An AI system that works alongside a human user, augmenting their capabilities by suggesting actions, drafting content, surfacing relevant information, and handling routine sub-tasks—but always with the human making final decisions. Copilots sit between chatbots (reactive, conversational) and agents (autonomous, action-taking). The term was popularized by GitHub Copilot for code completion and Microsoft Copilot for Office productivity, and now describes a broad category of AI assistants that enhance rather than replace human work.
Example
A sales copilot monitors a rep's email and CRM activity. Before a call, it surfaces the prospect's recent activity, suggests talking points, and drafts a follow-up email template. The rep reviews, edits, and sends—the copilot accelerates the workflow without taking autonomous action.
Frequently asked questions
What's the difference between a copilot and an agent?
A copilot assists and suggests; the human decides and acts. An agent decides and acts autonomously; the human supervises. Copilots are human-in-the-loop by design—every action requires human approval. Agents operate independently within defined guardrails. Many products evolve from copilot to agent as users build trust: start by suggesting emails, graduate to sending them automatically.
When should I use a copilot vs. a fully autonomous agent?
Use a copilot when: mistakes are costly and hard to reverse (legal drafting, financial decisions), human judgment adds significant value (creative work, relationship management), or users need to build trust before automating (new AI deployments). Use a fully autonomous agent when: the task is well-defined with clear success criteria, the cost of errors is low or easily reversible, and volume makes human review impractical.