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- Hacker News
- Checkpointing the live continuation shifts recovery cost toward retained state rather than lifetime event count, which seems especially useful for agents with many completed tool calls.by Trace88
- Was this written by AI? I even read the "technical paper" and still don't understand what it's supposed to do. Nowhere does it explain how this "checkpointing" and "resuming a continuation" actually works in practice.by tsss
- I’ve been building a durable execution framework with roughly the same mechanism. It’s a pretty logical rough edge to try shave off from Temporal-like systems.by iand675
- Snapshotting programs is also what https://github.com/pydantic/monty enables and aims for.
FWIW, I think we'll see a rise of AI-ready interpreters. In some sense, I like that it challenges traditional microservice architectures as an aside.
by alex_hirner - Nice.
Our architecture has had somewhat of a different primitive for years that yields the same outcome.
Our application architecture is named The Feature Architecture.
A unit of work is feature. A lot of common implementation can be derived from (or compressed into) the name of a feature. They are invoked by Features.invoke(feature_name,...) and seamlessly calls same service, service to service or frontend to backend service or even backend to frontend (with client ID and userid) seamlessly.
A command feature by default does durable execution by being a consumer as well as a feature as the machinery ensures all incoming command feature messages are injected into monolog (in house built akin to kafka). So command feature errors are retried by the machinery by default.
All Dip operations are idempotent and hence can be retried maximally.
Our arcc (The architecture compiler) enforces at compile time that 1) there are zero CQRS violations of query to command invocations 2) zero violations of query to Dip.insert/update/remove (extended CQRS for persistence) 3) all Dip mutate operations are idempotent (arcc --strict), 4) zero alien Dip collection access (a feature leaf and its handlers owns exactly one collection) 5) and a whole myriad of around 10 different architecture rules that usually depend on developer discipline and conventions.
One can set a command operation to be not a durable execution by specifying bypass: true for that feature in the registry but those are the outliers.
Checkpointing and replay are not mutually exclusive in our architecture as they emerge when a command feature is either a default or one with bypass: true.
So durable executions are inherently native and first class for all commands in our Feature Architecture.
by elendilm - Durable execution looks a bit like a buzzword in general. If your state is defined in the execution graph then it's just an umbrella term for a group of pre-existing algorithms and patterns. If it's undefined then what are you resuming to? The snapshot just before the crash likely leads to the undefined state again, in which case you're durably automating the crash (or even worse, uncaught incorrect behavior).
- Good model. Anybody interested in actually training models or designing agentic systems should be doing this.
My company started around working on this problem because it's the basis for how you train programming models/reliably deploy LLMs to do specific tasks. It allowed me to build a much better mental model for LLMs because I saw how weirdly fickle/inconsistent/picky they could actually be outside of a "chat" where it feels like they have a coherent persona or consistent knowledge/capability.
Initially I thought of it as a search over prompts for capability at completing specific tasks, but now I think the speed/reliability and operations (eg can I switch models without degrading perforamnce?) benefits are even bigger benefits for most users.
A little "secret" since labs are making it harder to even use their models in this way and it's important that it be more widely understood: distribution-aware replay/re-sampling is a key technique in post-training LLMs. But it's also something that allows you to automatically identify the best model for some subset of your tasks, which can save you a lot of money.
by weitendorf - Fun fact: The first-ever Durable Execution POC at AWS Simple Workflow used snapshots. The workflow was implemented as a fully asynchronous Java application, and a snapshot was a dump of the whole object graph using reflection. Performance was abysmal because a snapshot had to be generated after every state transition.
So we switched to replay. The biggest benefit of replay is that it lets you implement Durable Execution in any language as a library without a complex runtime. It also supports code changes while workflows are in flight (Temporal calls this patching). Making snapshots of arbitrary code state backward-compatible with code changes isn't practical.
I personally think that, in the long term, Durable Execution will use a runtime that supports both snapshotting and determinism. That way, snapshots can be taken infrequently, and replay can bring workflow code to the latest state. Similarly to a database recovering from a WAL. WASM is the most promising technology to achieve this.
by mfateev