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Latent Space · 3 min · Oct 21, 2026
Tenex keeps agent plans and decisions in version control
CJ Hess explains how Tenex standardizes agent workflows and stores plans, decisions and client context in the repo. That gives agents durable context instead of relying on people’s memories.
Special projects lead, Tenex
First hire at Tenex; co-built the MetaHarness and Flowy, a visual planning tool for Claude Code.

Shawn Wang (swyx), Host · Co-founder, AI Engineer
Wrote “The Rise of the AI Engineer” and founded Smol AI, maker of AI News.
About this segment
CJ Hess explains how Tenex standardizes agent workflows and stores plans, decisions and client context in the repo. That gives agents durable context instead of relying on people’s memories.
From “AI in NYC: How Tenex Builds an AI-Native Engineering Team — Alex Lieberman & CJ Hess”, Oct 21, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
“we just always have that in some durable record. And it's never like in someone's head. It's always in the repo.”
CJ Hessat 9:11 · Latent Space9:11Takeaways
- Agents use repo documentation and a CLI to drive work.
- Plans and decisions are checked into Git alongside the code.
- Durable context helps agents answer why a decision was made.
In this episode
Full episode7:16Tenex keeps agent plans and decisions in version control2:35
- 19:23Only 5% of large enterprises run non-engineering agents5:12Up next
- 30:26Rebuilding SaaS depends on value and maintenance burden4:03
- 4 more moments in the full episode
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 7:21
I don't know if we've talked about this before, but like the this idea of like the meta harness, which is like the the engineering workflows at 10x that we use that has allowed us to do, I think, like really meaningful work on long running tasks. Do you want to just talk through like what our process is? Because I feel like people find that interesting. Yeah. So we basically started trying to kind of standardize a set of skills.
- 7:44
I think most engineering orgs have done something similar by this point. And for us, that grew into what is actually like a very structured set of documentation within a repo and then a CLI that's around it that kind of the models are using to actually drive the work that's being done, whether it's creating a plan, a visualization, you know, creating some readout for a client. Effectively, like building all the pieces of the process around actually writing the code to just happen more automatically and standard across our team.
- 8:14
So we have a couple of things like one is this like big canonical plan document that has some similar structure. But I'd say as the models have gotten better, we've actually reduced the enforcements there in the structure. Kind of similarly across all of these. But once you have your plan, you actually build it out. And we have a very opinionated way on how agents will go and log their decisions and log what they're doing and just keep track of what they're up to.
- 8:40
Everything checked into Git. Yeah. All of this is checked in. all of this is really kind of marked down for me now more HTML I like to see these picture books that the agents fit out but all of that gets checked in and then it serves as this surface where not only do you have the code as the source I think there's a debate on do you even need docs the code can actually be the memory of a code base and I do think you do you need to know why you made a decision you need to know the context around the conversation that was had but ultimately that plus the code base if there's a question, if there's something we need to answer, if we need to look back on why we did something, we just always have that in some durable record. And it's never like in someone's head. It's always in the repo. So that just gives like our agents and our tools, these superpowers
- 9:27
to have all the context that I think a lot of engineering teams still just have in conversations or have in their heads. And our goal is to kind of continue to push that forward so that all the context is in one place and more and more of that work is just driven by the agent as opposed to us having to provide everything up front. Yeah, amazing. And one way we even measure like if any