The best AI agents need more humans than you think

Eno Reyes is the co-founder and CTO at Factory, a $1.5 billion company turning signal into deployed code inside some of the world's biggest engineering orgs. Before founding Factory in 2023, he was an engineer at Microsoft and then Hugging Face. In this conversation, Eno unpacks why the harness matters more than the model running underneath it, how to build your own 24/7 software factory, and why he's "bullish on humans in the loop for a very long time.” We also discuss: • Why product management isn't going away • The engineer who lint-checks Factory's own agents • Why coding agents might become the best general agents • The platonic representation hypothesis • Why Factory might hide when "memory" is happening • Building Factory's universal meta harness • Tokenomics, and how routing cuts the bill Timestamps: 00:00 Introduction 01:19 What a 24/7 autonomous software factory actually means 03:16 Why product management isn't going away 06:56 Alvin, the engineer who lint-checks Factory's own agents 10:16 "It makes me very bullish on humans in the loop for a very long time" 11:37 The Disney Epcot analogy for rolling out AI 16:47 Why coding agents might become the best general agents 20:54 The case against a model-independent harness, and Eno's counter 25:44 The platonic representation hypothesis explained 32:42 Why model quirks are like being left or right-handed 39:28 Why you could technically decompile Factory's entire harness 44:20 Why memory might be AI's most overused word 46:47 Inside AutoWiki and its Lore feature 55:32 Agent readiness: the deterministic feedback agents need 57:12 Missions: Factory's universal meta harness 1:01:12 "It's kind of turtles all the way down": validating the validators 1:04:37 Tokenomics: what missions cost, and how routing cuts the bill 1:11:08 Why Eno is bullish on open models 1:14:30 BenchBench, and why code review benchmarks might be broken References: • Aider: https://aider.chat/ • Alvin Sng: https://www.linkedin.com/in/alvinsng/ • Amp: https://ampcode.com/ • Andrej Karpathy: https://x.com/karpathy • Anthropic: https://www.anthropic.com/ • Anthropic–Emotion concepts and their function in a large language model: https://www.anthropic.com/research/emotion-concepts-function • Cursor: https://cursor.com/ • Deep Agents: https://docs.langchain.com/oss/python/deepagents/overview • DeepWiki: https://deepwiki.com/ • Epcot: https://en.wikipedia.org/wiki/Epcot • Factory: https://factory.ai/ • GLM: https://chat.z.ai/ • Harbor: https://www.harborframework.com/ • Hugging Face: https://huggingface.co/ • HuggingGPT: https://arxiv.org/abs/2303.17580 • Kimi: https://www.kimi.com/ • LangGraph: https://www.langchain.com/langgraph • LangSmith: https://smith.langchain.com/ • MiniMax: https://www.minimax.io/ • Open Knowledge Format (OKF): https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing/ • OpenAI: https://openai.com/ • OpenRouter Model Fusion: https://openrouter.ai/fusion • Ramp: https://ramp.com/ • Sakana Fugu: https://sakana.ai/fugu/ • SWE-bench: https://www.swebench.com/ • Terminal-Bench: https://www.tbench.ai/ Where to find Eno: • LinkedIn: https://www.linkedin.com/in/enoreyes • Twitter/X: https://x.com/EnoReyes Where to find Harrison: • LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/ • Twitter/X: https://x.com/hwchase17 Where to find LangChain: • Website: https://www.langchain.com/ • Docs: https://docs.langchain.com/ Send feedback or questions to [email protected]