A US financial services company scoped three infrastructure projects for five engineers across two quarters. Our two-person Velocity Pod delivered all three in one quarter: automated funds movement, an underwriting portal, and investor access. I want to explain what made that possible, because “two engineers using AI” leaves out most of the useful information. The platform was already processing live transactions. Existing code, existing integrations, existing customers whose money had to keep moving. An audit found several things preventing the agents from working reliably. Domain rules weren’t documented clearly. Build and test commands were difficult to discover. Different modules followed different conventions. Recurring tasks had no established procedure the agent could follow. So the agents filled in the gaps themselves. That is a pretty expensive place to let software guess. We documented the architecture, domain terminology, coding conventions, and exact verification commands at both repository and module level. We also packaged recurring procedures into reusable skills. Adding an endpoint or writing a migration now had a documented approach. Every task started with a specification. Tests came before implementation. Every change passed automated verification before merge, with a PR review agent checking against the spec and project standards. All three projects used a shared architecture, so each subsequent project started with a foundation already in place. The result: 60% fewer engineers than planned, half the estimated timeline, and about $200 in AI compute per developer per month. The lesson I’d take from this is that your codebase contains less of your company’s knowledge than you think. Your experienced engineers know which conventions matter, where the exceptions live, and why something was built a particular way. An agent needs access to that knowledge too. Making it explicit is part of the engineering work. That’s what our Velocity Framework was built for.