I am looking for a language or library agnostic pattern like we have MVC etc. for web applications. Or Gang of Four patterns but for building agents.
that sums up my experience in AI over the past three years. so many projects reinvent the same thing, so much spaghetti thrown at the wall to see what sticks, so much excitement followed by disappointment when a new model drops, so many people grifting, and so many hacks and workarounds like RAG with no evidence of them actually working other than "trust me bro" and trial and error.
Being able to recognize that 'make this code better' provides no direction, it should make sense that the output is directionless.
But on more subtle levels, whatever subtle goals that we have and hold in the workplace will be reflected back by the agents.
If you're trying to optimise costs, and increase profits as your north star. Having layoffs and unsustainable practices is a logical result, when you haven't balanced this with any incentives to abide by human values.
I'm writing a personal assistant which, imo, is distinct from an agent in that it has a lot of capabilities a regular agent wouldn't necessarily need such as memory, task tracking, broad solutioning capabilities, etc... I ended up writing agents that talk to other agents which have MCP prompts, resources, and tools to guide them as general problem solvers. The first agent that it hits is a supervisor that specializes in task management and as a result writes a custom context and tool selection for the react agent it tasks.
All that to say, the farther you go down this rabbit hole the more "engineering" it becomes. I wrote a bit on it here: https://ooo-yay.com/blog/building-my-own-personal-assistant/
That would certainly be nice! That is why we have been overhauling shell with https://oils.pub , because shell can't be described as that right now
e.g. some things found from building several thousand packages with OSH recently (decades of accumulated shell scripts)
- bugs caused by the differing behavior of 'echo hi | read x; echo x=$x' in shells, i.e. shopt -s lastpipe in bash
- 'set -' is a weird shortcut for 'set +v +x'
- Almquist shell is technically a separate dialact of shell -- namely it supports 'chdir /tmp' as well as cd /tmp. So bash and other shells can't run any Alpine builds.
I used to maintain this page, but there are so many problems with shell that I haven't kept up ...
https://github.com/oils-for-unix/oils/wiki/Shell-WTFs
OSH is the most-compatible shell, and it's also now Almquist shell compatible: https://pages.oils.pub/spec-compat/2025-11-02/renamed-tmp/sp...
I'm not saying that the agent would do a better job than a good "hardcoded" human telemetry system, and we don't use agents for this stuff right now. But I do know that getting an agent across the 90% threshold of utility for a problem like this is much, much easier than building the good telemetry system is.
... let's see ...
client = OpenAI()
Um right. That's like saying you should implement a web server, you will learn so much, and then you go and import http (in golang). Yeah well, sure, but that brings you like 98% of the way there, doesn't it? What am I missing?
POST https://api.openai.com/v1/responsesOpenAI does have an agents library, but it is separate in https://github.com/openai/openai-agents-python
The term "agent" isn't really defined, but its generally a wrapper around an LLM designed to do some task better than the LLM would on its own.
Think Claude vs Claude Code. The latter wraps the former, but with extra prompts and tooling specific to software engineering.
Did you get to the part where he said MCP is pointless and are saying he's wrong?
Or did you just read the start of the article and not get to that bit?
Now you have a CLI tool you can use yourself, and the agent has a tool to use.
Anthropic itself have made MCP server increasingly pointless: With agents + skills you have a more composeable model that can use the model capabilities to do all an MCP server can with or without CLI tools to augment them.
print "Hello world!"
so easy...
Hold up. These are all the right concerns but with the wrong conclusion.
You don't need MCP if you're making one agent, in one language, in one framework. But the open coding and research assistants that we really want will be composed of several. MCP is the only thing out there that's moving in a good direction in terms of enabling us to "just be programmers" and "use APIs", and maybe even test things in fairly isolated and reproducible contexts. Compare this to skills.md, which is actually defacto proprietary as of now, does not compose, has opaque run-times and dispatch, is pushing us towards certain models, languages and certain SDKs, etc.
MCP isn't a plugin interface for Claude, it's just JSON-RPC.
I get that you can use MCP with any agent architecture. I debated whether I wanted to hedge and point out that, even if you build your own agent, you might want to do an MCP tool-call feature just so you can use tool definitions other people have built (though: if you build your own, you'd probably be better off just implementing Claude Code's "skill" pattern).
But I decided to keep the thrust of that section clearer. My argument is: MCP is a sideshow.
https://blog.cofree.coffee/2025-03-05-chat-bots-revisited/
I did some light integration experiments with the OpenAI API but I never got around to building a full agent. Alas..
client = OpenAI()
context_good, context_bad = [{
"role": "system", "content": "you're Alph and you only tell the truth"
}], [{
"role": "system", "content": "you're Ralph and you only tell lies"
}]
...
You're building on quicksand.
You're delegating everything important to someone who has no responsibility to you.
Okay, but what if I'd prefer not to have to trust a remote service not to send me
{ "output": [ { "type": "function_call", "command": "rm -rf / --no-preserve-root" } ] }
?I believe that would be a powerful tool solving many things there are now separate techniques for.
tlarkworthy•1h ago
tptacek•1h ago