Enterprise AI Agent Primer: Customer Support Tools, Terms & Technologies

As leaders responsible for customer experience at scale, you’re evaluating AI not as a concept, but as a capability that must operate reliably across channels, systems, and real customer moments. After years building large-scale intelligent systems at Google and IBM, I co-founded Maven to address a gap I saw repeatedly: support automation that could talk, but not truly resolve with contextualization (and without hallucinations). 

Modern customer support requires AI that can reason and act — not just respond — across voice, chat, email, and SMS. Our focus has always been on building agentic AI that can reason, take action, and operate seamlessly across voice, chat, email, and SMS within enterprise environments. 

This “starter” glossary of the five core concepts of agentic ai in CX reflect how we think about that problem in practice, and is intended as a clear, practical reference as you assess what modern AI-driven customer support can and should deliver.

Interested in our complete list of "100 Essential AI Agent Terms?" Read the full article here.

A primer for CX teams under pressure

Enterprise CX teams need to reduce cost, improve resolution speed, and maintain brand trust - all while managing growing volumes across channels. Pressure meets confusion when terms like agentic AI, autonomous agents, and AI copilots are used inconsistently across vendors.

This glossary is designed to:

• Clarify key AI agent concepts in plain, enterprise-relevant language.
• Distinguish conversation-only AI from action-capable agentic systems.
• Provide third-party validation from industry leaders.
• Help CX leaders evaluate technology partners with confidence. 

If you’re exploring how AI could fit into your support strategy, this guide can serve as a shared reference point for CX, IT, and operations stakeholders.

1) Core AI & Agent Concepts

Agentic AI

Agentic AI refers to AI systems that can plan, reason, and execute tasks within explicit, policy-defined constraints in pursuit of a goal — often by invoking tools, APIs, and workflows across enterprise systems. Unlike traditional chatbots or generative AI, agentic systems are designed to complete work, not just generate responses.

IBM describes agentic AI as a pattern where language models are paired with tools and decision logic to act within real systems.
👉 IBM overview: Agentic AI — IBM

Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029, fundamentally changing CX operations.
👉 Gartner press release: Agentic AI Transforming Customer Service

Maven context: Maven AGI is built around agentic execution — enabling AI agents to reason and take secure, auditable actions across customer support workflows.
Learn more: Agent Maven

Maven Leadership’s Perspective:
From my perspective, the most important shift happening right now isn’t generative AI — it’s agency. Enterprises don’t struggle because they lack answers; they struggle because systems can’t take action across fragmented tools. Agentic AI matters because it closes that gap. When AI can reason about intent, understand constraints, and securely execute real workflows, it stops being a demo and starts becoming infrastructure.

AI Agent

An AI agent is a system that can perceive input, maintain context, reason about goals, and take action to achieve outcomes. This definition goes beyond reactive bots and aligns with long-standing AI research.

General definition:
👉 AI Agent — Wikipedia

In CX environments, AI agents must:

• Understand intent across channels.
• Access enterprise knowledge.
• Execute backend actions (not just suggest them).

Maven context: Maven’s AI Agents operate across voice, chat, email, and SMS using a shared reasoning layer, so customers don’t restart conversations when switching channels. AI agents can call eligible Actions defined in Maven with parameters and Instructions; they do not infer new Actions.
Explore agent channels: Agent Channels

Maven Leadership’s Perspective:
AI agents succeed when they combine reasoning with action. CX teams don’t just need answers; they need outcomes. That’s why Maven’s agents are designed to act on intent across all channels and touchpoints.

Autonomy & Human-in-the-Loop (HITL)

Autonomy refers to how independently an AI agent can operate. In enterprise CX, full autonomy is rarely binary. Instead, systems balance automation with human oversight.

Salesforce highlights how agentic AI differs from assistive AI by executing tasks proactively, while still allowing for human review when needed.
👉 What Is Agentic AI - Salesforce

Best practice: High-volume, low-risk workflows are often fully automated, while sensitive edge cases route to human agents with full context preserved.

Maven context: Maven supports configurable human-in-the-loop guardrails, allowing CX teams to define where autonomy ends and human review begins. Autonomy is always bounded by eligibility rules, Knowledge scope, and Action constraints.

Maven Leadership’s Perspective:
Autonomy doesn’t mean removing humans entirely — it means enabling them to focus on high-value exceptions. HITL strategies are central to enterprise reliability and trust.

2) Customer Interaction Channels

Voice AI / Voice Agents

Voice AI enables AI agents to handle live phone conversations - understanding interruptions, accents, and intent while performing real-time actions.

Maven Voice™ is designed for enterprise-grade voice automation, integrating with existing telephony and contact-center infrastructure.Voice channels operate similarly to all other channels, using the same Actions and Knowledge foundations.

To discuss Maven Voice or any of our agent solutions: Schedule Demo

Maven Leadership’s Perspective:
Customers don’t think in channels - they think in outcomes. One of the biggest failures of legacy support technology is treating voice, chat, email, and SMS separately. The agent should be the constant, not the channel. Channels are just interfaces; intelligence should be shared.

Chat & Messaging AI

Chat agents handle real-time digital conversations across web and in-app experiences. They maintain context, access knowledge, and escalate seamlessly when needed.

Maven context: Chat agents are powered by the same reasoning layer as voice and email, enabling consistent answers and actions. All channels share the same Knowledge, Actions, and quality evaluation signals.

Explore chat capabilities: Chat

Maven Leadership’s Perspective:
The shared reasoning engine ensures every interaction feels seamless, regardless of whether the customer switches channels mid-conversation.

Email & SMS Automation

Email and SMS are critical for high-volume interactions. AI can classify, summarize, draft, and resolve messages at scale while preserving tone and compliance.

Maven context: Email and SMS interactions are handled by the same agentic engine, allowing cross-channel continuity and measurable improvements in resolution speed.

Maven Leadership’s Perspective:
Consistency and context are non-negotiable - especially across asynchronous channels like email and SMS.

3) Knowledge, Context & Reasoning

Knowledge Graphs & Grounded AI

Enterprise AI must be grounded in trusted, governed knowledge to avoid hallucinations and policy violations. Without a reliable foundation, even capable models produce inconsistent answers, surface outdated information, or violate policy.

Maven context: Maven’s Graph of Record is a proprietary approach to grounding AI in enterprise truth. It connects knowledge across an organization’s existing systems without copying, migrating, or centralizing data. Rather than asking companies to migrate their information into a new system, Maven creates intelligent, real-time connections to systems of record such as CRMs, ticketing platforms, content repositories, and data warehouses.

Because every response is sourced directly from these systems, the Graph of Record ensures answers reflect the latest data, follow existing permissions, and respect enterprise policies. 

For example, when a customer asks about account status or billing eligibility, the agent responds using live system context, not a cached summary. This dramatically reduces hallucinations, stale responses, and compliance risk, especially in regulated or operationally complex environments.

Maven Leadership’s Perspective:
Intelligence without grounding is a liability. Knowledge, context, and guardrails are the hardest — and most important — work for enterprise-scale AI. Maven’s Graph of Record is the cornerstone of Business AGI because it allows intelligence to scale across the enterprise while remaining accurate, compliant, and operationally sound. By connecting systems instead of duplicating them, organizations can deploy AI faster, reduce hidden infrastructure costs, and move confidently from experimentation to production.

Contextual Memory & RAG

Retrieval-Augmented Generation (RAG) allows agents to fetch relevant enterprise data at runtime. Contextual memory ensures continuity across long or multi-session interactions. RAG in Maven retrieves from scoped Knowledge Bases that respect access rules and surface eligibility.

IBM and Gartner emphasize grounding and context as key to enterprise AI reliability.
👉 IBM enterprise AI insights: Enterprise AI Agents — IBM

Maven Leadership’s Perspective:
RAG and memory allow the AI to act with confidence, avoiding mistakes that could damage customer trust.

4) Integrations & Workflow Execution

CRM & Backend Integrations

AI agents must integrate with CRMs, billing systems, and internal tools to do real work - such as updating records or processing refunds.

Maven context: Maven integrates with systems like Salesforce and Zendesk to allow agents to call eligible Actions that read or write system data.

See all Maven integrations: Integrations

Maven Leadership’s Perspective:
AI that can’t act is just another interface layer. Agents must be first-class operators inside enterprise systems, with full auditability and security.

APIs & Secure Execution

APIs allow agents to interact with enterprise systems, triggering workflows and executing actions in a manner that is both secure and auditable. This ensures compliance with enterprise governance standards while driving operational efficiency and scalability.

Maven Context: Maven's API-first approach empowers agents to execute complex workflows directly within enterprise systems. Maven ensures that every action is governed by strict authentication, traceability, and compliance measures. This allows customers to integrate seamlessly with their existing infrastructure while maintaining control and achieving measurable efficiency gains.

Maven Leadership’s Perspective:
We see secure execution as a strategic enabler for enterprise innovation. Trust and accountability are the cornerstones of successful enterprise automation. By ensuring every automated action is traceable, auditable, and aligned with governance policies, we provide organizations with the confidence to scale their operations securely. 

5) Metrics & KPIs That Matter to CX Leaders

Autonomous Resolution Rate (ARR)

Definition: The percentage of customer inquiries or tickets that are fully resolved by AI agents without requiring human intervention. Autonomous resolution refers to conversations resolved without handoff and without negative feedback, consistent with ‘Resolved by Maven’ in product metrics.

Why it matters: Measures the effectiveness and reliability of agentic AI in executing tasks independently, helping reduce cost and free human agents for higher-value work.

Example: Papaya Pay achieved 90% of inquiries answered autonomously using Maven AGI.

First Contact Resolution (FCR)

Definition: The percentage of customer issues resolved during the first interaction, regardless of channel.

Why it matters: High FCR indicates efficiency and a seamless customer experience. AI agents can increase FCR by having immediate access to knowledge and systems, reducing the need for follow-ups.

Example: Papaya Pay achieved a 70% first contact resolution rate after deploying Agent Maven™.

Time to Resolution (TTR)

Definition: The average elapsed time between a customer submitting an inquiry and the issue being fully resolved.

Why it matters: Lower TTR signals faster service and better CX. AI agents can dramatically reduce TTR by automatically retrieving information, updating systems, and executing actions.

Example: Enumerate resolved 80% of tickets in under three minutes with Agent Maven™; highlighting dramatically lower TTR in production. 

Customer Effort Score (CES)

Definition: A metric reflecting how easy it was for the customer to get their issue resolved, usually measured via post-interaction survey (e.g., “How easy was it to get your issue resolved today?”).

Why it matters: High CES (low effort) correlates with customer satisfaction, loyalty, and reduced churn. AI agents contribute by reducing friction, avoiding unnecessary steps, and providing context-aware assistance.

Example: Mastermind resolved 93% of chat inquiries with 75% faster response times, a strong proxy for reduced effort. 

Reduced overhead costs

Definition: The reduction in operational expenses achieved by automating repetitive tasks, streamlining workflows, and minimizing the need for additional human resources. 

Why it matters: Lower overhead costs allow organizations to allocate budget toward strategic initiatives, improve scalability, and maintain profitability even as customer inquiries grow. AI agents contribute by handling high volumes of interactions autonomously, reducing the reliance on human agents for routine tasks. 

Increase CSAT and NPS

CSAT Definition: A measure of how satisfied customers are with a specific interaction or overall service, typically collected via a short post-interaction survey (e.g., “How satisfied are you with the support you received?” on a 1–5 or 1–10 scale).

Why it matters: CSAT reflects immediate customer sentiment and experience quality. AI agents can influence CSAT positively by providing accurate, fast, and context-aware resolutions.

Examples: Rho achieved 95% customer satisfaction (CSAT) while supporting more contacts without increasing headcount. During Thumbtack’s evaluation of 50+ vendors, Maven performed  15%+ better on the company’s internal CSAT assessment compared to the competition.

NPS Definition: A measure of customer loyalty and likelihood to recommend a brand, collected via a survey question like: “How likely are you to recommend our company to a friend or colleague?” Respondents rate 0–10, and scores are categorized as:

NPS Calculation =%Promoters−%DetractorsNPS

Why it matters: NPS reflects long-term customer loyalty. AI agents that reduce friction, improve resolution speed, and maintain consistent experiences across channels can increase promoter scores and reduce detractors.

Example: Enumerate reported a NPS of +40, indicating strong loyalty and satisfaction with the support experience enabled by Maven.

Julie Mohr, Principal Analyst at Forrester published on December 1, 2025 that “AI and automation in the mix, the old “faster is better” mindset makes even less sense” and offers her perspective on 5 new AI-centric KPIs.

👉 Forrester insight: It’s Time To Rethink Service Desk Success

Maven context: Many Maven customers achieve ARR >90% for targeted workflows.
Explore customer results: Case Studies

Maven Leadership’s Perspective:
Volume deflection alone isn’t success — resolution is. Metrics should reflect true outcomes for customers, not just ticket counts.

‍In Closing

We know that a glossary is likely just one of the many steps in the process for considering if, how and when agentic AI adoption could be adopted within your organization. 

Our sales, solution engineers and customer experience management team work collaboratively with our prospective clients to refine requirements, champion-in investment, build a testing and beta roadmap, and clarify agentic AI’s potential in your CX strategy.

An initial conversation around your goals and stage of consideration could be next!
We would love the opportunity to help shape the path forward: Connect with our team

About the Author

Sami Shalabi

Co-Founder & Chief Technology Officer, Maven AGI

Linkedin Profile

Sami Shalabi leads Maven's product vision and platform architecture for enterprise AI agents across voice, chat, email, and SMS. Former senior engineering leader at Google and IBM, Sami focuses on agentic AI systems that combine reasoning, autonomy, and enterprise integrations to deliver measurable CX outcomes.

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