About this session
Building a custom agents platform is one of the key pieces to unlocking AI for complex business workflows. Monte Carlo is the sales-specific system we've built to help scale Monaco's revenue: a platform where revenue teams encode their own playbooks as agents that run against everything we've ingested about their business, including pipeline, outbound, calls, and CRM. Sales is a particularly complex vertical for the following reasons: no two revenue orgs look alike, the data is large and heterogeneous, and the expertise that matters lives in per-rep workflows nobody has written down. Making it work at scale meant rebuilding the agent runtime on per-run Vercel Sandbox microVMs, a decision that propagated much further than we expected into how we handle context, how much of a playbook ends up as prose versus code, and what an agent is permitted to do at all. In this talk, I'll break down the components a verticalized agent platform needs in order to be fast and correct, including an execution substrate, memory and context, how expertise gets represented, and the evaluation loop, along with what is still unresolved.
What you’ll take away
- Identify the components of a domain-specific agent platform.
- Compare prose and code as representations of business expertise.
- Connect execution, memory, permissions, and evaluation in one architecture.
Speaker
Mihail Eric
AI Engineering · Monaco
Mihail Eric works in AI Engineering at Monaco. His Ship talk unpacks Monte Carlo, a sales-specific agent platform, and the difficult parts of encoding business expertise: heterogeneous data, memory, execution environments, permissions, and an evaluation loop that reflects real work.