tool?

For me, a good AI tool is one that makes my judgment better, not one that tries to replace it with a paragraph in a fancy font.

It should have a clear job, a reasonably small permission surface, and enough evidence that I can check its work. It should fail loudly when it is stuck. It should fit into the workflow I already have, rather than asking me to reorganize my life around a dashboard.

Most importantly, it should still be useful on a tired Tuesday with a bad file, incomplete information, and five minutes left. A spectacular demo is not the same thing as a useful tool.

A small promise

I will try to keep things concrete here. If I recommend a tool, I will say what I actually used it for. If I publish a prompt, I will include the bits that made it work. If I write about a trend, I will link to the original source where I can and try not to confuse a prediction with a fact.

I do not have a grand five-year theory of AI. I have a growing list of things I want to try, questions I want answered, and probably a few broken automations to explain later.

Thanks for reading the first page. Let us see where it goes.

Sources and starting points