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AI tools for Forward Deployed Engineering — Vasuman Moza, Varick Agents

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AI Engineer· published 2026-07-28· 0:20:22· en-US· indexed 2026-08-10 19:38

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What was stored

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Provenance

Each pipeline stage, its state and the model that produced it
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Transcript

216 cues· 3,704 words· 20,454 chars

  1. 0:12 All right, first and foremost, thanks so much for being here.
  2. 0:15 It's been a great experience, obviously, chatting amongst other industry giants like Cursor and Factory and Anthropic.
  3. 0:22 And I'm sure you guys are mostly here for them.
  4. 0:24 But thanks for sticking around for this talk.
  5. 0:26 My name is Voss.
  6. 0:27 I'm the CEO of Veric Agents.
  7. 0:28 We work with some of the largest companies on the planet, transforming them from the inside out with AI and agents.
  8. 0:34 And because of the nature of our work, which is highly bespoke, we go very deep into our clients.
  9. 0:40 It requires a lot of forward-deployed engineering.
  10. 0:42 And this conversation is around why that's so important, how we approach it at Varick, and some of the internal tooling that we've created internally to allow us to scale that forward deployed motion without increasing headcount exponentially.
  11. 0:55 And it's titled The Next Bottleneck because I fundamentally believe the next bottleneck is how deep can you go into a customer without scaling headcount exponentially?
  12. 1:04 How can AI do that job for you?
  13. 1:07 So as stated previously, AI is solving the execution of work.
  14. 1:12 If you were to look back a couple of years ago before thinking agents and reasoning agents were a widespread phenomenon, and I had asked you how many of you have used AI to solve an end-to-end task, the answer would be slim to none.
  15. 1:26 But if I ask that same question to everyone today saying, has AI solved an end-to-end task for you today, I'm sure every single one of you would raise your hands.
  16. 1:33 So clearly, execution is no longer the core bottleneck.
  17. 1:36 The models are improving to the point where intelligence is no longer the constraint.
  18. 1:40 And harnesses are being built in a way that allow us to use, whether it's browser use tooling or API tooling with very robust MCPs that allow us to execute work with near perfection.
  19. 1:54 And the bottleneck that is still here is, how much can you understand the business?
  20. 1:59 Because every business, every consumer is different.
  21. 2:03 One sales department for a health care company, for example, operates completely differently than the sales department for a SAS company.
  22. 2:09 And this is something that we see with our work today at Beric Agents.
  23. 2:11 And if there are any business operators in the room, you know exactly how hard it is to wrangle the latest models to solve for your specific use cases.
  24. 2:20 It's very difficult to extract that context from your employees and from your team.
  25. 2:25 And it's very difficult to feed that into an API call or a simple model call that doesn't break down very quickly.
  26. 2:32 So the bottleneck is, how much can you process re-engineer?
  27. 2:37 How much can you process understand?
  28. 2:38 And that's the job that we do here at Barrick.
  29. 2:40 So right now, operations are fundamentally centered around the human.
  30. 2:45 Right now, the work that you do today is done by humans, whether it's on top of software or completely agnostic to software.
  31. 2:51 But in the future, operations will be centered around AI.
  32. 2:53 And this means not only providing companies with AI tooling, like a cursor, a cloud code, codex, like a factory, like any of the other brilliant AI tools that I'm sure you're experiencing here today, but also changing the operations and the processes themselves.
  33. 3:10 And fundamentally, that is the role of a forward deployed engineer.
  34. 3:13 It's going into the company, understanding how things run today, and re-envisioning what it could look like tomorrow.
  35. 3:20 And we believe that is our job at Veric and why forward deployed engineering is such a core part of what we do.
  36. 3:27 So a forward-deployed agent, what does that really mean?
  37. 3:31 So why do we need FDEs?
  38. 3:32 As stated previously, I'm not going to go into this too much.
  39. 3:34 I'm sure you've been hearing a lot of this today.
  40. 3:36 FDEs are responsible for a few different things.
  41. 3:38 One is they map the way that humans are doing their work today.
  42. 3:41 So how we do that at Veric is several forward-deployed engineers will be embedded directly with a customer.
  43. 3:45 You can imagine it's an enterprise company with thousands of employees, but we'll scope it down to a single department.
  44. 3:51 In a finance department, for example, we'll have them sit down with the process leads for AP, AR, card reconciliation, banking, billing, FP&A, et cetera.
  45. 4:01 So interviewing every single one of these process leads to understand not only how are things running today, but more importantly, when things go wrong, what happens.
  46. 4:10 You know, a lot of the documentation that you have in companies about the golden path and maybe an edge case or two, but this is still fundamentally not the reality.
  47. 4:18 where when we talk to customers, it's a lot of Sarah in AP handles the workflow today in this way, but when things go wrong, she actually sends it over to Chris, who then takes four days of cycle time to handle reconciliations between a purchase order and an invoice.
  48. 4:34 Those are the realities that are, one, unique to every single company.
  49. 4:37 The way that they handle things is different from one company to the next.
  50. 4:40 And two, the real bottleneck for why AI can't just run amok and handle end-to-end processes without the hand-holding that you see today in the enterprise.

Chapters

  1. 0:00 Introduction: forward deployed agents
  2. 1:55 The question: what can an agent actually do?
  3. 3:27 What a forward deployed agent is
  4. 4:44 Automating a department end to end
  5. 6:14 Why enterprises need deployed engineers, no migrations
  6. 8:30 Hiring the top 1 percent
  7. 10:51 The platform and department wide ROI
  8. 11:53 Demo: tools for forward deployed engineers
  9. 13:07 Turning notes into a workflow spec
  10. 14:22 Engineering workflows with a source of truth
  11. 16:53 Post training models to extract context
  12. 18:37 Where this leaves us

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