Lua

AI agents are becoming an operational reality for organizations, but their deployment at scale raises problems that existing tools do not adequately address: how do you coordinate dozens of agents working in parallel? How do you ensure their actions are aligned with company policies? How do you measure an agent's impact and improve it over time? Who in the organization is responsible for overseeing agent activity? Lua, a London platform, was built to answer these questions. Lua is an operational platform for human-agent collaboration — not an agent builder, but the infrastructure that enables organizations to orchestrate, monitor, and improve agents already in production. The system provides tools for coordinating work between multiple agents and human teams, for tracking agent actions and maintaining organizational accountability, and for identifying where agents perform well and where they require intervention. A distinctive element is the network of certified implementers: Lua aims not only to sell the platform directly to organizations, but to build an ecosystem of specialized partners who help companies implement and scale their AI agent deployments. This indirect go-to-market approach is effective for complex enterprise solutions where implementation consulting is an essential part of the value. The target is organizations that have already begun adopting AI agents and are seeking to scale these deployments in a controlled and measurable way. The business model is presumably enterprise SaaS with licensing based on the number of managed agents or the volume of orchestrated interactions. Lua positions itself at a precise moment in the maturation of the enterprise AI market: the phase in which agent adoption moves beyond experimentation and becomes operational infrastructure, with all the governance, accountability, and optimization requirements that this entails.