The Factory That Dreams: 39 AI Agents, No Framework - Rushabh Doshi, Machinecraft
A 100-person factory built a multi-agent sales system around organized company memory, specialist roles, and human approval. The useful pattern is retrieval and governance, not custom model training.
A **100-person factory** built a system described in the transcript as **36 specialist agents**, backed by vector, graph, and CRM stores rather than a trained model. It exposes **213 tools** and handles nine go-to-market jobs while keeping outbound actions human-approved.
Treat company history as engineered memory: ingest private records, separate working facts from episodes and relationships, gate what is retained, and make corrections outrank conflicts. Keep agents narrow, cross-check sources, and preserve the rule that the system drafts while a person sends.
A **100-person factory** built a system described in the transcript as **36 specialist agents**, backed by vector, graph, and CRM stores rather than a trained model. It exposes **213 tools** and handles nine go-to-market jobs while keeping outbound actions human-approved. Treat company history as engineered memory: ingest private records, separate working facts from episodes and relationships, gate what is retained, and make corrections outrank conflicts. Keep agents narrow, cross-check sources, and preserve the rule that the system drafts while a person sends. The claimed build cost was **about $30,000**, versus a $230,000 agency quote, with a few thousand dollars in monthly running costs. No measured accuracy or business outcomes are given, and the title’s 39-agent count conflicts with the transcript’s 36.