Agentic AI

Definition of Skill Repository A skill repository is a managed collection of reusable skills available to an AI agent for performing different tasks and subtasks. It can contain both pre-installed skills supplied as part of the agent’s default capabilities and custom skills that have been subsequently developed, tested, validated, and approved for reuse. Rather than requiring the agent to consider every available skill directly during every task, the repository provides an organized source from which a task-relevant subset of skills can be retrieved. For a skill repository $\mathcal{S}={s\_1,s\_2,\ldots,s\_n}$, where $\mathcal{S}$ (skill repository) represents the complete collection of available skills and each $s\_i$ (reusable skill) represents an individual skill, a new task $\tau$ (target task) can be used as the basis for task-aware skill retrieval. Retrieval may use skill metadata, semantic matching, keyword or metadata search, capability matching, constraint filtering, ranking, or combinations of these methods to derive $\mathcal{S}\_{\tau}\subseteq\mathcal{S}$, where $\mathcal{S}\_{\tau}$ (task-relevant skill set) contains the skills considered sufficiently relevant or potentially useful for the particular task. For more complex repositories or tasks, this retrieval and filtering process may be delegated to a specialized skill-selection subagent operating within its own context window, allowing it to examine the task requirements and available skill descriptions without unnecessarily occupying the main agent’s working context. The resulting task-relevant skill set can then be made available to the main agent, which dynamically determines which skill to invoke for a particular subtask or execution state as the task progresses. The repository therefore serves not merely as storage, but as an organized and extensible skill-management layer supporting skill registration, discovery, retrieval, selection, reuse, and continued expansion across different agentic tasks. ...

Definition of Agentic Task An agentic task is a composite task performed by an AI agent through a structured combination of multiple subtasks, where each subtask represents a distinct part of what needs to be accomplished and is fulfilled by an appropriate reusable skill. An agentic task can be represented as $\tau=({T}\_{\tau},{S}\_{\tau},{R}\_{\tau},{M}\_{\tau})$, where $\tau$ (agentic task) denotes the complete task to be performed, ${T}\_{\tau}={t_1,t_2,\ldots,t_n}$ (subtask set) contains the subtasks that collectively constitute the task, ${S}\_{\tau}={s_1,s_2,\ldots,s_m}$ (skill set) contains the reusable skills available for fulfilling those subtasks, ${R}\_{\tau}$ (execution relations) specifies how the subtasks and their corresponding skills are organized and performed sequentially, iteratively, recursively, or through an appropriate combination of these patterns, and ${M}\_{\tau}$ (evaluation metrics) contains the task-specific measures used to determine whether intermediate results and the overall task outcome satisfy the expected requirements. For each subtask $t_i$ (subtask), an appropriate skill $s_j$ (reusable skill) may be selected from predefined skills or dynamically engineered during task execution when an existing skill does not adequately address the subtask. ...

Definition of Callable Skills Callable skills in Agentic AI are skills that an AI agent can actively invoke during task execution when their capabilities, procedures, or instructions are relevant to the requirements of the current task. A callable skill typically represents a defined unit of task-supporting capability, such as retrieving information, analyzing content, processing data, generating or modifying an artifact, validating an output, interacting with a tool, or carrying out a specialized workflow. Describing a skill as callable emphasizes that the skill is not merely defined, documented, or known to the agent, but is available through an invocation mechanism that allows the agent to activate it and use its prescribed functionality as part of the ongoing workflow. Callable skills may be selected from a broader runtime skill catalog according to the task instructions, detected requirements, current context, intermediate results, and execution conditions. Multiple callable skills may therefore be invoked sequentially, conditionally, or in combination to support different subtasks, while skills that are available but irrelevant to the current task can remain unused. ...

Definition of Instruction Space An instruction space is the organized set of instructions that may govern, constrain, guide, or otherwise influence the behavior of an AI agent. It can be represented as $I = \{i_1, i_2, \ldots, i_n\}$, where $I$ denotes the instruction space and each $i_j \in I$ denotes a unit instruction expressing an individual rule, requirement, constraint, preference, procedure, condition, or behavioral direction that the agent can interpret and apply. The instruction space may contain instructions originating from different authorized sources or functional levels, such as system instructions $I^{S}$, user instructions $I^{U}$, task instructions $I_{\tau}$, and skill or workflow instructions $I^{K}$, with these instruction sets forming relevant parts of the broader space, for example $I^{S}, I^{U}, I_{\tau}, I^{K} \subseteq I$. The elements of $I$ do not necessarily have equal authority, applicability, or effect: some instructions may apply generally, others only to a particular task $\tau$, state $z_t$, or runtime condition, and some may be overridden, disabled, superseded, or rendered inapplicable according to the governing instruction hierarchy and applicable conditions. Thus, $I$ represents the broader space of instructions potentially relevant to agent behavior, while an active instruction set $I^{*}_{\tau,t} \subseteq I$ can represent the instructions that actually govern or guide the agent for task $\tau$ at execution step $t$. ...

Definition of Runtime Skill Catalog A runtime skill catalog in Agentic AI is the collection of skills that are available to an AI agent for consideration and use while it is actively performing a task. Each skill represents a defined capability, procedure, workflow, or set of instructions for handling a particular type of requirement or subtask, such as information retrieval, document analysis, data processing, content generation, validation, or interaction with an external tool. The catalog provides the agent with an organized set of possible skills from which relevant ones can be identified and selected according to the current task, rather than requiring every available skill to be applied. It may contain skills that are permanently available in the agent’s environment as well as skills that become available dynamically because of the current configuration, connected tools, user permissions, task context, or other runtime conditions. In this sense, the runtime skill catalog describes the agent’s currently accessible skill space: it indicates what skills the agent can potentially use at a particular point during execution, while the actual skills triggered for a task form only a task-relevant subset of that catalog. ...

Definition of Agentic AI Agentic AI refers to an approach to artificial intelligence in which an AI system is designed not only to generate responses but also to pursue defined objectives by determining and performing actions within an operational environment. An agentic AI system may interpret a task, reason about what needs to be accomplished, plan or select appropriate actions, use available tools and information sources, execute individual steps, evaluate intermediate results, and determine subsequent actions according to the current state of the task. Depending on its design, these activities may include searching for information, analyzing data, interacting with software, generating or modifying files, calling external tools, coordinating multiple operations, or requesting additional information when necessary. The defining characteristic of agentic AI is therefore not simply the use of a large language model, but the organization of AI capabilities into a goal-directed process in which reasoning and action can be connected across multiple steps. The degree of autonomy can vary substantially: some agentic AI systems operate within tightly predefined workflows, while others are given greater flexibility to determine how an objective should be achieved. ...

Definition of Fallback Instructions Fallback instructions in the context of agentic AI are predefined alternative instructions that specify how an AI agent should proceed when its primary instructions, preferred method, or intended workflow cannot be successfully performed. They provide the agent with an alternative path toward the same task objective rather than allowing the process to stop unnecessarily when the preferred approach encounters a limitation or failure. The fallback instructions may simplify the original procedure, relax nonessential requirements, use a different available tool or information source, substitute an alternative method, or produce a more limited but still useful result. For example, if the primary instructions require an agent to retrieve information from a specialized external tool that is unavailable, fallback instructions might direct the agent to use another approved information source or complete only the parts of the task that can be reliably performed. Fallback instructions therefore serve as a form of predefined contingency guidance: the primary instructions describe how the task should preferably be performed, while the fallback instructions describe an acceptable alternative when that preferred approach is not feasible. Importantly, fallback instructions do not necessarily represent an inferior method; they represent a secondary method selected because the conditions required for the primary method are not satisfied. ...