Definition
AI Voice Agent
A software-based agent that conducts real phone conversations using natural language processing, text-to-speech, and contextual data from a CRM or database.
From the episode “The AI That Accepted a Coffee Date: Will Del Principe on Speed, Voice Agents, and the Future of Sales”Hear it on the podcast1 episode
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Related terms in Agents & Automation
AI operating systemA single internal system where a company's workflows, data, and AI tools run together and learn centrally, instead of a scatter of disconnected point tools.AI operatorA person whose job is directing and supervising a fleet of AI agents.AI PassengerA team member who defers to AI output and presents it as their own thinking without exercising independent judgment. Passengers say 'this is what AI thinks' rather than 'this is what I think, informed by AI.' In knowledge work, this is a liability — deferred thinking compounds until it fails visibly. Greg Shove's one-strike policy applies to this behavior.AI Thought PartnerUsing AI not to generate output, but to confirm decisions and surface blind spots before acting on them. A thought partner relationship involves genuine dialogue — asking AI to challenge your assumptions rather than validate them. Greg Shove identifies this as one of the most undervalued and underused applications of AI in business, especially for founders and executives.AI workflowThe end-to-end process of building and running an AI system: preparing data, training, testing, deploying, and maintaining the model.AI workflow engineerA developer who designs AI systems that produce code and other output.Ambient agentsAI coding agents running continuously in the background on development tasks.Atomic TaskThe smallest unit of implementation work an AI coding agent can execute in a single turn without requiring additional context, clarification, or mid-task correction. An atomic task has a defined start state (what exists in the codebase), a defined end state (what should exist after), and clear acceptance criteria (tests pass, linting clean). The goal of requirement decomposition in AI-native development is to produce tasks atomic enough that the agent completes each one before context drift introduces errors. Tasks that require mid-execution judgment calls are not yet atomic — they need further decomposition.