A fully autonomous customer service agent that manages all customer interactions from start to finish using natural language, business context, and product knowledge. Can seek information, make decisions, and take action to resolve complex queries without human intervention.

The process of seamlessly transferring a customer conversation from an AI Agent to a human agent when the AI cannot resolve the issue or when human expertise adds value. In well-designed systems, handoffs are invisible to customers, with full context and conversation history preserved.

Multi-step workflows that enable AI Agents to handle complex queries requiring business logic, third-party system integration, or cross-team approvals. Uses natural language combined with deterministic controls to automate sophisticated customer service tasks like refunds or cancellations.

Conversational interface powered by artificial intelligence that uses natural language processing and machine learning to understand customer queries and provide intelligent responses, going beyond simple rule-based scripts.

AI assistant that works alongside human agents to enhance productivity and decision-making. Provides real-time suggestions, automates repetitive tasks, and surfaces relevant information during customer interactions without replacing human judgment.

The application of artificial intelligence technologies—including AI Agents, machine learning, and natural language processing—to automate, enhance, and optimize customer support operations and experiences.

Framework of policies, processes, and controls that ensure responsible AI deployment. Covers ethical guidelines, risk management, compliance monitoring, and accountability structures for AI systems in customer-facing operations.

Five progressive stages organizations move through when adopting AI for customer service: Level 0 (Traditionalist), Level 1 (Explorer), Level 2 (Integrator), Level 3 (Architect), and Level 4 (Pioneer). Each level represents increasing AI integration and business transformation.

A critical role responsible for owning day-to-day AI Agent performance, tracking quality, tuning behavior, prioritizing fixes, and driving continuous iteration. This person ensures the AI Agent constantly improves and maintains clear accountability for AI system performance.

A continuous improvement cycle consisting of four phases: Train (strengthen knowledge), Test (validate changes), Deploy (roll out updates), and Analyze (measure performance). Each cycle compounds the next, driving systematic AI Agent enhancement over time.

A strategic approach where every customer interaction begins with an AI Agent, with seamless human collaboration when needed. Represents a shift from reactive, human-dependent support to proactive, AI-led service that scales independently of headcount while maintaining quality.

A company that structures its support operations around AI as the primary responder, with human agents focused on complex, high-value work that genuinely requires human judgment and expertise.

The world's first independent certification standard for AI customer service agents, developed by Intercom to establish trust, safety, and reliability benchmarks for autonomous AI systems handling customer interactions.

AI-powered technology that provides real-time suggestions, information, and guidance to human support agents during customer interactions. Enhances agent performance and reduces resolution time.

The coordination and management of multiple AI agents or systems working together to complete complex tasks. Orchestration ensures agents collaborate effectively, avoid conflicts, and deliver cohesive outcomes.

AI systems capable of autonomous decision-making and action-taking to achieve complex goals. Unlike reactive AI that responds to inputs, agentic AI can plan multi-step workflows, make contextual decisions, and adapt behavior based on outcomes.

A multi-step process where AI agents autonomously plan, execute, and iterate on tasks to achieve complex goals. Unlike single-turn responses, agentic workflows enable AI to break down problems and work through solutions systematically.

Ensuring AI systems behave according to human intentions, values, and goals. In customer service, alignment means AI responses match company policies, brand voice, and customer expectations while avoiding harmful or inappropriate behaviors.

The percentage of total support volume that an AI Agent both participates in and fully resolves, calculated by multiplying Involvement Rate by Resolution Rate. This metric reveals the true operational impact of AI across all customer conversations.

A legacy support metric measuring the average duration of customer interactions. In AI-first support models, AHT becomes less meaningful for human agents as they shift from handling volume to resolving complex, time-intensive issues that AI cannot address.

Customer Satisfaction Score - measures customer contentment with a specific interaction, product, or service. Typically collected through post-interaction surveys with ratings on a 1-5 scale. Provides immediate feedback on service quality.

A comprehensive metric that evaluates and scores every customer interaction across sentiment, resolution quality, and service quality. Used to identify patterns and optimization opportunities in AI Agent performance rather than evaluating individual queries.

An AI agent's ability to understand and incorporate relevant situational information—such as conversation history, customer data, and environmental factors—when generating responses and making decisions.

The maximum amount of text (measured in tokens) that an AI model can process and remember at once during a conversation or task. Larger context windows enable AI to handle longer, more complex interactions.

A role focused on designing how AI Agents communicate by defining tone of voice, response structure, handoff logic, and interaction flows. Ensures the AI Agent speaks clearly, helpfully, and consistently with brand standards and customer expectations.

A measure of how well an AI agent handles customer interactions—evaluating accuracy, completeness, tone, and whether the customer's problem was actually resolved, not just whether a response was generated.

Technology that enables natural, human-like dialogue between people and machines using natural language processing, machine learning, and contextual understanding. Powers customer service automation through text and voice interactions.

An AI-powered system that acts on behalf of customers to autonomously handle tasks, make decisions, and interact with businesses to achieve desired outcomes without requiring human intervention.

A legacy metric measuring the percentage of customer inquiries that do not reach a human agent. While traditionally used as a proxy for automation success, deflection rate can be misleading because it doesn't indicate whether customer issues were actually resolved.

Mathematical representations that convert text into numerical vectors, capturing semantic meaning so that similar concepts are positioned close together. The foundation behind semantic search and retrieval-augmented generation.

AI systems designed to recognize, understand, and respond appropriately to human emotions. Empathetic AI adjusts its communication style and approach based on customer sentiment and emotional state.

AI capability to store and recall specific interaction histories with individual customers. Enables personalized service by remembering past conversations, preferences, and issues. Critical for maintaining context across customer journey touchpoints.

A patented AI architecture purpose-built for customer service scale and complexity. Features layered custom-trained fin-cx models, multi-stage validation, modular sub-agent architecture, and intelligent content retrieval and ranking for accuracy, speed, and reliability.

Proprietary AI models purpose-built for customer service, including fin-cx-retrieval and fin-cx-reranker. Custom-trained on real support interactions, these models power the Fin AI Engine to deliver superior accuracy, speed, and reliability compared to generic LLMs.

A specialized AI model that scores and ranks retrieved content for relevance, accuracy, and contextual fit. Evaluates each piece against the customer query, downranks outdated sources, and selects the final content for the LLM to use in response generation.

A specialized AI model that scans knowledge sources to identify and retrieve relevant content. Uses semantic understanding rather than keyword matching to understand customer intent and select the top candidate content pieces for response generation.

The process of training a pre-trained AI model on domain-specific data to improve its performance for a particular use case. Adapts general-purpose language models to understand industry terminology, company context, and support patterns.

A traditional support metric measuring the percentage of customer issues resolved during the first interaction without requiring follow-up. In AI-first support models, FCR's relevance shifts as AI handles straightforward queries and human agents focus on complex, multi-touch issues.

The true cost of human-powered customer support that includes not just salaries, but benefits, taxes, software, equipment, management overhead, training, quality assurance, attrition, and backfill expenses. Essential for accurately calculating AI ROI and understanding real support economics.

Safety constraints and behavioral boundaries that govern AI Agent actions. Prevent unwanted behaviors, ensure compliance with policies, and maintain brand voice. Critical for deploying autonomous AI in customer-facing environments with confidence.

A feature that uses natural language prompts to control AI Agent tone, vocabulary, style, and behavioral rules. Ensures every response reflects your brand voice and policies by defining how the AI should communicate for specific brands, customer segments, or product lines.

When an AI model generates information that sounds plausible but is factually incorrect or fabricated. In customer service, hallucinations can lead to wrong answers, broken trust, and real consequences for customers.

AI system design where humans remain involved in the decision-making process, providing oversight, validation, or intervention when needed. In customer service, ensures AI Agent quality while maintaining the efficiency of automation.

The AI capability to identify the underlying purpose or goal behind a customer's message, regardless of how it's phrased. Enables systems to understand what customers really want beyond their literal words, improving response accuracy and relevance.

A connector that enables secure access to Intercom conversations, tickets, and user data directly inside AI tools like ChatGPT through the Model Context Protocol.

The percentage of new, incoming customer conversations in which an AI agent actively participates. Involvement rate measures AI coverage across total support volume, showing how much of your inbound conversation flow the AI agent handles before any resolution outcome is measured.

The structured collection of information an AI agent draws from to answer customer questions—help articles, documentation, FAQs, and internal content that serves as the agent's source of truth.

A specialized role responsible for maintaining the structured, accurate content that AI Agents depend on. Owns macros, snippets, help articles, and knowledge base architecture to ensure AI has reliable, current, and scalable information sources.

Advanced AI system trained on vast amounts of text data to understand and generate human language. LLMs enable AI Agents to deeply understand customer intent, maintain context across conversations, and generate natural, accurate responses.

The AI agent's ability to store and recall information across multiple conversation sessions over time. This enables personalized, context-aware experiences by remembering customer preferences, history, and previous interactions.

An open protocol that enables AI models to securely connect to external data sources and tools. MCP standardizes how AI agents access real-time information and execute actions beyond their trained knowledge.

The degradation of an AI model's performance over time as the real-world data it encounters diverges from the data it was trained on. Requires continuous monitoring and retraining to maintain quality.

A quality control system that checks every AI Agent response for accuracy, relevance, and policy compliance before delivery. Part of the Fin AI Engine architecture, this validation layer ensures consistent, high-quality responses and prevents hallucinations.

A support strategy where customers receive consistent, connected service across every channel—chat, email, phone, social—with full conversation context preserved regardless of where the interaction happens.

A structured approach to rolling out AI customer service in stages—starting with lower-risk queries and progressively expanding scope as the system proves reliable and the organization builds confidence.

Initiating customer contact before issues arise or needs are expressed, using AI to identify opportunities for helpful intervention. Shifts from reactive support to anticipatory assistance that prevents problems and enhances experiences.

The AI's ability to make decisions and predictions based on uncertainty, weighing multiple possibilities and their likelihoods rather than operating on absolute certainty. Essential for handling ambiguous customer service scenarios.

A security vulnerability where malicious instructions are inserted into AI prompts to manipulate the model's behavior, bypass safety guidelines, or extract sensitive information. A critical concern for production AI systems.

Machine learning technique where AI learns optimal behavior through trial and error, receiving feedback on actions. In customer service, enables AI to improve resolution strategies based on outcomes and human feedback.

The percentage of customer inquiries that an AI Agent successfully resolves without human intervention. Best-in-class AI Agents like Fin achieve 59%+ average resolution rates, with top performers reaching 80%+ for complex, multi-step queries.

An AI technique where the system pulls relevant content from knowledge bases to craft precise, contextually accurate answers. RAG combines information retrieval with language generation to ensure responses are grounded in actual documentation rather than hallucinated.

AI knowledge base containing factual information, policies, procedures, and conceptual understanding. Distinct from episodic memory of specific interactions. Enables AI to answer questions based on learned information rather than just pattern matching.

Search technology that understands the meaning behind a query rather than just matching keywords. Enables AI agents to find relevant information even when customers describe problems in unexpected or varied ways.

The ability of an AI agent to retain and reference information from the current conversation session. This enables the AI to maintain context and continuity throughout a single customer interaction.

A testing tool that allows teams to run fully simulated customer conversations from start to finish before deploying changes to production. Shows how the AI Agent will respond, when it's reasoning, and where it passes or fails, enabling safe validation of complex workflows and procedures.

An intelligent system that automatically directs customer inquiries to the most appropriate agent, team, or resource based on factors like query type, customer value, agent expertise, and real-time availability.

A technical role responsible for building workflows and backend actions that enable AI Agents to execute tasks beyond just answering questions. Translates customer intents into business system operations, working closely with product and engineering teams.

The use of technology to execute repetitive tasks and workflows without human intervention. In customer service, task automation handles routine processes to improve efficiency and consistency.

An analytics tool that automatically organizes customer conversations into topics and subtopics, revealing what drives support volume and impacts quality. Identifies content gaps, frequent handoffs, and low-resolution areas to prioritize AI Agent improvements.