GLOSSARY
AI Worker (digital worker)
An AI worker is an autonomous AI agent that owns a complete, continuous scope of work, like a digital colleague: it sources, qualifies, follows up, schedules and reports, with human approval on sensitive actions.
IN DEPTH
An AI worker (also called digital worker or digital employee) is an artificial intelligence agent configured to own an entire business scope, continuously and with a high degree of autonomy. Unlike a chatbot (which answers questions) or a copilot (which assists a task on demand), the AI worker receives goals ("qualify every inbound application", "no candidate left without a reply for more than 5 days") and chains the necessary actions on its own, 24/7. In recruitment, an AI worker typically owns inbound qualification, personalized outreach, multi-channel follow-ups, interview scheduling and database hygiene. Measured gains at equipped teams: around 60% less sourcing time and 30% more placements at constant headcount. Best practice remains human-in-the-loop: the AI worker executes, the human approves outbound messages and keeps the selection decision, as the EU AI Act requires. Balt, Cobalt's native agent, is an example of an AI worker embedded in the platform.
Frequently asked questions
The AI agent is the technology building block (an AI able to chain actions); the AI worker is its business embodiment: an agent configured to own a precise scope continuously, with goals, rules and management, like a team member.
No: it absorbs execution tasks (screening, follow-ups, scheduling, reporting), about 40% of a recruiter's time. Judgment, relationships and negotiation stay human, and the selection decision legally must (EU AI Act).
Related terms
- Agentic AIAgentic AI refers to AI systems able to pursue a goal autonomously: planning steps, using tools (ATS, email, calendar), executing actions and self-correcting. Generative AI answers, agentic AI acts.
- MCP (Model Context Protocol)MCP is an open standard created by Anthropic that lets AI assistants (Claude, ChatGPT) connect directly to business tools like an ATS or CRM, to read data and execute actions in natural language.
- AI Matching (candidate-mission)AI matching uses machine learning models to evaluate a candidate's relevance to a mission by crossing skills, experience, availability and cultural fit. It replaces Boolean search.
- Semantic SourcingSemantic sourcing uses AI to understand natural language queries (e.g., "DevOps AWS 5 years Paris available March") and retrieve relevant candidates without Boolean search.
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