Stripe is the company I usually hold up as the exemplar of polished internal tools. Hopefully this is taken the right way and I don't want to be negative towards the teams working on this, but I see a distinct lack of polish in these tools and presentations. Some examples:
- Unnecessary AI copy throughout the interfaces like "Browse, discover, and manage skils for your agents", "No favorites yet — hover a card and click the star to pin it here", "One execution environment, shared across agents". These instantly read as AI copy and decrease my enthusiasm.
- Inconsistent, AI-sloppy look-and-feel with different typefaces spattered across the interface.
- The session metrics slide looks busy and AI-generated. It repeats 360,014 sessions in one of the cells at the top, but also has a "360K sessions" in the heading.
I don't know if I'm the only one that notices this stuff, or whether others see it too.
I think there’s been somewhat of a mask-off moment post-AI in which we’ve realized that a lot of the careful and considered output we’ve come to expect of some companies wasn’t out of a respect for the craft or desire to produce “good work”.
Pre-AI the attitude was: if we are going to do something, it is going to use up our precious resources, so we should do it well, because our staff are capable and the marginal cost of doing it well vs. doing it at all is negligible.
Post-AI: we can churn out things quickly, we don’t have to worry about resource allocation, churn churn churn!
Ultimately, it is pragmatic for businesses to behave this way, but it is a shame for those who love the craft. I think we took for granted the beautiful ornate hand carved furniture era of software engineering. We are now in the ikea era.
I don't think the beautiful ornate hand carved furniture era of software engineering has existed for many decades now.
What's happening right now is more like, we're moving from a hyper-optimized era of just-in-time manufacturing (in software, this is called "Agile", "scrum", "sprints", etc.) to getting our manufacturing outsourced to another country. In software, the "other country" is AI.
> Ultimately, it is pragmatic for businesses to behave this way, but it is a shame for those who love the craft. I think we took for granted the beautiful ornate hand carved furniture era of software engineering. We are now in the ikea era.
I feel this so much. I thought AI would make it easier to get lots of hand-carved ornate furniture; but right now what we are getting looks like the typical Ikea product line -- a bunch of mismatched pieces by a host of different designers with no common underlying design theme or continuity (I'm talking specifically about the looks and design, not the materials).
I am really interested in this topic, but man I had to give up reading the document because there were too many paragraphs that contained lots of words but no additional detail that I could actually use. It could be AI-generated or human-generated with too many buzzwords. Doesn't really matter, still gave up.
It sure feels like companies have lost their way now. It's no longer about high polish and quality, and all about volume of features. I don't think users want to be overwhelmed with features, they want things that work nicely, and consistently.
Working in b2b SaaS, there’s a real gravity toward checkbox-driven development. Any time we lose a deal because a competitor had feature A, the immediate response is “we need to build feature A.”
And from a sales perspective, that’s probably not wrong.
I think a new (or newly critical) engineering challenge is how to build in a way that keeps a codebase that’s churning out features from crumbling under the weight.
That's funny, because I recall in 2015 hitting their endpoint for a large-ish customer, and if you added a boolean to get the total result count, it would 500 every time, presumably because it was doing some kind of "SELECT count(1)" over a postgres table. IIRC, Stripe was ruby internally for a long time, no?
Also their documentation was frequently just straight up incorrect (as in the described json schema for a response was violated. keys missing, different field names, etc.).
But it's been over 10 years, has it improved since then? I'm still in my impression of their stack from back then, although they were decently mature by then as well.
I recently had to implement a stripe payment system for a client (having never used it before) and found their documentation to be accurate and excellent (at least for the Java SDK and relatively basic use case).
No love for Stripe but IMO their documentation feels like a first-class product.
I think the specific cases where it failed was when the customer was one of their earlier ones. My guess is that some data schema migration happened, and older data was being served which didn't quite match. So perhaps recency wouldn't potentially even surface those problems either.
They have published a bunch of stuff about their internal tools in the past. Look up https://stripe.com/blog/stripe-home and compare it to this, as an example.
It's always possible that in reality they were always a bit less polished behind the scenes though.
Maybe they have jobs to do so they offloaded a lot of the presentation work to AI, assuming reasonable people would not judge based on surface level copy style.
The issue is not that that the presentation work has been offloaded to AI. It's that the result is sloppy.
People are always going to judge based on what seems "surface-level". Stuff like legibility also matters just for data comprehension, the AI tic of constantly repeating key numbers all over a presentation or webpage actively hurts legibility.
Very cool demonstration of managed agents built for the needs of their own business.
I think this is where a lot of companies are going to go: on-prem platforms that give various teams access to agents that are as powerful as coding agents, but much more managed and governed.
The readme was very clearly AI-written (or at least heavily AI-assisted)... not saying that's a bad thing, and I agree it's fairly well written... but it is very obviously AI
k, I just looked through it for where the latest is. First of all, the earliest version were fully written by me, and then lightly edited with AI. The current state are as follows: intro sentences written by me, "why lightspeed" section too. Quickstart AI written, Features mostly written by me, but AI keeps interfering there. Design was also written by me several times, it also used to be much longer, but I just noticed AI put one a few paragraph of slop in there, I think that's the most egregious--shame on me for not caching it. So, if I look at the readme right now, I would say it's about 60% me. It can be better, I'll make the effort because it really bothers me if readmes are not (mostly) written by humans.
I really don't have a problem with you using AI, as long as you review and validate/test what it writes, and seems like you did that. Great project btw!
I read a buzz word "Knowledge AI Platform" but I did not see any specific feature helpful for knowledge management like verification or transparency. It is more like any generic Agent builder. Maybe it meant to justify building something internally.
I think that’s because none of them go past “I’ve set up agents to be orchestrated this way” and that’s about as impressive as “look at my cloudformation template”.
> Building a standalone agent product wouldn't work. Instead, it would force users out of their natural workflows and into a new app.
I am finding the opposite to be true with one of my clients. They explicitly want a new channel. The chat-style UI/UX is vastly preferred over their poorly maintained internal tooling.
I suppose if you are Stripe actual, most things would be well designed and more difficult to abandon. Most places aren't like Stripe.
The hard part is bringing everything that matters to the new channel, but it's certainly feasible to do this. I am doing it right now. We will soon be able to delete hundreds of wildly inconsistent cshtml views and related controllers in favor of a single agent tool that applies json patches. This will easily cover 99.9% of use cases. A manual json editor is retained for the rare case where we need some multi-megabyte merge operation.
Cool to see, but wondering what the upside of this is vs what notion is building with their own agents. I can imagine notion agents can do something very similar to this (and with the same amount of control / security) and at what point would it make sense for a company to build their own vs buying into one..
Ahhh I built an internal set of agents to run our company (finances, all info in the karpathy-style LLMWiki and a database of clients, contracts, billing, time tracking, all managed by MCPs, etc) and it's also called Kai (company name is Kaizen)
I want to read this, but the ever-changing linear gradient of the background is too visually distracting. I tried to get around it by highlighting text I want to read, but since the background is changing underneath it, so is the highlight. My eyes hurt.
Wonderful name. You may know this, but Kai means food in Maori. Many words and many, many place names also embed it, e.g Moana == sea, so Kaimoana is seafood.
TIL people still fall for eh I mean use Langchain. Sorry, low value comment; I don’t know how to do that differently; it is such bad garbage since day one and strangely it did not improve. Sorry anyway for the comment, at least it was not LLM generated?
my biggest code smell is debugging. Trying to step through the code is slow because of how much the debugger has to keep track off and it's a lot of step into / jumps until you get to the piece you actually care about. Then you also see stuff like "if langchain_v2: <branch> elif langchain_v3: <branch> else ...." ... breaking changes are ok! I feel like at this point they're hopelessly spaghettied and need a more opinionated approach to clean up a lot of the mess.
Since others are asking: My current goto is pydantic ai and it's been doing pretty well for my use cases.
It (and most other systems) abstract the wrong concepts when you want to create an agent. They promote what was likely never a great strategy but even parsimoniously what are strategies that were good ideas months or a year back.
For example how langchain or whatever else handles subagents and deep research is laughable even today.
So whats the recommendation? Just use pydantic and code everything up yourself. Maybe strands I dont know.
Which specific strategies do you think are outdated, and what would you recommend instead? This sounds more like a criticism of older versions of LangChain than its current APIs. The post also uses deepagents, a separate package in the same ecosystem. Is there something in the current implementation of either that you’re referring to?
The fact that Stripe are touting a production product that (at face value) benefits their business seems to suggest that maybe crap is subjective and as ever, being overly opinionated in an emerging space might actually be a blocking mindset rather than a positive one.
It's a lot of sugar and high level abstractions on top of existing things, I find using the existing things not that complicated or difficult and I struggle to see the value on using the framework as it doesn't sit well with my way of abstracting the stack.
It's very close to the direction we're taking for windmill.dev, we call it "operator builders" rather than focus on "knowledge graph" which the article is very light on details of.
What I'm mostly reading is a developer platform and runtime where users can build agents that can run tools and for that you need a secure code runtime, ACL/permissions, easy way to build apps or what cloudflare OS call gadgets. We're betting on this too at https://github.com/windmill-labs/windmill, very curious to see if that's the future for most enterprise and if a model where everyone vibe-code/fork cloudflare OS to their enterprise need is the future, or a more exhaustive/enterprise platform like ours does.
By choice. I currently work at Stripe (opinions my own, I'm just some guy) and was here when it released. It's a genuinely useful tool, which explains the widespread adoption.
Size and scale, I would think. An out of the box solution probably doesn't quite have the same capabilities as something they can (and now have to) manage in it's entirety.
Stripe probably WANTS to be opinionated about how their company works with the tools.
Somewhat interesting how the design of Stripe's developer-focused landing pages used to be the "cream of the crop," so to speak, but now they just look like Claude run amok. I wonder if the web design "skills" for Claude, and other LLMs, were overly trained on Stripe.
"Sharing the substrate forces discipline and creates a flywheel: improvements to the execution environment benefit both internal and product agents simultaneously."
1. The whole interface feels way too vibe coded with tons of unneeded stuff. Why does it have a console and snake game built in? Why does it have annoying sounds? Why is it full of AI slop writing? The latter is especially confusing because the first person listed under authors is a "Technical Writer". I guess the interface is the general stripe.dev page not exactly related to Kai but the post definitely is pure AI slop writing.
2. It seems to claim things that might not be substantiated like the following: "When Account Executives use Kai, they produce 2x the sales activity, create 17% more opportunities, generate 26% more revenue opportunities, and close 39% more deals when compared to the same sellers in weeks they don't use it." Correlation is not causation. If sales people have less activity (vacations or sick days etc) then they also wont use this tool much, it doesn't mean all the increase in sales is because of the tool.
3. the post talks a lot about how great this tool is but it describes nearly nothing of value to the outsider who can't access it. What were the valuable lessons learned? What's neat about it? There is not much meat imho.
It doesn't live up to my usual expectations from Stripe.
- Unnecessary AI copy throughout the interfaces like "Browse, discover, and manage skils for your agents", "No favorites yet — hover a card and click the star to pin it here", "One execution environment, shared across agents". These instantly read as AI copy and decrease my enthusiasm.
- Inconsistent, AI-sloppy look-and-feel with different typefaces spattered across the interface.
- The session metrics slide looks busy and AI-generated. It repeats 360,014 sessions in one of the cells at the top, but also has a "360K sessions" in the heading.
I don't know if I'm the only one that notices this stuff, or whether others see it too.
Pre-AI the attitude was: if we are going to do something, it is going to use up our precious resources, so we should do it well, because our staff are capable and the marginal cost of doing it well vs. doing it at all is negligible.
Post-AI: we can churn out things quickly, we don’t have to worry about resource allocation, churn churn churn!
Ultimately, it is pragmatic for businesses to behave this way, but it is a shame for those who love the craft. I think we took for granted the beautiful ornate hand carved furniture era of software engineering. We are now in the ikea era.
What's happening right now is more like, we're moving from a hyper-optimized era of just-in-time manufacturing (in software, this is called "Agile", "scrum", "sprints", etc.) to getting our manufacturing outsourced to another country. In software, the "other country" is AI.
I feel this so much. I thought AI would make it easier to get lots of hand-carved ornate furniture; but right now what we are getting looks like the typical Ikea product line -- a bunch of mismatched pieces by a host of different designers with no common underlying design theme or continuity (I'm talking specifically about the looks and design, not the materials).
I think by any measure that OS/360 would be "high craft".
And from a sales perspective, that’s probably not wrong.
I think a new (or newly critical) engineering challenge is how to build in a way that keeps a codebase that’s churning out features from crumbling under the weight.
Also their documentation was frequently just straight up incorrect (as in the described json schema for a response was violated. keys missing, different field names, etc.).
But it's been over 10 years, has it improved since then? I'm still in my impression of their stack from back then, although they were decently mature by then as well.
No love for Stripe but IMO their documentation feels like a first-class product.
unless you work there how would you know this?
It's always possible that in reality they were always a bit less polished behind the scenes though.
People are always going to judge based on what seems "surface-level". Stuff like legibility also matters just for data comprehension, the AI tic of constantly repeating key numbers all over a presentation or webpage actively hurts legibility.
I think this is where a lot of companies are going to go: on-prem platforms that give various teams access to agents that are as powerful as coding agents, but much more managed and governed.
I'm betting on this with my open source project, Lightspeed: https://github.com/smartcomputer-ai/lightspeed
the author commented otherwise.
> There might be some AI-isms in there, because agents just can’t help themselves to dump their arcanae in there.
I think that’s because none of them go past “I’ve set up agents to be orchestrated this way” and that’s about as impressive as “look at my cloudformation template”.
I am finding the opposite to be true with one of my clients. They explicitly want a new channel. The chat-style UI/UX is vastly preferred over their poorly maintained internal tooling.
I suppose if you are Stripe actual, most things would be well designed and more difficult to abandon. Most places aren't like Stripe.
The hard part is bringing everything that matters to the new channel, but it's certainly feasible to do this. I am doing it right now. We will soon be able to delete hundreds of wildly inconsistent cshtml views and related controllers in favor of a single agent tool that applies json patches. This will easily cover 99.9% of use cases. A manual json editor is retained for the rare case where we need some multi-megabyte merge operation.
Happy to be in such good company!
The agent has switched a few times, initially NanoClaw, then Hermes, and now Vercel's Eve (maximum customizability).
NextJS/Shadcn web app, postgres db, eve agent layer, MCP tools (over 100 so every single thing can be done by an agent), SwiftUI mobile app, etc.
Integrations with gmail, gcal, gdrive, quickbooks, using mdx for the wiki displays (so we have rich diagrams, 3d models, etc).
We write about it here: https://kznconsulting.com/work/how-we-run-kaizen
https://gp-tree.com/enterprise
Our developer docs for GPTree knowledge engine here https://gp-tree.com/docs/knowledge-api
Since others are asking: My current goto is pydantic ai and it's been doing pretty well for my use cases.
For example how langchain or whatever else handles subagents and deep research is laughable even today.
So whats the recommendation? Just use pydantic and code everything up yourself. Maybe strands I dont know.
I use Apache Burr but it's not as good as LangChain. I just don't want to use software whose website has a pricing page if it's not a SAAS.
What I'm mostly reading is a developer platform and runtime where users can build agents that can run tools and for that you need a secure code runtime, ACL/permissions, easy way to build apps or what cloudflare OS call gadgets. We're betting on this too at https://github.com/windmill-labs/windmill, very curious to see if that's the future for most enterprise and if a model where everyone vibe-code/fork cloudflare OS to their enterprise need is the future, or a more exhaustive/enterprise platform like ours does.
By force or by choice?
seems every company that has spare engineering resource all builds such thing internally
Stripe probably WANTS to be opinionated about how their company works with the tools.
> No existing tool could handle the data security requirements and specific workflows Stripe needed
The whole piece is mostly AI slop.
"Sharing the substrate forces discipline and creates a flywheel: improvements to the execution environment benefit both internal and product agents simultaneously."
No human would write that.