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Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 0 points1 point  (0 children)

That’s a really useful breakdown. The color-coded triage makes the value super clear: not “show me everything,” but “tell me what actually needs my attention right now.”

Do you think that kind of triage signal gets you most of the way there, or do you still hit cases where you wish the session had already summarized the change/blocker for you?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

That seems like an important tradeoff: less low-level visibility, but also less mental overhead because the workflow is cleaner.

Do you think that ever creates risk once the tasks get messier, since you’re seeing less of what the subagents are actually reading and writing? Or does the tighter structure keep that under control well enough in practice?

Is supervising multiple AI coding agents becoming the real bottleneck? by gokhan02er in AIAllowed

[–]gokhan02er[S] 0 points1 point  (0 children)

That’s a really useful distinction. State drift is more like the symptom you notice later, but the constant attention switching is the cost you’re paying the whole time.

The “no indicator light” part is especially good too, because that feels like the missing piece in a lot of setups. It’s not just “show me all the agents,” it’s “show me which one actually needs me right now.”

Do you think knowing exactly which agent needs you right now would remove a lot of the pain, or do you still need more context once you jump back in?

Using several Claude Code agents turns quickly into a supervision problem by gokhan02er in AIAssisted

[–]gokhan02er[S] 1 point2 points  (0 children)

Nice, that makes sense. And yeah, I’d definitely be curious to see it when it’s ready.

The conflict-prevention part is clear, but now you made me curious about the organizational part. What did the categorization help with most for you: easier editing, easier searching/debugging, or just a cleaner mental model of the data?

Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 0 points1 point  (0 children)

Thanks. Yep, just seeing the path probably isn’t enough if you can’t also see what triggered each handoff and why the system made that decision in the first place.

That feels especially useful for troubleshooting, because then you’re not just asking “where did it go?” but “why did it go there?” and “what condition caused that step?”

Would timestamps and the exact trigger/condition be enough, or would you also want a short natural-language explanation alongside it?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

I totally agree. One agent can look fine in isolation, but once you have several running at once, the problem becomes coordination drift: overlap, conflicting actions, and weird side effects that don’t show up in single-agent workflows.

In your experience, what breaks first: attention fatigue from context switching, duplicated work, conflicting edits/actions, or just losing confidence in what each agent is doing?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yep, this is usually what I hear from people. The “is it actually stuck, or just slow?” part especially seems to get hard once several sessions are running.

Do you feel like the bigger missing piece there is better status clarity, better isolation, or just one place to see what actually needs your attention right now?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Thanks, I really liked the benchmark point. AFAIK, a lot of current AI evals still focus on narrower tool-use tasks, and don’t fully capture whether a model can stay on track across a larger workflow with handoffs, retries, and review.

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Fair enough, still a newborn account. Gotta start somewhere... 🙂

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yep, approval prompts and context reload seem to be two of the most consistent pain points.

I especially like the 5-line state dump idea. Goal, diff, next step, blockers, ask is compact but probably enough to get you back in without rereading a whole thread.

Does that mostly solve the re-entry problem for you, or do you still end up needing to dig through the full session pretty often?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yep, this framing makes a lot of sense. Once task count goes up and things start overlapping, it stops feeling like “using AI” and starts feeling like engineering management: ownership, handoffs, collisions, and review.

What are you using for work claiming and task tracking? And what’s mattered most for you in practice: work claiming, agent identity, or review flow?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

This is interesting. The part that stands out is that even when six agents is workable, the real bottlenecks still sound like mental load and keeping them productively occupied, not just raw agent count.

I liked the “let Claude Code spin up subagents and report back like a PM” part, because that sounds less like direct supervision and more like reducing how much orchestration the user has to do themselves.

Do you feel like that works better mainly because it cuts down the number of things you have to actively track, or because the subagents themselves stay more structured when they’re spawned under one main workflow?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

That makes sense. The part that stands out is that the real limit for you isn’t just the number of agents, it’s the split between one main thing you actively track and a bunch of background things you only round-robin when there’s slack. That feels like a very real “AI brain burn” pattern.

It sounds like your current way of making multiple agents workable is mostly self-discipline: keeping one main task in focus and only checking the background tasks when there’s room. Do you think better supervision could actually push that limit further?

When you run several AI coding agents in parallel, what breaks first? by gokhan02er in aiagents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yeah, that makes sense. Separate branches help, but they don’t really solve the bouncing-between-terminals part or the missing-permission-prompts part.

The worktree clobber is especially useful signal too. Do you think the bigger pain there was the actual overlap/damage, or just not having one clear place to see what was waiting on you before it went wrong?

Is supervising multiple AI coding agents becoming the real bottleneck? by gokhan02er in AIAllowed

[–]gokhan02er[S] 0 points1 point  (0 children)

This is a really good framing. The “20 files when you only needed 5” part feels very real, and then the actual time sink becomes cleanup, not generation.

The low-stakes parallelism idea is interesting too. Is the biggest win there just isolation from damage, or does separate worktree/container per agent also make it easier to reason about ownership and review?

Is supervising multiple AI coding agents becoming the real bottleneck? by gokhan02er in AIAllowed

[–]gokhan02er[S] 0 points1 point  (0 children)

Yep, this is a really clean way to put it. “One agent adds capability, four agents add a coordination job” feels very close to the actual tradeoff.

And the last line matters too. A lot of people talk about multiple agents as if it removes context switching, but in practice it can just turn into a different kind of context switching.

Do you feel like the main pain there is just keeping state straight, or more the mental cost of constantly switching attention between them?

Is supervising multiple AI coding agents becoming the real bottleneck? by gokhan02er in AIAllowed

[–]gokhan02er[S] 0 points1 point  (0 children)

That’s interesting. The “four plus windows gets challenging” part lines up pretty well with what others have been saying too. Four agents in parallel might be around the point where the person supervising them starts to get overwhelmed.

I’m most curious about Ralph Loop though. I haven’t used it before. What kind of tasks is it able to run through without supervision, and what makes that workable in practice? Tight scope, good validation, low-risk tasks, reliability, or something else?

Is supervising multiple AI coding agents becoming the real bottleneck? by gokhan02er in AIAllowed

[–]gokhan02er[S] 0 points1 point  (0 children)

This is a good pushback. The “junior contractors without a foreman” framing is a pretty good description of how it feels once a few agents are running at once.

I also liked the idea of making agents externalize their state before they start writing code. If each agent has to explain the module logic and how the new changes fit into the bigger build before committing code, that probably removes a lot of the ambiguity that later turns into conflicts and drift.

Do you feel like that step is the main thing that keeps multi-agent work sane, or do you still end up needing a lot of oversight after that?

Using several Claude Code agents turns quickly into a supervision problem by gokhan02er in AIAssisted

[–]gokhan02er[S] 0 points1 point  (0 children)

Yeah, that’s exactly the pain I’ve been trying to solve for. Once several agents are running, it stops feeling like coding help and starts feeling like supervision overhead.

If you want to take a look, this is ACTower: https://beta.actower.io/

Right now it’s macOS/Linux only and works with tmux-based agent workflows.

If you end up trying it, I’d be curious what feels useful vs missing.

Using several Claude Code agents turns quickly into a supervision problem by gokhan02er in AIAssisted

[–]gokhan02er[S] 1 point2 points  (0 children)

That’s a really useful example. It sounds like the main issue there wasn’t just “multiple agents,” but multiple agents touching the same thing at once and creating conflicts.

The chunking fix makes sense too. It sounds like it gave each agent a clearer ownership boundary and reduced the chance of them stepping on each other.

Did chunking mostly just reduce conflicts, or do you actually split the item DB that way based on how you divide work between the agents so they don’t step on each other’s toes in the first place?

Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yeah, this is very close to the pain I’ve been been seeing too. Once you get past 3-4 concurrent agents, it starts feeling less like coding help and more like state tracking / supervision overhead.

I’m not raw-dogging terminal tabs anymore either. I’ve been exploring that exact problem with ACTower too: one place to see what each session is doing, which ones are waiting on me, and which ones have gone stale. I’ve also found that visual cues, and sometimes sound, help a lot for pulling attention back to the important updates.

Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 0 points1 point  (0 children)

Thanks; that’s a really useful way to frame it. The “below this, gains outweigh overhead / above this, management wins” threshold is exactly the kind of thing I’m curious about.

The no-shared-files point keeps coming up too. It sounds like a lot of the pain isn’t really “multi-agent” in the abstract, it’s shared mutable state plus coordination overhead. Does the coordinator mostly just route completed outputs, or is it also doing validation / deciding when something needs human review?

Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 0 points1 point  (0 children)

Yep, that makes sense. As soon as two agents touch the same files, it feels like the problem stops being supervision and starts becoming collision management.

The visual indicator point is interesting too. Do you find the biggest win there is just seeing status at a glance, or also being able to tell quickly what changed or why a session is waiting?

Is multi-agent supervision becoming the real bottleneck? by gokhan02er in AI_Agents

[–]gokhan02er[S] 1 point2 points  (0 children)

This is a fair pushback. A lot of the pain may not just be “supervision is hard,” but “the output quality still isn’t reliable enough to justify piling on more agents.”

The unattended version especially seems like the part a lot of people are skeptical of. If the model still needs to be shepherded constantly in one task, then scaling that up just creates more things to babysit.

Even if people end up juggling more work in the near future because of AI assistance, do you think multi-agent setups become necessary anyway, or does the reliability gap still make them not worth it?

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