Agentic AI Glossary: 100 Essential AI Agent Terms for Enterprise Buyers

This glossary is designed to equip buyers, operators, and decision-makers with the core terminology required to confidently evaluate agentic AI platforms and partners, with an emphasis on enterprise readiness, customer experience automation, and production-grade deployment.

Agentic AI Foundations & Core Concepts

1. Agentic AI – AI systems designed to autonomously plan, decide, and act toward goals rather than simply respond to prompts. In enterprise environments, agentic AI enables end-to-end workflows by combining LLMs, tools (Actions), and enterprise Knowledge, as implemented in the Maven platform. 
2. AI Agent – A software-based entity that perceives inputs, reasons over context, and takes actions to achieve specific objectives. Unlike traditional bots, AI agents can adapt behavior dynamically.
3. Autonomy – The degree to which an AI agent can operate independently without human intervention. Higher autonomy increases efficiency but requires stronger governance and guardrails.
4. Goal-Oriented Behavior – An agent’s ability to work toward defined outcomes rather than isolated tasks. This allows agents to sequence actions intelligently across systems.
5. Multi-Agent System (MAS) – An architecture where multiple AI agents collaborate or coordinate to solve complex problems. MAS designs improve scalability and specialization.
6. Action Space – The complete set of actions an agent is authorized to take. Defining the action space is critical for safety and predictability.
7. Policy – A set of rules that governs how an agent makes decisions and selects actions. Policies ensure alignment with business and compliance requirements.
8. Planner – The component responsible for breaking down high-level goals into executable steps. Planning enables agents to handle complex, multi-stage workflows.
9. Executor – The component that carries out planned actions by invoking tools, APIs, or workflows. Executors translate reasoning into real-world impact.
10. Reasoning Engine – The logic layer that evaluates context, constraints, and options to determine next actions. Strong reasoning is foundational to reliable agent behavior.

Memory, Knowledge & Context Management

11. Memory – Storage mechanisms that allow agents to retain and recall information over time. Memory enables continuity, personalization, and learning.
12. Short-Term Memory – Context retained during a single interaction or session. This allows agents to understand conversational flow and immediate intent.
13. Long-Term Memory – Persistent storage of facts, preferences, and historical interactions. Long-term memory supports personalization and institutional knowledge.
14. Vector Database – A specialized database optimized for similarity search over embeddings. Vector databases are commonly used to store agent memory and knowledge.
15. Embeddings – Numerical representations of text or data that capture semantic meaning. Embeddings allow agents to retrieve relevant information efficiently.
16. Retrieval-Augmented Generation (RAG) – A technique that grounds AI outputs by retrieving relevant enterprise knowledge before generating responses. Maven uses retrieval patterns to ground answers in your Knowledge Bases, built from sources like Salesforce, Zendesk, Freshdesk, uploaded files, and crawled URLs.
17. Knowledge Base – A curated collection of enterprise content such as FAQs, policies, and documentation. Agents rely on knowledge bases for grounded responses.
18. Grounding – The practice of anchoring agent responses in verified data sources. Grounding reduces hallucinations and increases reliability.
19. Context Window – The amount of information an LLM can consider at once. Managing the context window is essential for performance and cost control.
20. Hallucination – When an AI model generates incorrect or fabricated information. Enterprise platforms must actively mitigate hallucinations.

Models, Prompts & Tooling

21. Large Language Model (LLM) – A model trained on massive text corpora to understand and generate language. LLMs serve as the reasoning core for many agents.
22. Foundation Model – A general-purpose model that can be adapted to many downstream tasks. Foundation models enable flexibility across use cases.
23. Prompt Engineering – The practice of designing prompts that guide agent behavior and outputs. Effective prompting improves consistency and reliability.
24. System Prompt – Instructions that define an agent’s role, tone, boundaries, and objectives. System prompts provide behavioral alignment.
25. Tool Calling – Allowing agents to invoke external tools, APIs, or systems to complete tasks. Tool calling enables real-world action.
26. Function Calling – A structured approach for models to call predefined functions with validated inputs. This increases precision and safety.
27. API Integration – Connecting agents to enterprise systems such as ticketing, billing, or data platforms. Integration unlocks automation at scale.
28. CRM Integration – Using customer data to personalize and contextualize agent actions. CRM integration is critical for CX use cases.
29. Event-Driven Architecture – A design where agents respond to events or triggers rather than static requests. This supports proactive automation.
30. Workflow Automation – End-to-end execution of business processes without manual intervention. Agentic workflows go beyond simple rule-based automation.

Orchestration, Reliability & Scale

31. Orchestration – Coordinating multiple agents, tools, and workflows into a cohesive system. Maven customers use orchestration to connect Actions, Knowledge, and workflows into end-to-end support processes (see real-world MavenAGI case studies).
32. Concurrency – The ability to run multiple agent processes simultaneously. Concurrency is required for high-volume environments.
33. Latency – The time it takes for an agent to respond or act. Low latency is critical for customer-facing use cases.
34. Throughput – The number of tasks an agent system can handle over time. Throughput reflects scalability.
35. Scalability – Maintaining performance and reliability as demand increases. Enterprise agents must scale predictably.
36. Fallback Strategy – Predefined alternative paths when agents fail or lack confidence. Fallbacks protect customer experience.
37. Escalation Path – Routing issues to humans or specialized agents when needed. Effective escalation balances automation with trust.
38. Self-Healing – Automatic detection and recovery from errors or failures. Self-healing improves resilience.
39. State Management – Tracking agent context and progress across interactions. State management ensures continuity.
40. Session Management – Handling discrete user-agent interactions securely and efficiently.

Governance, Security & Compliance

41. Guardrails – Constraints that prevent unsafe, non-compliant, or undesired actions. Guardrails are policies, constraints, and checks that keep agents’ behavior safe and compliant, especially when they call Actions or use enterprise Knowledge (see MavenAGI’s Trust & Security Compliance practices).
42. Human-in-the-Loop (HITL) – Human review embedded within agent workflows. HITL is often used during early deployment stages.
43. Human-on-the-Loop – Humans supervise agents and intervene only when thresholds are crossed. This enables scale with oversight.
44. Governance – Policies, controls, and processes that ensure responsible agent behavior. Governance is critical for enterprise adoption.
45. Compliance – Adherence to regulatory, legal, and organizational standards. Compliance requirements vary by industry.
46. Data Privacy – Protecting sensitive customer and enterprise data used by agents. Privacy-by-design is essential.
47. Security – Safeguards against unauthorized access, misuse, or data leakage. Security underpins trust.
48. Access Control – Managing permissions for agent actions and data access. Fine-grained control reduces risk.
49. Audit Logs – Immutable records of agent actions and decisions. Auditability supports compliance and accountability.
50. Explainability – The ability to understand and justify why an agent took a particular action.

Evaluation, Optimization & Learning

51. Observability – Visibility into agent behavior, decisions, and outcomes. Observability enables continuous improvement.
52. Telemetry – Operational data emitted by agents for monitoring and analysis. Telemetry feeds evaluation systems.
53. Evaluation (Eval) – Measuring agent accuracy, quality, and business impact. Evals should be ongoing, not one-time.
54. Benchmarking – Comparing agent performance against baselines or alternatives. Benchmarks inform investment decisions.
55. A/B Testing – Testing multiple agent variants to identify optimal performance. A/B testing reduces risk.
56. Reflection – An agent’s ability to assess and improve its own outputs. Reflection increases robustness.
57. Feedback Loop – Incorporating user or system feedback into agent improvement cycles.
58. Continuous Learning – Ongoing refinement of agent behavior over time. Continuous learning supports long-term value.
59. Synthetic Data – Artificially generated data used for testing or training. Synthetic data reduces dependency on sensitive data.
60. Simulation – Testing agent behavior in controlled environments before production deployment.

CX, Automation & Business Outcomes

61. Conversational AI – Agents that interact with users through natural language. Conversational agents are central to CX automation.
62. Natural Language Understanding (NLU) – Interpreting user intent, entities, and context. NLU enables accurate routing and resolution.
63. Natural Language Generation (NLG) – Producing coherent, human-like responses. NLG impacts perceived quality.
64. Sentiment Analysis – Detecting emotional tone in user messages. Sentiment informs escalation and personalization.
65. Intent Classification – Categorizing user requests into predefined intents. Intent accuracy drives resolution rates.
66. Slot Filling – Extracting structured information from unstructured input. Slot filling supports automation.
67. Personalization – Tailoring agent behavior based on user context and history. Personalization improves CSAT.
68. CX Automation – Using agents and workflows to handle customer interactions and back-end tasks without manual effort. In Maven, CX automation is powered by the Maven platform, where agents use Actions and Knowledge to resolve conversations and integrate with systems like CRMs and help desks.
69. Autonomous Resolution – Completing customer tasks without human involvement. Autonomous resolution reduces cost-to-serve.
70. Case Deflection – Resolving issues without creating support tickets. Deflection improves operational efficiency.

Metrics, ROI & Buyer Considerations

71. First Contact Resolution (FCR) – Resolving customer issues in a single interaction. FCR is a key CX metric.
72. CSAT – Customer Satisfaction Score measuring perceived experience quality.
73. Net Promoter Score (NPS) – A metric indicating customer loyalty and advocacy.
74. ROI – Financial return generated from agentic AI investments. ROI justification is essential for scale.
75. Time to Value (TTV) – How quickly an organization realizes benefits from deployment.
76. Cost Optimization – Managing compute, tooling, and model costs. Cost control supports sustainability.
77. Token Management – Controlling LLM input and output usage. Token efficiency directly impacts cost.
78. Adoption Metrics – Tracking agent usage and engagement across teams or customers.
79. Change Management – Preparing people and processes for agent adoption. Change management reduces resistance.
80. Enablement – Training teams to work effectively with agentic systems.

Platform Strategy & Enterprise Readiness

81. Agent Framework – Software frameworks used to build and manage agents. Framework choice impacts flexibility.
82. Platform Approach – Standardizing on a single system to deploy, monitor, and govern agents instead of stitching together point tools. Maven follows this model with a platform that unifies Knowledge, Actions, quality controls, and Insights.
83. Best-of-Breed – Combining specialized tools instead of a single platform. This increases flexibility but adds complexity.
84. Interoperability – The ability for agents to work across systems and vendors.
85. Vendor Lock-In – Dependency on a single technology provider. Buyers should evaluate exit strategies.
86. Open Standards – Shared protocols that enable portability and integration.
87. Roadmap – Planned evolution of agent capabilities and features.
88. Proof of Concept (POC) – A limited deployment used to validate value before scaling.
89. Production Readiness – Stability, security, and scalability required for live environments.
90. Enterprise-Grade – Meeting requirements for reliability, governance, security, and scale so agents can safely run in production. Maven is designed for enterprise-grade deployments, including deep integrations, analytics, and control over Actions and Knowledge (see real-world MavenAGI case studies).

Advanced Agent Capabilities

91. Proactivity – Agents initiating actions without explicit prompts. Proactive agents unlock new efficiencies.
92. Recommendation Engine – Suggesting next-best actions based on context and data.
93. Decision Support – Assisting humans with insights while leaving final decisions to people.
94. Contextual Awareness – Understanding situational nuance beyond raw input.
95. Confidence Scoring – Estimating the reliability of agent outputs.
96. Maturity Model – A framework describing stages of agent capability growth.
97. Build vs. Buy – Evaluating whether to build custom agents or adopt a platform.
98. Knowledge Grounding Strategy – A systematic approach to ensuring factual accuracy.
99. Operational AI – AI embedded directly into live business processes.
100. Autonomous Enterprise – An organization where agents handle a large share of repeatable operations and workflows within guardrails, while humans focus on judgment, relationships, and exceptions. In Maven, this looks like hybrid teams of people plus agents, not AI-only operations.

For implementation guidance and real-world examples, explore our customer stories or reach out to our team for a live walkthrough.