Agentic Strategy Glossary

An AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike static AI models, agents can plan, reason, use tools, and operate with varying degrees of autonomy.

A category of artificial intelligence systems characterized by autonomous decision-making, goal-directed behavior, and the ability to adapt strategies based on feedback. Agentic AI systems can plan multi-step processes, use external tools, and learn from outcomes.

A set of protocols and tools that allows different software applications to communicate with each other. Essential for integrating AI agents with existing systems and enabling tool use.

The degree to which an AI agent can operate independently without human intervention. Ranges from fully manual (human-in-the-loop) to fully autonomous (agent makes all decisions within defined boundaries).

A strategic approach that focuses on creating new market space and making the competition irrelevant, rather than competing in existing markets. Relevant when identifying new opportunities for AI/agent deployment.

A structured argument for investing in an AI/agent initiative, typically including problem definition, proposed solution, cost-benefit analysis, risks, and success metrics.

A strategic management template for developing new or documenting existing business models. Useful for mapping how AI agents create, deliver, and capture value.

The underlying design of how an AI agent thinks, reasons, and makes decisions. Includes components like memory systems, planning mechanisms, tool use, and feedback loops.

A condition or circumstance that puts a company in a favorable business position. AI agents can create competitive advantage through speed, personalization, or capabilities competitors cannot match.

The amount of information (measured in tokens) that a language model can process in a single request. Larger context windows allow agents to consider more relevant information when making decisions.

A defining capability or advantage that distinguishes an organization from its competitors. Building agent capabilities can become a core competency.

The integration of digital technology into all areas of a business, fundamentally changing how organizations operate and deliver value. Agentic AI represents a new wave of digital transformation.

The gradual decline in an AI system's performance over time, typically caused by changes in the environment, data patterns, or user behavior that the model wasn't trained on.

A mathematical representation of text, images, or other data as vectors in high-dimensional space, enabling semantic similarity comparisons and retrieval.

A predefined process for transferring control from an AI agent to a human operator when the agent encounters situations beyond its capabilities or confidence thresholds.

A system where the outputs or outcomes of an agent's actions are captured and used to improve future performance. Critical for learning and adaptation in agentic systems.

The process of further training a pre-trained AI model on specific data to adapt it for particular tasks or domains.

The competitive edge gained by being the first to enter a market or adopt a technology. Organizations deploying agentic systems early may gain sustainable advantages.

The policies, processes, and controls that ensure AI agents operate safely, ethically, and in alignment with organizational objectives. Includes oversight mechanisms, risk management, and compliance frameworks.

Constraints and safety mechanisms built into AI agents to prevent undesired behaviors, ensure compliance, and protect against risks.

When an AI model generates information that appears plausible but is factually incorrect or entirely fabricated. A critical risk in agentic systems that requires validation mechanisms.

A design pattern where humans remain involved in the decision-making process, typically reviewing or approving agent actions before execution.

The process of creating new value through novel products, services, processes, or business models. Agentic AI enables new forms of innovation previously impossible.

The technical process of connecting AI agents to existing systems, data sources, and tools within an organization's technology stack.

A lightweight data interchange format that is easy for humans to read and write and for machines to parse and generate. Commonly used for AI agent API communications.

A measurable value that demonstrates how effectively an organization is achieving key business objectives. Essential for measuring AI agent impact and ROI.

A structured representation of information that captures relationships between entities, enabling more sophisticated agent reasoning and decision-making.

A methodology for developing businesses and products that aims to shorten product development cycles. Applicable to AI agent pilot programs and rapid experimentation.

The systematic process of capturing performance data, identifying improvement opportunities, and updating agent behavior based on real-world outcomes.

A neural network trained on vast amounts of text data, capable of understanding and generating human-like text. Forms the cognitive core of many modern AI agents.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. Underlies many agent capabilities.

An agent's ability to store and retrieve information from past interactions, enabling context-aware decisions and personalized responses. Can be short-term (within a conversation) or long-term (across sessions).

A version of a product with just enough features to be usable by early customers. A proven approach for launching initial agent capabilities.

An architecture where multiple AI agents collaborate, each with specialized capabilities, to accomplish complex tasks that would be difficult for a single agent.

A branch of AI that enables computers to understand, interpret, and generate human language. Foundation technology for conversational agents.

When a product or service becomes more valuable as more people use it. AI agents that learn from user interactions can create powerful network effects.

The ability to understand what an AI agent is doing, why it made specific decisions, and how it’s performing. Critical for debugging, compliance, and trust.

The combination of organizational structure, processes, and governance that enables a company to deliver value. Agentic operating models integrate AI agents as core operational elements.

The coordination of multiple AI agents, tools, and workflows to achieve complex business objectives. Includes routing, task delegation, and result synthesis.

The monitoring, review, and control mechanisms that ensure AI agents operate within acceptable boundaries and align with organizational policies.

An agent’s ability to break down complex goals into sequences of steps, anticipate obstacles, and adapt strategies based on changing conditions.

A business approach that creates value by facilitating exchanges between interdependent groups. AI agent platforms can create ecosystem effects.

A framework for analyzing competitive forces within an industry. Useful for assessing how AI agents alter competitive dynamics.

The practice of crafting inputs (prompts) to AI models to elicit desired outputs. Includes techniques like few-shot learning, chain-of-thought, and structured instructions.

Systematic processes to ensure products or services meet specified requirements. Essential for validating agent behavior before deployment.

A technique where an AI agent retrieves relevant information from external knowledge sources before generating responses, reducing hallucinations and enabling access to current data.

The cognitive process where an AI agent analyzes information, draws inferences, and makes logical connections to solve problems or answer questions.

An agent’s ability to handle errors gracefully, recover from failures, and maintain performance in the face of unexpected inputs or system issues.

An architectural style for building web services that use HTTP requests to access and manipulate data. Common interface for agent tool integration.

A performance measure used to evaluate the efficiency of an investment. Critical metric for justifying AI agent initiatives.

The ability to grow and handle increased demand without compromising performance or proportionally increasing costs. Critical consideration for agent deployment.

The degree to which AI agent initiatives map to explicit business outcomes, organizational priorities, and strategic objectives.

A strategic planning technique identifying Strengths, Weaknesses, Opportunities, and Threats. Useful for assessing AI agent initiatives.

Initial instructions given to an AI agent that define its role, behavior guidelines, constraints, and capabilities. Acts as the agent’s “operating system.”

The basic unit of text that AI models process, roughly equivalent to a word or word fragment. Context limits and costs are typically measured in tokens.

An agent’s ability to interact with external systems, APIs, databases, and services to gather information or take actions beyond text generation.

The ability to track an agent’s decision-making process, including what information it accessed, what reasoning it applied, and what actions it took.

A deep learning architecture that revolutionized natural language processing. The foundation of modern large language models and AI agents.

A specific situation or scenario in which a product or service could be used. Essential for scoping and prioritizing agent implementations.

The overall experience of a person using a product or service. Critical consideration for agent interface design and interaction patterns.

A set of activities that a firm performs to deliver a valuable product or service. AI agents can transform multiple steps in the value chain.

The unique value a product or service provides to customers. Articulating agent value propositions is critical for adoption.

A specialized database optimized for storing and retrieving embeddings, enabling semantic search and similarity matching for agent memory and knowledge retrieval.

A predefined sequence of steps that an agent follows to accomplish a task, often involving multiple decisions, tool calls, and conditional logic.

A markup language that defines rules for encoding documents in a format that is both human-readable and machine-readable. Sometimes used for structured agent outputs.

A human-readable data serialization language commonly used for configuration files. Often used to define agent workflows and system configurations.

An AI model’s ability to perform tasks it wasn’t explicitly trained on, using only natural language instructions. Enables flexible agent capabilities without retraining.