← All IntelClip / AI AgentsScale with tokens: replace every stage of the FDE lifecycle
From Forward Deployed Engineering at Ramp: Scoping and Agents · ≈6:20
Frames agent adoption as decomposing an existing human workflow — context gathering, scoping, spec writing, implementation — into stages that can each be automated, rather than as one intimidating end-to-end problem.
What’s in it
- Frames agent adoption as decomposing an existing human workflow — context gathering, scoping, spec writing, implementation — into stages that can each be automated, rather than as one intimidating end-to-end problem.
Clip transcript
them and and thus kind of emphasizes the importance of scoping up front. Now, Okay, so let's say that you and your team have become masters of scoping. You know, you're you're amazing. In today's world, this is not enough. So, unless you are scaling with model capabilities, you are going to fall behind. Now, I'm not going to belabor this point too much. I think like every talk in this uh in in this conference is probably some flavor of this, but like the point is that we basically have to reinvent our jobs constantly now. So, whatever work we are doing today, you know, for the most part it's knowledge work, we have to figure out how to have models and agents do it for us. And so, that brings me to the second half of this talk and the the other point that I want to convey today, which is all of us have to figure out how to scale with tokens. And the way that I interpret scaling with tokens for FTE is take a look at the whole life cycle of what an FTE does. From gathering context to scoping out a request to writing out a spec and then implementing the feature, each stage of that pipeline can be replaced with agents. And at first it seems kind of daunting. You're like, like how are you going to go and approach and like solve that? But if you break the problem down and then make progress on it, it's it's actually pretty tractable. And so, I'll share share with you guys one example of something Oops. Something that we um that we've done at Ramp. So,
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