Connor Heggie spent his early career on a fifteen-person self-driving startup run like a research lab, then moved to Scale AI's mapping team, before becoming the co-founder and CTO at Unify. Unify builds agents for go-to-market teams, and for the last two years, one of AI's big mainstream narratives has been automating away the sales rep entirely. Connor and his team built the opposite: an agent that gives every sales rep "an engineer in their back pocket." He walks through how Unify's harness evolved from million-agent batch jobs to a chat product, and unpacks the engineering that makes it cost-effective to run at scale.
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We also discuss:
- How Unify cut 90-95% of costs two weeks before launch
- The 15-requests-per-second ceiling inside OpenAI's prompt cache
- Why your LLM judge must be a different model family
- The Speed Audit: why one at a time beats a table of 1,000
- Why Unify's subagents are just a function call
- What working on self-driving taught Connor about running evals
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Timestamps:
00:00 Introduction
01:30 "Go-to-market is a search problem"
06:10 The old workflow: drag-and-drop nodes over a million-row table
09:40 Why the harness is similar to a coding agent's
10:50 Running durable agents in the cloud without a full VM
13:15 Giving models pandas-like superpowers over a live table
15:50 Why Unify's subagents are just a function call
18:25 The 15-requests-per-second limit hiding inside OpenAI's cache
24:20 Optimizing for prompt caching hit rates
28:20 Fork versus child subagents
32:35 The Speed Audit: why one at a time beats a table of 1,000
37:50 Locking memory to keys instead of letting the agent freestyle
44:15 Why AI isn’t taking over sales
49:00 What working on self-driving taught Connor about running evals
52:46 Why your LLM judge must be a different model family
54:45 Ditching full VMs for Monty, a Python REPL that suspends
58:55 Semantic merge sort: why Connor is obsessed with RLMs
59:53 How Unify cut 90-95% of costs two weeks before launch
1:01:15 The case for "semantic linters" over skill files
1:04:55 Why Unify runs mostly on OpenAI, their first investor
1:06:12 Why 10x cheaper tokens still lose on tool efficiency
1:07:24 Why open-source models don’t make economic sense (yet)
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Referenced:
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Where to find Connor:
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Where to find Harrison:
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Where to find LangChain:
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Send feedback or questions to maxagency@langchain.dev
