How Unify cut its AI agent costs 95% in two weeks
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. – 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 – 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) – Referenced: Anthropic ChatGPT Claude Claude Fable 5 Claude Sonnet 5 Context Hub Deep Agents GLM-5.2 GPT-5.4 Helm.ai LangSmith OpenAI QuickJS Recursive Language Models (RLM) Salesforce Scale AI Scaling Managed Agents Unify – Where to find Connor: Twitter/X LinkedIn – Where to find Harrison: Twitter/X LinkedIn – Where to find LangChain: Twitter/X LinkedIn – Send feedback or questions to maxagency@langchain.dev
