Multi-Agentic Software Development Is a Distributed Systems Problem

ttie-in about 8 hours ago 19 commentsRead Article on kirancodes.me

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xer•about 1 hour ago

The fundamental assumptions of distributed systems is having multiple machines that fail independently, communicate over unreliable networks and have no shared clock has the consequence of needing to solve consensus, byzantine faults, ordering, consistency vs. availability and exactly-once delivery.

However, AI agents don't share these problems in the classical sense. Building agents is about context attention, relevance, and information density inside a single ordered buffer. The distributed part is creating an orchestrator that manages these things. At noetive.io we currently work on the context relevance part with our contextual broker Semantik.

yangshi07•37 minutes ago

After you point this out, it is obviously right!

21asdffdsa12•about 2 hours ago

To be honest humans often have no overview over a application either. We navigate up and down the namespace, building the "overview" as we go. Nothing i see what prevents an agent from moving up and down that namespace, writing his assumptions into the codebase and requesting feedback from other agents working on different sets of the file.

airstrike•about 1 hour ago

"Nothing" prevents it other than the fact that an agent doesn't really have memory and has a pretty limited context, hallucinates information, mistakes metadata with data, and so on.

The path forward is always one that starts from the assumption that it will go wrong in all those different ways, and then builds from there

SamLeBarbare•about 4 hours ago

Conway’s law still applies.

Good architecture, actor models, and collaboration patterns do not emerge magically from “more agents”.

Maybe what’s missing is the architect’s role.

falcor84•about 7 hours ago

The thing that TFA doesn't seem to go into is that these mathematical results apply to human agents in exactly the same way as they do to AI agents, and nevertheless we have massive codebases like Linux. If people can figure out how to do it, then there's no math that can help you prove that AIs can't.

bloppe•about 6 hours ago

Humans can also count the number of Rs in strawberry, but good luck proving that mathematically

EagnaIonat•about 5 hours ago

LLMs don't see words. They see tokens, which is why previously they had a hard time counting the r's.

You can certainly prove that mathematically, and giving that proof to an LLM it will give you the correct answer. Which is a prompting technique btw to improve accuracy of an LLMs results.

Although the more recent LLMs have built in approaches to answer this question.

Gemma4 for example:

-----

The user is asking for the number of 'r's in the word "strawberry".

* S

* T

* *R* (1)

* A

* W

* B

* E

* *R* (2)

* *R* (3)

* Y

There are 3 'r's.

-----

GPT-OSS-120b example.

-----

The user asks: "How many r's in strawberry." This is a straightforward counting problem: count the letter 'r' in the word "strawberry". The word "strawberry" contains letters: s t r a w b e r r y. Count r's: there are three 'r's? Let's check: s(1) t(2) r(3) a(4) w(5) b(6) e(7) r(8) r(9) y(10). Actually the word is "strawberry". Let's write out: s, t, r, a, w, b, e, r, r, y. So there are three r's: at positions 3, 8, 9. Yes, three r's. So answer: 3.

But we may also note that "r" appears three times.

Thus answer: 3.

We can provide a short answer.

Thus final: There are three r's in "strawberry".

----

kang•about 3 hours ago

Doubt if you can make a dumb model smart by feeding it proofs

tacotime•about 5 hours ago

I doubt it is possible to mathematically prove much inside of a black box of billions of interconnected weights. But at least in the narrow case of the strawberry problem, it seems likely that LLM inference could reliably recognizing that sort of problem as the type that would benefit from a letter counting tool call as part of the response.

jbergqvist•about 6 hours ago

Doesn't this whole argument fall apart if we consider iteration over time? Sure, the initial implementation might be uncoordinated, but once the subagents have implemented it, what stops the main agent from reviewing the code and sorting out any inconsistencies, ultimately arriving at a solution faster than it could if it wrote it by itself?

timinou•about 4 hours ago

I'd wager that a "main agent" is really just a bunch of subagents in a sequential trench coat.

At the end, in both cases, it's a back and forth with an LLM, and every request has its own lifecycle. So it's unfortunately at least a networked systems problem. I think your point works with infinite context window and one-shot ting the whole repo every time... Maybe quantum LLM models will enable that

lifeisstillgood•about 6 hours ago

It’s not a solution but it’s why humans have developed the obvious approach of “build one thing, then everyone can see that one thing and agree what needs to happen next” (ie the space of P solutions is reduced by creating one thing and then the next set of choices is reduced by the original Choice.

This might be obvious to everyone but it’s a nice way to me to view it (sort of restating the non-waterfall (agile?) approach to specification discovery)

Ie waterfall design without coding is too under specified, hence the agile waterfall of using code iteratively to find an exact specification

vedant_awasthi•about 3 hours ago

Makes sense. Coordination between multiple agents feels like the real challenge rather than just building them.

cooloo•about 3 hours ago

Well it starts with agree list. I don't agree next gen models will be smarter. I would argue no real improvement in models in last couple of years just improvement in stability and tools (agentic ones) around it.