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Agentic guardrails should be deterministic and algorithmic as opposed to using LLM or relying on LLMs.

The moment you rely on LLM to be a guardrail, well you are risking it to fail.


Author here, built Netflix Conductor.

The post is three arguments that together form a new framing for agent runtimes:

The LLM shouldn't execute anything (planning and execution belong on strictly separated planes), signals are the primitive that makes out-of-band cancellation and replanning possible, and the workflow's graph should be synthesized by the LLM at runtime rather than declared by a programmer at commit time. That last one is the load-bearing idea - a Late-Bound Saga.

Agentspan is the runtime that implements it on top of Conductor. Happy to answer about any of these and why I think the `while` loop is the wrong primitive.

Repo: https://github.com/agentspan-ai/agentspan



take a look at https://github.com/conductor-sdk/conductor-python which is easier and will not force you to write with specific framework.


how does it compare to DBOS?


how is it different from Conductor? https://github.com/conductor-oss/conductor


I haven't used Conductor, but quickly looking at the README, Conductor lets you define JSON workflows to orchestrate existing microservices. By contrast, DBOS helps you build highly reliable applications--it runs as a library inside your program and helps you build durable workflows that run alongside your code and are written in the same language.


Maestro is a domain specific implementation for ML and data pipelines that uses Conductor as its core

https://netflixtechblog.com/orchestrating-data-ml-workflows-...

https://github.com/Netflix/maestro/blob/main/maestro-engine/...




This is not true. Looks like they needed some functionalities and just used a small part of conductor.




This has been there for a while and is supported on all three clouds.


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