There came a point when Newton Asare realized AI agents weren\u2019t just tools anymore. \u201cThey were operating more like teammates,\u201d he told TechCrunch. <\/p>\n\n
The realization crystallized when Asare and Kiran Das, both serial founders, noticed they were using AI agents to perform tasks they usually would have done themselves. Asare said he came to believe that the future lay in people managing AI employees.<\/p>\n\n
\u201cAnd if that\u2019s true, we\u2019ll need a real system to manage them, with structure around onboarding, coordination, and oversight for digital workers,\u201d he added. <\/p>\n\n
Last year, the duo launched Reload<\/a>, an AI workforce management platform. On Thursday, the company announced its first AI product, Epic<\/a>, alongside a $2.275 million round led by Anthemis, with participation from Zeal Capital Partners, Plug and Play, Cohen Circle, Blueprint, and Axiom.<\/p>\n\n Reload is a platform that lets organizations manage their AI agents across teams and departments. Companies can connect agents, regardless of who built them (whether by a third party or internally), assign them roles and permissions, and track the work they perform. \u201cReload acts like the system of record for AI employees, providing visibility, coordination, and oversight as agents operate across functions,\u201d said Asare, the company’s CEO.<\/p>\n\n Right now, he observed, teams are using multiple agents simultaneously for tasks such as coding, debugging, and refactoring. The problem is that these agents are often focused solely on whatever they were prompted to do and don\u2019t necessarily retain long-term memory of what a product is or why they were told to perform a specific function. They operate, in other words, with only short-term memory.<\/p>\n\n Over time, an agent can lose context, or the system can evolve away from its original intent. That\u2019s why Reload is launching Epic. Built on top of the Reload platform, it serves as an architect alongside other coding agents, continuously defining a product\u2019s requirements and constraints, and reminding agents what they are building and why, to keep a system consistent as it develops. <\/p>\n