AI worker
A task-focused AI system built to do a real business job, such as answering calls, collecting lead details, booking requests, or sending handoff notes. It is measured by business outcomes, not by how clever the chat feels.
Plain-English AI glossary
AI phone agent terms business owners need.
A practical glossary for buying or building AI phone agents, AI receptionists, missed-call systems, RAG knowledge assistants, and workflow automations without getting buried in vendor jargon.
AI phone agent
A voice-based AI worker that answers or places calls, asks approved questions, captures details, and routes the conversation according to business rules.
AI receptionist
An AI front-desk layer for common questions, intake, appointment requests, after-hours coverage, and overflow calls. It should support staff, not pretend to replace licensed or sensitive human judgment.
Voice agent
An AI system that talks and listens over the phone or a web voice widget. The quality depends on voice model, latency, scripts, routing logic, and escalation rules.
IVR
Interactive Voice Response — the phone menu system callers use to choose options, answer prompts, or reach the right department without a live receptionist first.
ACD
Automatic Call Distribution — the routing system that sends incoming calls to the right person, team, queue, or AI workflow based on rules like availability, priority, or call type.
Call tree
A structured map of where a caller can go next: sales, support, emergency, billing, scheduling, voicemail, or human handoff. AI can make the call tree feel more natural, but the routing logic still matters.
Call queue
A waiting line for calls when the team is busy. An AI worker can reduce queue pressure by answering common questions, collecting details, or offering a callback.
Overflow routing
A backup path for calls that staff cannot answer in time. Overflow can go to an AI receptionist, another team member, voicemail, text follow-up, or an after-hours workflow.
After-hours coverage
A phone or messaging workflow that handles inquiries when the business is closed, then sends a summary or priority alert for the next business day.
Callback request
A structured way to capture who called, why they called, when they want a response, and how urgent the issue is so the team can follow up cleanly.
Call disposition
The final label or outcome of a call, such as new lead, booked appointment, urgent issue, spam, existing customer, vendor, or needs human follow-up.
Intent detection
The system’s best read on what the caller is trying to accomplish. For example: book an appointment, request pricing, report a problem, ask a question, or reach a specific person.
Escalation rule
A business rule that tells the AI when to stop handling the conversation and notify a human, such as emergencies, angry callers, high-value leads, or sensitive requests.
Missed-call capture
A workflow that responds when a business cannot answer live, collects what the caller needed, and alerts the owner or team before the opportunity goes cold.
Human handoff
The moment an AI stops and sends the right information to a real person. A good handoff includes the caller’s need, urgency, contact details, transcript or summary, and recommended next step.
Lead qualification
The process of separating serious opportunities from poor-fit requests by asking useful questions about need, timing, budget, location, urgency, and decision-maker status.
RAG
Retrieval-Augmented Generation — a way to let an AI answer from approved documents, FAQs, policies, product files, or knowledge bases instead of relying only on the model’s general training.
Vector database
A search system designed to find meaning, not just exact words. Tools like Pinecone and Weaviate can help an AI retrieve the most relevant chunks of business knowledge.
Embedding
A numerical representation of text that helps software compare meaning. Embeddings are what let a system understand that “missed call,” “voicemail,” and “after-hours inquiry” may be related.
MCP
Model Context Protocol. A standard way for AI systems to connect with tools, data, and actions. In plain English, it helps an AI use approved capabilities instead of being trapped in a chat box.
Agent-native architecture
An AI system designed around state, tools, memory, routing, evaluation, and handoff from the beginning. It is more durable than a single prompt glued onto a workflow.
Multi-agent workflow
A setup where specialized AI workers handle different parts of a process. For example: one agent answers the call, another qualifies the lead, another prepares the owner summary.
State graph
A controlled map of where an AI workflow can go next. It helps keep complex agent behavior organized so the system does not wander into unsupported actions.
Evals
Tests that check whether an AI system is producing acceptable answers. Evals are useful for catching regressions, hallucinations, bad routing, and policy violations before customers feel the problem.
Hallucination
When an AI produces information that sounds confident but is not grounded in approved facts. Good systems reduce this with RAG, rules, handoffs, and testing.
Guardrails
Rules and checks that limit what the AI can say or do. For business use, guardrails protect against unsafe advice, bad promises, privacy mistakes, and unsupported actions.
Call routing
The logic that decides what happens after a call is answered: text the owner, book a callback, escalate urgent issues, archive spam, or add the lead to a CRM.
Post-call summary
A concise record of what happened during the call: caller identity, reason, urgency, key details, next step, and any transcript or recording link.
CRM integration
A connection between the AI workflow and a customer system such as HubSpot, Salesforce, Airtable, or a Google Sheet so leads do not stay trapped in call notes.
Workflow automation
The behind-the-scenes steps that happen after the AI collects information: alerts, forms, CRM updates, follow-ups, reminders, and reports.
Latency
The delay between a person speaking and the AI responding. Low latency matters in phone agents because long pauses make the conversation feel unnatural.
Fallback path
The backup action when the AI cannot safely answer or complete a task. Examples: take a message, route to a human, schedule a callback, or send a clear “I’m not sure” response.
Pilot
A small first deployment designed to prove value quickly. A good pilot has one clear workflow, a measurable outcome, and a human review loop before expansion.
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