Towards a science of scaling agent systems: When and why agent systems work(research.google)
19 pointsby gmays4 hours ago5 comments
  • detroitwebsites20 minutes ago
    The "alignment principle" vs "sequential penalty" finding mirrors my production experience exactly.

    I run a multi-agent system where specialized agents handle different business functions (customer support, code review, deployment monitoring). The key insight: task decomposability determines architecture.

    Parallelizable tasks (analyzing independent customer tickets, running separate test suites) show massive gains with independent agents. Sequential workflows (debugging a specific issue that requires following a chain of logic) degrade with coordination overhead.

    The "tool-use bottleneck" is real. We hit it around 12-15 tools per agent. The coordination tax becomes severe. Solution: role-based tool access. Support agents get 5 tools, deployment agents get 8, code review agents get 6. Overlap is minimal.

    One counter-intuitive finding: persistent memory per agent beats centralized knowledge. Each agent has AGENTS.md (instructions), TOOLS.md (available actions), and memory/ directory (session logs). Agents learn from their own mistakes without polluting each other's context.

    The error amplification metric (17.2x for independent vs 4.4x for centralized) explains why we use a hub-and-spoke model with human checkpoints at handoff boundaries.

    Documented these patterns at howtoopenclawfordummies.com for anyone building similar systems.

  • localghost3000an hour ago
    I’ve been building a lot of agent workflows at my day job. Something that I’ve found a lot of success with when deciding on an orchestration strategy is to ask the agent what they recommend as part of the planning for phase. This technique of using the agent to help you improve its performance has been a game changer for me in leveraging this tech effectively. YMMV of course. I mostly use Claude code so who knows with the others.
  • CuriouslyC2 hours ago
    This is a neat idea but there are so many variables here that it's hard to make generalizations.

    Empirically, a top level orchestrator that calls out to a planning committee, then generates a task-dag from the plan which gets orchestrated in parallel where possible is the thing I've seen put in the best results in various heterogeneous environments. As models evolve, crosstalk may become less of a liability.

    • zbyan hour ago
      Reasoning is recursive - you cannot isolate where is should be symbolic and where it should be llm based (fuzzy/neural). This is the idea that started https://github.com/zby/llm-do - there is also RLM: https://alexzhang13.github.io/blog/2025/rlm/ RLM is simpler - but my approach also have some advantages.
      • CuriouslyC7 minutes ago
        I only agree with that statement if you're drawing from the set of all possible problems a priori. For any individual domain I think it's likely you can bound your analytic. This ties into the no free lunch theorem.
  • pevansgreenwood2 minutes ago
    [dead]
  • verdverm2 hours ago
    gonna read this with a grain of salt because I have been rather unimpressed with Google's Ai products, save direct API calls to gemini

    The rest is trash they are forcing down our throats

    • 4b11b42 hours ago
      Yeah alpha go and zero were lame. The earth foundation model - that's just ridiculous.

      That's sarcasm

      ---

      Your "direct Gemini calls" is maybe the least impressive

      edit: This paper is mostly a sort of "quantitative survey". Nothing to get too excited about requiring a grain of salt

      • verdverm2 hours ago
        The underlying models are impressive, be it Gemini (via direct API calls, vs the app or search), I would include alpha-go/fold/etc in that classification

        The products they build, where the agentic stuff is, is what I find unimpressive. The quality is low, the UX is bad, they are forced into every product. Two notable examples, search in GCloud, gemini-cli, antigravity (not theirs technically, $2B whitelabel deal with windsurf iirc)

        So yes, I see it as perfectly acceptable to be more skeptical of Google's take on agentic systems when I find their real world applications lackluster

        • 4b11b4an hour ago
          I agree with you in general re "agentic systems". Though they might deliberately not be trying to compete in the "agent harness" space yet.

          The antigravity experiment yes was via windsurf - probably nobody expected that to take off but maybe was work that made have surfaced some lessons worth learning from.

          • verdverman hour ago
            My hunch is that Google is past it's prime, all the good PMs are gone, and now it looks like a chicken hydra with all the heads off and trying to run in multiple directs.

            There is no clear vision, coherence, or confidence that the products will be around in a another year

            • nawgz42 minutes ago
              Kind of a weird take given they are one of the strongest AI providers who are the most vertically integrated. Sure, maybe the company isn’t as healthy as it once was, but none of them are - late stage capitalism is rotting most foundations