#105 🧠 AI Agent Terminology Cheatsheet: 100 Essential Terms Every Product Manager Should Master

AI agents are dominating tech headlines and industry conversations, yet we're only at the surface of understanding and implementing these powerful systems. As product managers, we find ourselves at the frontier of a technology that promises to fundamentally change how users interact with digital products.

Despite all the buzz, many of us are still grappling with what agents actually are, how they differ from traditional AI systems, and what capabilities they truly offer. The terminology around agents can be particularly challenging, with concepts that blend technical capabilities, cognitive science, and system design in novel ways.

This comprehensive cheatsheet equips you with 100 must-know AI agent terms to help you navigate this rapidly evolving landscape. Whether you're evaluating agent platforms, communicating with technical teams these terms will provide the foundation you need to lead with confidence and clarity.

Foundation Models & Core Concepts

1. Agent - An AI system that perceives environments, makes decisions, and takes actions toward goals

2. Agentic AI - AI systems capable of autonomous decision-making and action without direct human oversight

3. LLM (Large Language Model) - Foundation model trained on text data that powers many agent systems

4. MoE (Mixture of Experts) - Neural network architecture that routes inputs to specialized sub-models

5. Foundation Model - Pre-trained AI system serving as the base for agent capabilities

6. Fine-tuning - Process of adapting pre-trained models for specific tasks or domains

7. Multi-modal - Systems that can process multiple types of inputs (text, images, audio, etc.)

8. Cognitive Architecture - Framework defining how an AI agent processes information and makes decisions

9. Agency - The ability to act independently toward objectives

10. Embodied AI - Agents that exist within and interact with physical or virtual environments

Agent Reasoning & Decision-Making

1. LRM (Large Reasoning Model) - Models optimized for advanced logical reasoning capabilities

2. Deliberative Reasoning - Systematic thinking through of consequences before action

3. CoT (Chain-of-Thought) - Breaking complex reasoning into explicit step-by-step processes

4. ToT (Tree-of-Thought) - Exploring multiple reasoning pathways simultaneously

5. ReAct - Framework combining reasoning and action in alternating steps

6. Planning - Determining sequences of actions to achieve goals

7. Strategic Reasoning - Long-term decision making considering multiple future scenarios

8. Tactical Reasoning - Short-term decision making focused on immediate obstacles

9. Bounded Rationality - Making decisions under constraints of limited information and computing resources

10. Meta-cognition - Agent's awareness and regulation of its own thinking processes

Memory & Knowledge Systems

1. Agent Memory - Systems allowing agents to store and retrieve information over time

2. Working Memory - Short-term storage for immediate task processing

3. Episodic Memory - Storage of specific experiences or interactions

4. Semantic Memory - Storage of factual knowledge and general information

5. Procedural Memory - Storage of action sequences and operational knowledge

6. Vector Database - Storage system for embedding representations used in agent memory

7. Knowledge Graph - Structured representation of relationships between entities

8. Embedding - Dense vector representation of information for efficient processing

9. Context Window - Amount of information an agent can consider at once

10. Retrieval - Process of accessing stored information when needed

Tool Use & Interaction

1. Tool Use - Agent capability to leverage external systems to extend functionality

2. Function Calling - Mechanism for agents to execute code or API calls

3. Tool Selection - Process of choosing appropriate tools for specific tasks

4. API Integration - Connecting agents to external services via application programming interfaces

5. Structured Output - Generating responses in specific formats (JSON, XML, etc.)

6. MCP (Model Connection Protocol) - Standard for connecting AI models with external tools

7. Sandbox - Controlled environment for testing agent actions safely

8. Effectuation - Process by which agents convert decisions into actions

9. Action Space - Complete set of possible actions available to an agent

10. Tool Library - Collection of functions and APIs available for agent use

Multi-Agent Systems

1. Multi-Agent System - Environment where multiple AI agents interact and collaborate

2. Agent Communication - Protocols for information exchange between agents

3. Collaborative Agents - Agents designed to work together toward shared goals

4. Competitive Agents - Agents pursuing individual objectives in shared environments

5. Swarm Intelligence - Collective behavior emerging from decentralized, self-organized agents

6. Orchestration - Coordination of multiple agents in complex workflows

7. Agent Specialization - Optimization of agents for specific tasks or domains

8. Role Assignment - Process of determining which agent handles which tasks

9. Consensus Mechanisms - Methods for agents to reach agreement on decisions

10. Emergent Behavior - Complex patterns arising from interactions of simpler agents

Agent Architecture & Design

1. Workflow - Predefined sequence of operations for agents to follow

2. Agent Pipeline - Connected sequence of processing steps in agent systems

3. Modular Architecture - Design approach using interchangeable components

4. Hierarchical Agents - Multi-level systems with different layers of abstraction

5. Reactive Agents - Simple agents that respond directly to environmental stimuli

6. BDI (Belief-Desire-Intention) - Framework modeling agents based on mental attitudes

7. Actor Model - Computational model treating agents as concurrent computational entities

8. Black Box - Components whose internal workings are not transparent

9. White Box - Components with transparent internal operations

10. Agent Topology - Structural arrangement of agent components and connections

Learning & Adaptation

1. Reinforcement Learning - Learning through trial and error with reward signals

2. RLHF (Reinforcement Learning from Human Feedback) - Using human evaluations to guide learning

3. DPO (Direct Preference Optimization) - Learning directly from preference comparisons

4. Constitutional AI - Systems with built-in constraints based on principles

5. Self-Improvement - Agent capability to enhance its own performance

6. Transfer Learning - Applying knowledge from one domain to another

7. Curriculum Learning - Training agents with progressively more difficult tasks

8. Imitation Learning - Learning by observing and mimicking example behaviors

9. Meta-Learning - Learning how to learn more efficiently

10. Adaptive Agents - Systems that modify strategies based on environmental changes

Evaluation & Alignment

1. Alignment - Ensuring agent behavior matches human values and intentions

2. HITL (Human-in-the-Loop) - Systems incorporating human oversight and intervention

3. Reward Function - Specification of what constitutes success for an agent

4. Oversight - Monitoring and verification of agent actions

5. Benchmarking - Standardized testing of agent capabilities

6. Evaluation Harness - Framework for assessing agent performance

7. Interpretability - How understandable agent decisions are to humans

8. Explainability - Agent ability to articulate reasoning behind decisions

9. Safety Layer - Protective mechanisms preventing harmful actions

10. Value Alignment - Matching agent objectives with human ethical principles

Specialized Capabilities

1. Vertical Agents - Agents optimized for specific industries or domains

2. RAG (Retrieval-Augmented Generation) - Enhancing outputs with retrieved information

3. Agentic RAG - Systems where agents dynamically decide when and how to retrieve information

4. Self-Healing - Ability to detect and correct errors autonomously

5. Reflection - Agent evaluation of its own performance and decision making

6. Overthinking - Inefficient over-analysis of simple problems

7. Self-Critique - Agent evaluation of its own outputs for quality

8. Hallucination Mitigation - Techniques to reduce false or unfounded outputs

9. Uncertainty Quantification - Agent assessment of confidence in its conclusions

10. Planning Horizon - How far into the future an agent considers consequences

Advanced Concepts & Future Directions

1. AGI (Artificial General Intelligence) - Hypothetical AI with human-like general intelligence

2. Recursive Self-Improvement - Agents enhancing their own capabilities repeatedly

3. Agentic Workflows - Dynamic process management by autonomous systems

4. Cognitive Simulation - Modeling human-like thinking processes in agents

5. Homeostatic Agents - Systems maintaining internal stability despite external changes

6. Counterfactual Reasoning - Considering alternative scenarios that didn't occur

7. Bounded Agency - Operating with defined constraints on autonomy

8. Value Handshake - Negotiation between human and agent values and preferences

9. Corrigibility - Agent willingness to be corrected or modified by humans

10. Digital Mind - Philosophical concept of consciousness in advanced agent systems

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