> Agents need to handle recurring tasks reliably and effectively

This core problem remains unsolved. The solution presented in the article with Human In The Loop and some skill-magic such as "Write principles, not rules etc." is unsatisfactory because it offers no guarantees whatsoever. I find it difficult to harness agents into deterministic workflows which need to produce reliable outcomes.

I was bit confused in the beginning thinking its some product from Anthropic, looks like Warp is the startup, most likely getting rebate on using Claude and providing functionality to users, trying to get them addicted to the feature. And Anthropic is the one thats doing marketing for them cause eventually its their LLM which is being used. Not sure about the agents but this arrangement is definitely increasing the value of both companies in circular fashion.

> What if it turns out the real AGI was the SKILL.md files we made along the way?

I already knew what Warp is (I switched to Ghostty and haven't looked back), but I find it odd that the "The quick pitch" card at the top of this article makes no mention of what the company actually does. Who cares more about their founder/growth/age over that?

So this is Hermes Agent?

https://github.com/nousresearch/hermes-agent

The problem of handling recurring tasks predictably comes down to the probabilistic nature of LLMs, which are based on next-token prediction.

I founded a company called Aide where our goal was to help support teams reliably deploy customer-facing agents without worrying about poor interactions. The first problem we needed to solve was making them deterministic and eliminate the variance that comes naturally with base models.

Getting them to always adhere to brand policy, eliminate hallucination, and stay grounded in data was a fun challenge. Proud to say that we’ve devised a solution that runs well and it’s worked out quite nicely in compliance-heavy and regulated environments.

Nothing to see or learn from... move on.

And are the agents argumentative and condescending I'm wondering.

My way to do the self improving agents is a CLAUDE.md instructed to write my every decision to the decision log with the relevant context. Agent is using it to challenge me, to make things better and remind me why I did something. It also helps with the invalidation.

How is the invalidation handled in Warp? Is it actually self-improving, or just better retrieval?

I tried this and after being unhappy about how much longer the agent seemed to take, I wonder if there’s some tradeoff between a model’s performance (and task cost) and the amount of principles / rules you’ve invoked so far in the session

There is a thriving and vibrant terminal ecosystem out there, with Ghostty, Kitty, Herdr, Tmux, and others leading the way with all kinds innovations and features. I can't imagine how a closed SaaS / freemium, telemetry sending, login requiring terminal like Warp competes in this environment. I know they've got all these enterprise features like runbooks, shared workflows, and some compliance do-dads, but I can't see anyone willingly using this over Ghostty.

> In our series, we highlight how startups are transforming their industries with AI.

I'm sorry, but I don't see Warp transforming shit.

It's strange to me that there is not a more conscious call out that letting an agent edit its own behavior crosses an explicit risk threshold that requires additional controls. They happily drew the whole loop at the top of the page without any human in the loop reviewing the changes the agent is making to its own instructions.

They do get to it later on - casually mentioning it opens a PR for changes that a human accepts in the middle of a paragraph somewhere. Even there though, the focus isn't on risk mitigation (eg: against embedded prompt injection) but rather just "check if this is a good idea or not".

It seems to me that, in engineering terms, identifying self-modifying agent loops and managing the risk of them is going to be one of the key aspects that will emerge in best practises for how these systems are eventually designed.

This is all great until you change or upgrade to a newer model, and you can start over again because some of the directions/rules are now part of the model.

I've already been playing with something similar locally where the agent updates the review skill if human reviewers make valid criticisms of the code which the agent failed to detect.

This reminds me a lot of “dreaming” for skill improvements rather than lost tasks and ideas from past sessions.

> 800K monthly developers build on Warp.

Most impressive.

> Warp, the AI-powered terminal

Ah, now that's a name I haven't heard in many moons. Looks like they found their niche... editing markdown files?

this warp https://www.warp.dev ?

(now I understand why cloudflare, had to?, change the name of their warp)

This is just shilling for the Warp terminal, and this approach could have been a tweet, but okay, Anthropic, whatever helps increase your valuation.

> Agent self-improvement loops built on skills

aka "make no mistakes" in various random Markdown files that "self-improving agents" are free to ignore at any moment

$73M raised? I like Warp but not enough to pay for it. Somebody please make this VC money thing make sense. I can never get the napkin math to work on 90% of things that come through HN. Is it just a "Money Printer Go Brrr' and right connections thing? What am I missing? Is it actually all just fake?

In our Wochenschau, we examine how startups follow the Gleichschaltungsprinzip to accelerate the AI Endsieg.

We do so in a chaotic and unreadable way since unlike Hitler we could not afford editors for "Our Struggle".

[dead]

> Engineers complained that their agent made unhelpful comments and produced low-quality output.

Do you mean they found Claude's output, full of smoking-guns and honest caveats which are all load-bearing and genuinely bite -- they found it "low-quality" by default? Wow. Color me surprised. /s