Do you want to work on distributed systems? We are building the stack for running agents in production.

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

Apply: https://job-boards.greenhouse.io/orkes/jobs/4998231008

https://job-boards.greenhouse.io/orkes/jobs/4894213008

https://job-boards.greenhouse.io/orkes/jobs/5203462008

All the roles are in in-office in Sunnyvale, CA

Why not just use https://github.com/conductor-oss/python-sdk provide durability, distributed and orchestration.

A good reason: you do not want npm AND docker AND java just for your queue.

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

Runtime for agents https://github.com/agentspan-ai/agentspan Built on top of Conductor OSS - https://github.com/conductor-oss/conductor

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/...

Conductor was moved here: https://github.com/conductor-oss/conductor Maestro uses conductor as its core.

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

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

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