Hundreds of bots modifying thousands of microservices may sound good on the surface, but all those thousands of microservices make up an architecture and a product.

Agents aren't very good at carrying the entire model in their context, so when they reason about a small piece of code, they often come up with something that hurts other parts of the code (especially as the KLOCs pile up). The complexity hasn't been replaced, only moved. And guess what's going to happen when all of these microservices become even more of a moving target than they already are?

AI is capable of improving productivity, but this approach sounds more like a nightmare in the making.

A wise troll once said:

> best weapon against complexity spirit demon is magic word: "no"

In counterpoint, I believe small teams can remain small. Small teams can ship simple monoliths with high velocity, commit count, and quality. Service orientation didn’t suddenly become low-cost because of agents; the boundaries between multiple services that version and deploy independently are still tricky beasts to wrangle. And it’s not clear why “running more agents” is inherently desirable or impactful; my small team’s (admittedly anecdotal) experience is that the value quickly saturates.

I have already commented about this is probably the worst advice I have heard in a long time, and I would definitely not let this guy anywhere near a codebase I had to maintain.

But this is also essentially blogspam. The author intentionally includes two screenshots which add absolutely nothing to their point and which are too small to read. When you click the second one, you are not taken to a larger version, you are taken to the frontpage of his company website which features the same screenshot.

Wait two years until we have sufficient churn of senior talent in the teams. Then all of the services will have outages daily.

Only the seniors who know their systems are keeping the lights on today by keeping bs commits out.

Once they burnout and quit, nobody will have a freaking clue what the LLMs have done and why services are down.

I'm really skeptical you can ship ~10 PRs a day per person unless these PRs are tiny pieces of one feature or all of them are tiny bugs that are each a 3 line fix that you could review instantly. Otherwise how can you confirm that the AI even did the right fix or feature correctly? That you even wanted that feature done that way?

I hope people don't call automations as "teams". If you are calling it a team just because it "does" work, then CPU cores and threads are also a team, though not so probabilistic (intelligent). They do get the work done.

Funny take. Monoliths are actually better then microservices because of the context they carry. At the end developers are supposed to run a product, not to keep mindlessly running agents just because they can

I saw the `require('gulp')` and the memories def came back. That's def how we used to do code ~10 years ago. I still don't like the multi-threaded PER PROJECT too much, I prefer having 2 projects and switching context window, I find the current tools (at least the ones I know) are a bit underwhelming for multi-threading. But I'm also trying to upgrade my knowledge.

A good way I've found, since I do a lot of OSS and have my own libraries, when I find a bug in one of those libraries I can work on the same project on the main window while fixing the library on another window. I normally need to tell the main one "let's skip this for now, I'm fixing the library" meanwhile or similarly.

> If you have a large monolithic service where every change has to be coordinated carefully..

Microservices are WAY harder to coordinate for deployments. You end up with feature service dependencies, and you are back to the same coordination, but now harder to discover down stream dependencies..

Microservices can trample on each other just as easily as monolith internals can. If anything, the friction of reconciling changes in a monolith is useful signal that conflicting changes happened, and it takes slow and flaky e2e tests to replicate in microservices. It's not like you're resolving the merge conflicts by hand.

Pretty sure Uber ended up with thousands of microservices because they used to tie owning a service to perf and promo, and were trying to cut down the number for years. It's hilarious to see this interpreted as an intentional choice.

> So Uber’s approach to modularity may have seemed extreme at the time, but it could become the new normal.

I doubt it. This seems to conflate code modularity with service modularity. Moving complexity from the codebase into operations is counterintuitive to at least the way I use LLMs.

One of my predictions for software in the AI age is that we will end up with nano-services. The thinking is this:

1. LLMs perform best with tight, focused context.

2. Encapsulating specific well-defined functionality into a single service, tool or API inherently limits the amount of code it requires, and hence the context an LLM requires to reason about it. The smaller the codebase, the better an LLM will do with it.

3. Code is already increasingly written by swarms of agents that tend to step on each others' toes, and hence a way of isolating them would be great.

4. Combining the 3 points and taking them to the logical conclusion, we end up with nano-services.

It's basically the Unix philosophy, just scaled up to distributed systems. For all its other issues, the core philosophy worked pretty well for Unix tools, so it should work well for nano-services too.

Currently the "pipes" between these services are network calls, but they need not be. One could imagine an architecture where these components are composed either as direct function calls or remote network calls depending on access patterns and resource requirements for a given instance. Never worked with Elixir but I think that might fit the bill?

Maybe every sufficiently complicated distributed system will end up re-inventing an ad hoc, informally-specified, bug-ridden, slow implementation of half of Elixir.

My biggest issue with these type of posts is that they never answer the "why". Hell, they don't even ask the "why".

> The more modular your code, the more agents you can run

OK, but why would I want to run more agents? So I can be more productive? What does this productivity lead to? And are we actually being more productive? Take a look at Bun's repo on GitHub which seems to be fully automated. Well over 5000 PRs open.

What's the use? How can we justify these 5000 PRs? Over the past years, software has become considerably more shit. Are these 5000 PRs improving the quality of software?

Is the end-user reaping the rewards? Are they getting better software, cheaper?

The answer to all of those is going to be "no".

And let's take Uber for example. They have many teams, and many more times the services. Has ride hailing become cheaper? No. Has it become more efficient? No.

Nothing is getting better, but at least we're all off worse!

> A small team today, running 20-100 agents in parallel, might generate 500 commits/200 pushes/100 PRs

Who the fuck is writing the requirements in that story?! Setting aside the problem of pushing AI-generated code that nobody has read straight to production, the reason why you're pushing code to production in the first place is to implement a product that is solving a problem for your users. And the problem I have with the hypothethical workflow described above is: How is any product owner supposed to come up with hundreds of feature or improvement requests a week?

i agree with this.. but also think it could stress a couple things more:

- satisfying the needs of parallel agentic development is wholly aligned with the optimal DevSecOps CI/CS/CD WhateverTerm models out there. And that's rad, bc a lot of orgs have a reference frame to map to.

- microservices, monoliths, monorepos, mammoths, whatever.. The code and services can be structured however, so long as the release capabilities are modular and governable/manageable/auditable/flexible/transparent/etc. A killer workflow allows for tight independent releases, but not chaotic, with proper add'l structure/scaffolding to satisfy that list above. Microservices and smaller repos can help with the context window bit initially, but you can rig up and kind of local llm-focused setup to allow for selective context and holistic context (across N repos or N projs within monorepo)

- strategy: use the robots to fix the problems in your PDLC/CICD/ABC so that the robots can help you out more, and keep iterating on that

Is this satire? Changing your easy to maintain monolith architecture to a microservice architecture to enable parallel agent development, really? That's got to be some of the worst advice I'd heard in a long time.

You will have conflicts during parallel work if people are changing features that are related. You will also get these conflicts with a microservice architecture and hundreds of independent repos, but now it is not a merge conflict because you touched the same syntax, it is a semantic conflict.

And didn't Uber also have a famously slow and convoluted CI pipeline where it took an enormous amount of time and resources to build anything?

The reason micro services became fancy is because they could split up large groups of people. They could work on different parts more autonomously without all being dependent on each other.

It has an overhead but as scaling the amount of people was worth it there was an advantage.

Where did this work: where the interfaces between those groups of people were agreed upon.

A team could call: finance->billTrip(TripObject) via an API and the finance team became independent. The micro services had an API which was documented. They could move from Stripe to SAP to a custom solution internally without bothering the other teams.

Now we have AI.

Those interfaces are still needed. But is there still an advantage to hard separating those interfaces in real micro services, with separate databases for each team, over http or other protocols?

On some point it may. A service which needs to scale unlimited (like RenderThumbnails with hundreds of thousands of call per day) might have that. But if you send out 1000 invoices per day it might not be needed for that at all.

In Rails they have active job for it for example. Same codebase but a scalable worker for longer running jobs.

AI is capable of quickly keeping function calls consistent internally. So a hard coded API is way less useful now for those services which don’t need the scale. An interface could suffice just fine. No human would ever read the finance API anyway.

Keeping consistency in the monolith part is way easier. That is statically testable, quicker testable and there are plenty of tools. All disadvantages on micro services on those calls are just a waste.

One thing where I think hard boundaries are useful is on really blocking AI coding tools. With microservices you are able to physically prevent Claude or others to modify the other interfaces. That way it prevents hallucinations, quick fixes and other workarounds the tools like to do. Just because an AI wants to get sometime done it will sometimes skip corners.

I see that as a current state which will be fixed quite soon. The models and their harnesses will become increasingly better in preventing stupid corner cutting to get the fix out.

When that is done: why would you want more complexity with micro services instead of less?

Instead of we all implementing microservices and other structures we should get the harnesses right to respect interfaces and boundaries.

And in the micro services era we had meetings to sync the API’s between them. So maybe the AI’s should meet about the interfaces, with a good coffee.

Thousands of microservices = thousands deployments that can fail, that needs to kept compatible, that needs to be able to find each other, that needs to be monitored. This is just pushing the pain downwards.

> if you have thousands of microservices

You just replaced ns memory accesses by ms API calls. Just the moment people were starting to maybe start thinking about performances again this train-wreck of power wastefulness had to appear.

I am a team of one, and I don’t use agents

There is no "small team" pushing 100PRs/day, agents or not.

He misses the bit about Microservices where you need to integrate changes across services and avoid undocumented API spec from your changes blowing something up. Getting alignment and so on. There are probably agentic solutions but this article misses a lot of detail and doesn't even hand wave it.

You can use this as a canary.

The out-of-control factor = the number of parallel working agents : the number of human programmers.

1. If the factor > N, you're losing control and there will be no organizational wisdom passed down.

2. If your team can't function with the factor <= N, your architecture is way too complex.

Choose N over your prior. My recommendation is 1.

Then you’ll have “GitHub” uptime, breaking prod every day.

If you want a truly free to use agentic IDE

check shikigami.dev - free to use - not a vc baked, indie product

Why not just bake an agent into every service and chat with it? Sounds more fun at least.

> Fire up a coding agent for each one, tell it to “improve performance”, and there’s a good chance you ship significant improvements across all of them.

But I can do this like what, once or twice? Maybe once every release? Then my agents finish "improving performance" and it's Monday 10am. What the fuck I'm supposed to do the rest of the week?

Call me old school but I feel like the solution to humans clogging up the slop cannon will be for AI generated code to look more human.

That is, the code should be correct but also idiomatic.

I'm a 1 person dev team who wrote frontend code that outperforms React.

Is was successful because I didn't use any sort of LLM assistance.

Agentic coding puts a lot of pressure on team communication. Individuals produce more output. So there now is a lot more to discuss and synchronize on. This favors smaller teams that typically are responsible for more things. That doesn't necessarily mean fewer people. But I do think it favors having more smaller companies over fewer larger ones that are each able to specialize more. It also means that companies that currently outsource all their development or buy SAAS products now should consider in housing some things again. A lot of people worry about work disappearing. I think the opposite is going to happen: more work popping up in a lot of new places. Because doing that work is now feasible and doable.

Staying on top of work done across many teams is going to be a huge challenge for larger companies and it's going to cause them to organize and hire very differently. Getting this wrong means teams diverge much quicker than they used to and everything gets misaligned much quicker. Some team might launch a product before some other team that was considering to do a similar thing is even aware that is happening. As most engineers might appreciate, the easiest way to tackle complexity is to just work on cohesiveness and coupling. Small teams with few dependencies working on a coherent thing will be much more effective than large orgs with a lot of inter team coupling and no coherent plan.

I'm in my fifties so, I've been around for a while. I currently work in a very small company (3 people) so I'm used to doing things by myself. I also used to work in traditional teams inside large multinationals. Very different game. You spend non trivial amounts of time communicating with people in big organizations. And you basically only get responsibility for a tiny amount of functionality. I had to learn a lot after I left the safety of a big organization. When everything is your problem, you need to skill up in a hurry to deal with all the challenges effectively.

In my current role, I do basically everything vaguely technical. And non technical as well. And it is more than ever since AI coding entered the mix last year. I stopped referring to myself as a backend person. Because I also do UI, devops, websites, IT infrastructure, etc. And a bit of sales, marketing, etc. I'd add management but we're such a small team that I suffer a little from impostor syndrome on that front. But I can do the job if I need to.

I had a whole hiring plan that I developed three years ago that we never executed on. It had all the traditional dev team roles spelled out. That plan is obsolete. These roles will probably never be filled. I need different people though. I need more people like me that can do everything I do when I'm not around and people with complementary skills that are strong where I'm weak. But I have much less need for specialists. I mainly need generalists with attention to detail that can deliver complete working products and systems. I might want a product focused person but not a dedicated product manager. If you can specify it in an issue tracker, you can learn to prompt an AI as well. But I still need a good product plan and roadmap. In the same way, I expect designers to shape UI work directly and not via a separate UI team. I expect them to own the front end experience and drive it. I still need people that are strong in these roles. But not exclusively. Good small teams have less people than all the traditional roles you find in larger traditional teams. That calls for people with overlapping skills that can put on different hats as needed that complement each other.

There's another way to get this kind of parallelism without the mess of microservices.

Build a well architected monolith and be super strict on single purpose and keeping modules separated from each other.

> While I’m not saying every LLM user is an imbecile, they’re built to convince the mediocre and incurious that they’re remarkable, and it turns out that a great many of them run venture capital firms and Fortune 500 companies.

> I also want to be clear that while there are sane and normal people who use these things, they’re mostly drowned out by a crowd of people that oscillate between bootlicking and regurgitating capitalist mythology in a way that makes it hard to trust anybody who spends significant amounts of time using an LLM.

> One thing you’ll notice about the most moistened AI boosters is that they lack much degree of pride in their work. Everything they say must, at some point, compliment the mindless, unprofitable, unreliable tool underneath it — how “incredibly powerful” it is, how it’s “only getting better,” how it’s “only the beginning” of something that’s eaten over a trillion dollars and absorbed the majority of venture capital.

Ed Zitron. The Revenge of the business idiot.

https://www.wheresyoured.at/the-revenge-of-the-business-idio...

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