The failure mode I keep seeing isn’t hallucination per se… it’s blurred responsibility between intent and execution. Once a model can both decide and act, you’ve already lost determinism.
The stable pattern seems to be-
LLMs generate proposals, deterministic systems enforce invariants.
Creativity upstream, boring execution downstream.
What matters in production isn’t making models smarter, it’s making failure modes predictable and auditable. If you can’t explain why a state transition was allowed, the architecture is already too permissive.
A pattern I’ve seen bite systems like this isn’t compute or storage first… it’s semantic drift in metric definitions over time.
When you have ~1,200 deterministic metrics sharing primitives, the real cost driver becomes definition coupling, not execution. If metrics are “configurable” but allowed to encode control flow, branching semantics, or hidden normalization rules, you end up with 1,200 soft-coded functions anyway… just harder to reason about.
One approach that’s worked well for me is to explicitly separate:
• Primitive signals (pure, immutable, versioned)
• Metric transforms (strictly functional, no side effects, no cross-metric reads)
• Aggregation/composition layers (where ranking and composite indices live)
The key constraint… metric definitions must be referentially transparent and evaluable in isolation. If a metric can’t be recomputed offline from recorded inputs and its definition hash, it’s already too powerful.
On representation… I’ve had better outcomes with a constrained expression tree (or typed DSL) than raw JSON/YAML. The goal isn’t flexibility… it’s preventing the system from becoming a general purpose programming environment.
For Phase 1, I’d strongly cap scope at:
• A small, fixed primitive vocabulary
• 100–200 metrics max
• Explicit versioning + replay tooling
• Hard limits on metric execution cost
The biggest cost explosions I’ve seen come from
• Allowing metrics to depend on other metrics implicitly
• Letting “configuration” evolve without versioned invariants
• Optimizing performance before semantic boundaries are locked
Curious whether you’re thinking about definition immutability and replayability as first class constraints, or treating them as implementation details later.
One thing that worries me about the “everything becomes an agent” framing is that it collapses coordination cost into abstraction. In real systems once everything is agentized, the dominant failure mode isn’t intelligence but loss of dimensional integrity / unclear authority boundaries, error amplification, and feedback loops that only appear under load.
At scale the hard problem seems less about making more capable agents and more about constraining where agency is allowed to exist so coordination geometry stays stable. Some components benefit from agency… others need to remain invariant anchors.
Curious how people are thinking about architectural limits here… not “can this be an agent?” But “what breaks if it is?”
One thing that stood out to me is that many of the reported gains from coordination seem highly task topology dependent rather than agent count dependent.
In particular, the error amplification vs. containment difference between independent and centralized coordination feels like an architectural property, not an optimization artifact. It suggests that scaling failures may come less from “too many agents” and more from mismatched coordination geometry relative to task dimensionality.
I’m curious whether the authors experimented with adaptive coordination strategies that change topology mid task (e.g., collapsing from decentralized to centralized once saturation or error coupling is detected) or whether the evaluation assumes a fixed coordination structure per run.
A lot of these answers are still treating this as a permissions problem.
The deeper issue is that once an agent is allowed to express intent directly against a live system, you’re already inside the blast radius… no amount of allowlists fully fixes that.
The safer pattern is to separate reasoning from execution entirely: the agent can propose actions, but a deterministic layer is the only thing that can commit state changes.
If the worst case outcome of an agent run isn’t acceptable, the architecture is already too permissive… regardless of how fine grained the controls look.
The failure mode I keep seeing isn’t hallucination per se… it’s blurred responsibility between intent and execution. Once a model can both decide and act, you’ve already lost determinism.
The stable pattern seems to be- LLMs generate proposals, deterministic systems enforce invariants. Creativity upstream, boring execution downstream.
What matters in production isn’t making models smarter, it’s making failure modes predictable and auditable. If you can’t explain why a state transition was allowed, the architecture is already too permissive.