Vulkan's presentation API makes this distinction explicit: VK_PRESENT_MODE_MAILBOX_KHR is the "replace if already queued" mode that actually reduces latency, while VK_PRESENT_MODE_FIFO_KHR is the pipeline-queue variant that adds frames ahead of time. OpenGL never standardized the difference,
so "triple buffering" meant whatever the driver implemented -- usually vendor-specific extension behavior that varied between hardware. The naming confusion outlived OpenGL's dominance because the concepts got established before any cross-platform API gave them precise semantics.State machines make failure transitions explicit, but behavior trees can re-enter branches in ways that resemble retry amplification unless guarded carefully.
Curious whether anyone has modeled agent execution control as a state machine specifically to make containment explicit.
We ran into a retry amplification issue in one of our LLM agents recently.The provider returned 429s for a short period. We had per-call retry limits in place. We did NOT have containment at the request-chain level.Because calls were nested and spread across multiple workers, retries multiplied in ways we didn’t anticipate.Per-call limits were not enough.For those running LLM systems in...
A lot of people talk about “async performance” without realizing the core efficiency gain came from avoiding O(n) scans on idle FDs.
Curious how many higher-level runtimes still leak edge-triggered vs level-triggered semantics in subtle ways.
They just assume LLM calls behave like deterministic RPC.
The real issue is that we’re embedding stochastic, cost-weighted calls inside recursive graphs without structural bounds.
Orchestration isn’t the failure. Lack of containment is.
What you’ve described sounds a lot like layered containment:
Loop budget (hard recursion bound)
Progressive checks (soft convergence control)
Sleep cycles (temporal isolation)
Deep sleep cap (bounded self-modification)
Git rollback (failure domain isolation)
Out of curiosity, have you measured amplification?
For example: total LLM calls per wake cycle, or per deep sleep?
I’m starting to think agent systems need amplification metrics the same way distributed systems track retry amplification.
It looks less like a “model failure” and more like a containment failure.
When agents audit themselves, you’re effectively running recursive evaluation without structural bounds.
Did you enforce any step limits, retry budgets, or timeout propagation?
Without those, self-evaluation loops can amplify errors pretty quickly.
The interesting gap is that retrieval systems used in LLM pipelines often don't inherit those signals in a structured way. They fetch documents, but the model sees text, not provenance metadata or confidence scores.
So even if the ranking system “knows” a source is weak, that signal doesn’t necessarily survive into generation.
Maybe the harder problem isn’t retrieval, but how to propagate source trust signals all the way into the claim itself.
Most RAG pipelines retrieve and concatenate, but they don’t ask “how trustworthy is this source?” or “do multiple independent sources corroborate this claim?”
Without some notion of source reliability or cross-verification, confident synthesis of fiction is almost guaranteed.
Has anyone seen a production system that actually does claim-level verification before generation?
One scenario I'm curious about: how do you think about overnight or weekend runs when no one is around to approve? Human gates work well during business hours, but agents don't necessarily respect that clock.
Do you see Axon evolving toward hybrid controls — human approval for sensitive actions, plus automatic limits for volume or repetition?
The harder problem seems to be concurrent semantic edits. Git-style merging works for code because conflicts are syntactic. With prose, two agents can produce logically conflicting conclusions without triggering a merge conflict.
How does Sayou reason about semantic divergence when Agent A updates a research note while Agent B is drafting against an older snapshot?
In distributed systems we rarely let loops run unbounded — we add budgets, backoff, circuit breakers, etc. With agents, it’s interesting that most frameworks focus on observability (traces, logs) but not on hard containment of the loop itself.
If you were extending the mapping, would “agent budget limits” fit better as a rate limiter, a circuit breaker, or something closer to a bounded work queue?
It enforces: - hard budget stops - retry containment - circuit breaking
The goal is simple: the $12K weekend never happens.
LLM agents are good at deciding. They are bad at knowing when to stop.VERONICA is a failsafe state machine that sits between strategy engines and external systems. It provides: - Per-entity circuit breakers (task A failure doesn't block task B) - SAFE_MODE manual halt that persists across crashes - Atomic state persistence (survives SIGKILL) - Signal-aware graceful shutdown...