AI Agent
A system that reasons about a situation, decides what action to take from a defined set of options, executes that action across one or more systems, and hands off to a person when a case falls outside what it should handle alone.
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A conversational AI tool built on an organization's own data, tone and internal processes — distinct from a generic chat interface because it's scoped, access-controlled and built for repeatable business use.
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A defined way for two systems to exchange data or trigger actions in each other, without either system needing to know the internal details of the other.
More on Custom API Development for AI ToolsBusiness Process Automation (BPA)
Automating the repeatable steps of an end-to-end business process — intake, routing, approvals, completion — while leaving the steps that genuinely require human judgment in place.
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A conversational interface that answers questions, typically by retrieving information from documentation or support history. Chatbots are informational by design — they don't usually take action in another system on their own.
More on AI Chatbot DevelopmentCRM Automation
Automated deduplication, enrichment, routing and follow-up sequencing built directly into the CRM a sales or success team already uses, rather than a separate parallel tool.
More on CRM AutomationEscalation Path
The defined route a case takes when an automated system decides it can't or shouldn't finish the job alone — who gets notified, with what context, and by when.
Fine-Tuning
Further training an existing AI model on an organization's own historical examples so its outputs match that organization's patterns more closely. Usually a second step after prompt engineering, not a first one.
More on Prompt Engineering & Custom GPT Fine-TuningGuardrails
The explicit boundaries placed around what an AI system is allowed to do — which actions it can take unsupervised, which require approval, and what it should never do regardless of what it concludes.
Hallucination
When an AI model generates output that sounds plausible but isn't factually grounded in its actual inputs or knowledge. The main reasons systems are built with retrieval, citations and human review points rather than trusted blindly.
Handoff
The moment an automated system passes work back to a person. A well-designed handoff arrives with full context already attached, rather than a bare notification the person has to go investigate.
Human-in-the-Loop
An automation design where a person reviews, approves or can override an AI system's output before it takes effect — standard practice for anything with real consequences, especially early in a system's life.
Integration
Connecting two or more systems so they share data or trigger actions in each other automatically, removing the manual re-entry that otherwise happens between tools that don't natively talk to each other.
More on AI Integration into Existing SystemsiPaaS (Integration Platform as a Service)
Cloud platforms — Zapier, Make and n8n are the most common — that let teams connect apps and automate workflows between them without writing custom integration code for each connection.
More on Workflow Automation (Zapier, Make, n8n)Compare Zapier, Make and n8nLarge Language Model (LLM)
A machine learning model trained on large volumes of text, capable of generating, summarizing and reasoning about language. The underlying technology behind most modern chatbots, assistants and AI agents.
Machine Learning
A branch of AI where systems improve at a task by learning patterns from data, rather than following only explicitly programmed rules — the broader field that language models and many automation techniques sit within.
Natural Language Processing (NLP)
The branch of AI concerned with understanding and generating human language — the technology that lets a system parse an inbound email, ticket or message and act on what it actually means.
Orchestration
Coordinating multiple systems, tools or automated steps so they run in the right order, handle failures gracefully, and produce one coherent outcome instead of several disconnected ones.
Prompt Engineering
The practice of designing the instructions given to an AI model so its outputs are accurate, consistent and on-brand — the first lever teams pull before considering fine-tuning.
More on Prompt Engineering & Custom GPT Fine-TuningRetrieval-Augmented Generation (RAG)
An architecture where an AI system looks up relevant information from a specific knowledge base before generating a response, so its answers are grounded in real source material rather than the model's general training alone.
Robotic Process Automation (RPA)
Rule-based bots that operate an existing software interface exactly as a person would — click, copy, paste, repeat. Deterministic and reliable for stable, high-volume tasks, but not built to handle ambiguity the way an AI agent is.
More on Robotic Process Automation (RPA)Supervised Autonomy
A staged approach to automation where a system starts with a human reviewing its actions and earns more independence over time, based on real production evidence rather than a one-time launch decision.
Tool Calling (Function Calling)
The mechanism that lets an AI model actually do something — look up a record, send a message, update a system — rather than only generate text, by calling a predefined function with structured inputs.
Voice Agent
An AI system that handles phone calls — bookings, triage, FAQs — using natural-sounding speech synthesis, with a defined transfer point to a human for anything outside its scope.
More on AI Voice AgentsWebhook
An automated message one system sends to another the moment a specific event happens — a new order, a form submission — so the receiving system can react immediately instead of on a polling schedule.
Workflow Automation
Automating the sequence of steps a piece of work moves through — across one tool or several — so it progresses without a person manually triggering each step.
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