Classification & Categorization, as an agentic skill, is the capability of an AI agent to assign items such as documents, records, messages, images, transactions, products, incidents, or data points to predefined or discovered categories, labels, codes, classes, or taxonomies.

It operates by examining features, meanings, patterns, metadata, or contextual signals in the input and matching them to category definitions, rules, learned models, or similarity structures.

The skill can support activities such as routing cases to the correct queue, tagging content for retrieval, coding observations for analysis, grouping customers or risks, and preparing labeled data for reporting or downstream modeling.

It is different from merely organizing content into tables, outlines, or schemas, because its primary output is a category assignment rather than a new arrangement of the material.

In practice, classification may be deterministic, rule-based, probabilistic, or based on machine-learned patterns, and it can handle large volumes of items more consistently and rapidly than manual review.

However, category boundaries can be ambiguous, overlapping, or dependent on domain context, and an AI agent may misclassify unusual, incomplete, or subtly phrased cases.

Therefore, this skill is most reliable when category definitions are clear, when confidence or uncertainty can be reported, and when human professionals review borderline, high-stakes, or conceptually complex assignments.

Calculation & Quantitative Computation, as an agentic skill, is the capability of an AI agent to perform mathematical operations, statistical aggregations, formula evaluations, numerical simulations, and algorithmic processing on structured data or defined parameters. It operates by parsing mathematical expressions, applying deterministic algorithms, executing code-based computations, and processing large datasets to produce precise numerical outputs, ratios, projections, or statistical metrics.

This skill is highly valuable for tasks requiring rapid, high-volume, or complex numerical processing, such as financial modeling, engineering calculations, risk scoring, and data analysis, where it eliminates manual arithmetic errors and drastically reduces processing time. However, quantitative computation is strictly bounded to executing defined mathematical logic; it does not inherently understand the real-world context of the numbers, validate the underlying business assumptions, or interpret the strategic significance of the results.

Therefore, in human–AI collaboration, the agent can execute the heavy computational lifting and data processing, while the human professional defines the mathematical models, verifies the input data quality, and applies analytical judgment to interpret what the computed results mean for the specific domain.

In action-based skill selection, where skills are chosen according to the specific operation a task requires, this skill is selected when the immediate need is to execute precise numerical transformations, statistical summaries, or algorithmic calculations rather than to conceptually reason, qualitatively evaluate, or creatively generate new insights.

Data & Document Extraction, as an agentic skill, is the capability of an AI agent to pull structured values, named entities, dates, amounts, statuses, clauses, and other factual elements out of unstructured or semi-structured materials such as documents, forms, emails, scanned pages, images, tables, and files.

It goes beyond simply locating a source by converting raw content into machine-readable fields, records, or annotations that can support later operations such as comparison, verification, summarization, analysis, reporting, or workflow automation.

The skill typically depends on natural language comprehension, document parsing, image interpretation, and extraction rules or model-based inference to identify what information is present, normalize its format, and place it into a usable structure.

Its value is especially clear when large volumes of textual or visual material must be processed quickly and consistently, but the output may still require human review because extraction can be affected by poor source quality, ambiguous wording, unusual layouts, missing values, or context-dependent meaning.

In human–AI work, this skill can either reduce manual data-entry and review effort or work alongside professional judgment, where the agent produces structured evidence and the human validates accuracy, resolves exceptions, and interprets significance for the task.

1.1. Blockchain Engineer as an Occupation

A blockchain engineer designs, implements, and secures distributed ledger protocols, consensus mechanisms, and smart contracts to build scalable, trustless, and decentralized systems. This occupation connects cryptography, distributed systems theory, game theory, and software engineering by transforming theoretical trust models and economic incentives into robust, immutable, and executable network architectures. To ensure that these decentralized systems operate securely and efficiently without central oversight, the engineer must understand how cryptographic primitives, network latency, state machine replication, economic attack vectors, and scalability constraints collectively determine the viability and security of the blockchain ecosystem.

Typical responsibilities of a blockchain engineer include:

• blockchain architecture and protocol design;
• smart contract development and security auditing;
• decentralized application (DApp) integration and backend engineering;
• cryptographic implementation and consensus optimization;
• network infrastructure, node operations, and scalability; and
• blockchain testing, deployment, and lifecycle management.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, architectural context, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Blockchain Engineering

Blockchain engineering increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by blockchain engineers. They include distributed systems reasoning, cryptographic intuition, game-theoretic modeling, security mindset, economic incentive design, threat modeling, state-machine logic formulation, and architectural judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In blockchain engineering, these skills include smart contract code generation, static vulnerability analysis, fuzzing script creation, gas optimization suggestion, formal verification scaffolding, network simulation, log parsing, and documentation drafting.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the economic implications of a novel tokenomics model or identifying a complex logical re-entrancy exploit relies primarily on human knowledge of game theory and adversarial thinking; an AI agent may then use agentic skills to generate thousands of fuzzing test cases, simulate network congestion, and scan the codebase for known vulnerability signatures. In protocol design, the engineer first determines the appropriate consensus mechanism and trust assumptions, and an AI agent subsequently scaffolds the peer-to-peer networking code and simulates message propagation latencies. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Computer Systems Analyst as an Occupation

A computer systems analyst studies an organization’s current computer systems, procedures, and business workflows to design technological improvements that enhance operational efficiency. This occupation serves as the critical bridge between business objectives and information technology (IT) implementation. Unlike software developers who primarily focus on writing application code, or business analysts who focus on high-level process optimization, the computer systems analyst evaluates the entire technological ecosystem—including hardware, software, networking, and data architecture—to design integrated solutions that solve complex data processing and operational problems. To ensure these systems deliver tangible value, the analyst must understand how business requirements, technical constraints, cost-benefit ratios, system compatibility, and user capabilities collectively determine the viability of an IT solution.

Typical responsibilities of a computer systems analyst include:

• business requirements analysis and cost-benefit evaluation;
• system architecture design and technical specification;
• system integration and compatibility management;
• system implementation, testing, and quality assurance; and
• user training, documentation, and production support.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Computer Systems Analysis

Computer systems analysis increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by computer systems analysts. They include business process reasoning, systems thinking, architectural judgment, stakeholder communication, cost-benefit analysis, technical translation (bridging IT and business vocabularies), problem sensitivity, and deductive reasoning.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In computer systems analysis, these skills include requirements extraction from interview transcripts, diagram generation (e.g., UML, data flow diagrams), code snippet generation for integration scripts, automated test case creation, log parsing for system troubleshooting, technical documentation drafting, and API compatibility scanning.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the nuanced political and operational constraints of a legacy business unit relies primarily on human knowledge of organizational dynamics and workflow realities; an AI agent may then use agentic skills to structure those qualitative insights into formal use cases and user stories. In system design, the analyst first determines the logical boundaries between hardware and software components, and an AI agent subsequently generates the initial architecture diagrams and suggests standard integration patterns. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Document Management Specialist as an Occupation

A document management specialist implements, configures, and administers enterprise-wide document management systems (DMS) and related procedures that allow organizations to capture, store, retrieve, share, and govern electronic content. This occupation connects information technology, business operations, and information governance by transforming chaotic, decentralized file shares into structured, secure, and highly searchable digital repositories. While a technical writer creates document content and a records manager defines long-term legal retention schedules, the document management specialist focuses on the active system architecture, metadata taxonomies, workflow automation, and daily operational efficiency of the enterprise content management (ECM) environment. To ensure that digital information remains accessible, secure, and compliant, the specialist must understand how business workflows, metadata standards, access control models, system integrations, and user behaviors collectively determine the viability and security of the organization’s information ecosystem.

Typical responsibilities of a document management specialist include:

• DMS platform administration and system configuration;
• information architecture and metadata taxonomy design;
• workflow automation and process optimization;
• security, access control, and information governance enforcement;
• system integration, migration, and data quality management; and
• user training, support, and adoption strategy.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Document Management

Document management increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by document management specialists. They include information governance reasoning, taxonomy design, workflow logic formulation, security judgment, user empathy, change management, system architecture evaluation, and complex problem-solving.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In document management, these skills include optical character recognition (OCR) and natural language processing (NLP) for metadata extraction, automated document classification, API script generation, access log monitoring, bulk file migration execution, deduplication scanning, and training documentation drafting.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the nuanced business context required to design a logical metadata taxonomy for cross-departmental contracts relies primarily on human knowledge of organizational structure and retrieval behaviors; an AI agent may then use agentic skills to scan thousands of legacy filenames, extract common keywords, and propose an initial metadata schema. In system security, the specialist first determines the ethical and operational boundaries for confidential workspaces, and an AI agent subsequently audits permission matrices to detect unauthorized access drifts. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Software Developer as an Occupation

A software developer researches, designs, codes, tests, and maintains computer software and applications to solve specific user needs and business problems. This occupation connects computer science, mathematical analysis, and engineering principles by transforming abstract requirements and logical models into functional, reliable, and scalable executable programs. While systems analysts focus primarily on high-level business processes and organizational architecture, and computer programmers focus strictly on translating provided specifications into code, the software developer owns a broader segment of the software development lifecycle (SDLC). To ensure that software solutions are robust and maintainable, the developer must understand how user requirements, algorithmic complexity, system constraints, security boundaries, and performance expectations collectively determine the viability and quality of the final application.

Typical responsibilities of a software developer include:

• software requirements analysis and system design;
• software coding, development, and implementation;
• software testing, debugging, and quality assurance;
• software maintenance, optimization, and upgrades;
• system integration and API development; and
• technical documentation and development lifecycle collaboration.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Software Development

Software development increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by software developers. They include algorithmic reasoning, architectural design, complex problem-solving, code readability judgment, edge-case anticipation, technical debt evaluation, system integration logic, and mentorship.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In software development, these skills include code completion, boilerplate generation, syntax translation, static code analysis, unit test generation, refactoring suggestion, API schema formatting, and documentation drafting.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Formulating a novel algorithm to optimize a complex data-processing pipeline relies primarily on human knowledge of computational complexity and mathematical logic; an AI agent may then use agentic skills to generate the initial code scaffolding, suggest language-specific syntax optimizations, and write the corresponding unit tests. In system integration, the developer first determines the secure boundaries and data contracts between microservices, and an AI agent subsequently generates the client libraries and mocks the endpoints. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Web Developer as an Occupation

A web developer designs, builds, and maintains websites, web applications, and interactive web interfaces. This occupation connects user experience design, backend data management, and network infrastructure by transforming visual prototypes and business requirements into functional, browser-compatible, and secure web experiences. While web designers focus primarily on visual aesthetics and user interfaces, and general software developers may focus on desktop or embedded systems, the web developer specializes in the unique constraints and protocols of the web—such as HTTP, browser rendering engines, responsive design, and web accessibility standards. To ensure these applications perform reliably across diverse devices and network conditions, the developer must understand how frontend frameworks, server-side logic, database architectures, and hosting environments collectively determine the viability and quality of the final web product.

Typical responsibilities of a web developer include:

• web application architecture and technology strategy;
• front-end development and user interface implementation;
• back-end development and server-side logic;
• web performance optimization and scalability;
• web security, testing, and quality assurance; and
• web deployment, hosting, and infrastructure management.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Web Development

Web development increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by web developers. They include browser compatibility reasoning, user-centric design translation, web accessibility judgment, architectural planning, security threat modeling, performance intuition, and cross-disciplinary communication.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In web development, these skills include boilerplate code generation, automated accessibility auditing, cross-browser testing execution, dependency vulnerability scanning, CSS optimization, server log parsing, and API mocking.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the nuanced accessibility requirements for a complex interactive web dashboard relies primarily on human knowledge of web content accessibility guidelines and keyboard navigation flows; an AI agent may then use agentic skills to scan the codebase for missing ARIA labels and generate the necessary semantic HTML corrections. In back-end development, the developer first determines the appropriate database schema and API contracts, and an AI agent subsequently scaffolds the server-side routes and generates standard database queries. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.1. Bioinformatics Annotator as an Occupation

A bioinformatics annotator assigns biological meaning, functional classifications, and structured metadata to raw molecular data, genomic sequences, and biological literature. This occupation connects molecular biology, biochemistry, and data science by transforming uncharacterized sequences, structural models, and unstructured scientific texts into standardized, computable biological knowledge. To ensure that these annotations accurately reflect current scientific consensus and support reliable downstream computational research, the annotator must understand how biological nomenclature, standardized ontologies, experimental evidence codes, and curation guidelines collectively determine the validity and interoperability of biological databases.

Typical responsibilities of a bioinformatics annotator include:

• genomic and sequence feature annotation;
• functional and ontology annotation;
• variant classification and phenotypic annotation;
• literature curation and evidence synthesis;
• annotation quality assurance and conflict resolution; and
• training data generation and AI model evaluation.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, data, biological context, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Bioinformatics Annotation

Bioinformatics annotation increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by bioinformatics annotators. They include biological domain expertise, ontology reasoning, critical literature evaluation, evidence code interpretation, nomenclature governance, structural biology intuition, and scientific judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In bioinformatics annotation, these skills include natural language processing for literature mining, automated sequence alignment, ontology mapping, database querying, pattern recognition in genomic sequences, structural prediction execution, and draft annotation generation.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the precise biological function of a newly discovered protein relies primarily on human knowledge of metabolic pathways and experimental evidence strength; an AI agent may then use agentic skills to mine thousands of research abstracts, map protein domains to standardized ontology terms, and retrieve homologous sequences from public repositories. In variant classification, the annotator first determines the appropriate classification framework and weighs conflicting clinical or phenotypic evidence, while an AI agent subsequently aggregates population frequencies, predicts structural disruptions, and retrieves related case reports. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.