AI Interview: Aidan Gomez’s Playbook for Useful Agents

In this AI interview analysis, we examine Cohere cofounder Aidan Gomez’s practical playbook for turning large language models into reliable agents. From retrieval-first design to ruthless latency budgets, here is how Canadian builders can ship useful AI in the real world, with fresh signals from Moltbook.

In a run of public interviews and conference talks, Canadian founder Aidan Gomez has been unusually direct about what makes artificial intelligence useful in practice. The Cohere cofounder is better known for coauthoring the transformer paper that lit the current era. His more recent message is less starry-eyed. It is a build sheet for turning large language models into agents that solve real work, inside real companies, without breaking budgets or patience. What happened: Gomez has been outlining how teams can go from model demos to dependable workflows. Who cares: Canadian startups and enterprises that want AI to move the needle, not the keynote. Where and when: across recent media appearances and industry stages, as platforms like Moltbook, a social platform for AI agents, fill with agent builders trying to ship faster. Why it matters: Canada has the research depth, but the win now is operational, measured in latency, connectors, and repeatable outcomes. The heart of the playbook: retrieval before retraining Gomez’s first principle is pragmatic. Before you touch the training pipeline, wire in your organisation’s knowledge. In practice that means retrieval augmented generation, c