1.1. Strategic Business Analyst as an Occupation

A strategic business analyst is a professional who analyzes organizational needs, business performance, processes, capabilities, stakeholder requirements, and potential solutions to support strategic decisions and organizational change. The occupation connects business strategy with operational analysis, data analysis, process improvement, solution development, technology-enabled change, and stakeholder decision-making.

Strategic business analysis extends beyond documenting requirements for individual systems or projects. A strategic business analyst must understand how organizational objectives, business capabilities, operational processes, stakeholder needs, information, technology, costs, risks, and expected benefits interact. The analyst helps transform broad problems and opportunities into structured business needs, alternative solutions, actionable requirements, and evidence-supported recommendations.

Typical responsibilities of a strategic business analyst include:

• strategic needs, opportunities, and current-state analysis;
• stakeholder engagement and requirements analysis;
• business process analysis, architecture, and improvement;
• business data, performance, and decision-support analysis;
• business case development and solution evaluation;
• solution validation, implementation readiness, and change support; and
• executive communication, documentation, and strategic advisory support.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to the strategic importance of the issue, task ambiguity, stakeholder diversity, information quality, analytical complexity, organizational consequences, and expected outcome.

1.2. Human Skills and Agentic Skills in Strategic Business Analysis

Strategic business analysis increasingly involves both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by strategic business analysts. They may include strategic reasoning, business analysis, systems thinking, stakeholder engagement, requirements elicitation, process analysis, financial reasoning, problem solving, facilitation, communication, organizational understanding, negotiation, contextual interpretation, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In strategic business analysis, these skills may include information retrieval, document extraction, classification, summarization, comparison, data analysis, visualization generation, process-model generation, requirements drafting, gap analysis, scenario analysis, business-case support, consistency checking, report generation, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. Strategic problem definition may depend heavily on human interpretation of organizational objectives and competing stakeholder interests while using agentic skills for research and evidence synthesis. Business process analysis may involve substantial agentic support for documentation and comparison while relying on human skills to understand why processes operate as they do. Business-case development may combine agentic financial and scenario analysis with human evaluation of strategic value, organizational feasibility, and risk.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.

1.1. Machine Learning Engineer as an Occupation

A machine learning engineer is a professional who designs, develops, evaluates, deploys, maintains, and improves machine learning systems that use data and computational models to produce predictions, classifications, recommendations, generated outputs, or other results. The occupation connects machine learning methods with software engineering, data systems, computing infrastructure, and production environments.

Machine learning engineering extends beyond developing or training models. A machine learning engineer must understand how data, model architecture, training procedures, evaluation methods, software components, deployment environments, production inputs, and operational constraints affect one another throughout the machine learning lifecycle.

Typical responsibilities of a machine learning engineer include:

• machine learning problem formulation and solution design;
• data preparation and feature engineering;
• model development, training, and experimentation;
• model evaluation, validation, and performance optimization;
• model deployment, serving, and system integration;
• machine learning pipelines, automation, and lifecycle engineering;
• production monitoring, troubleshooting, and continuous model improvement; and
• machine learning software engineering, technical coordination, and knowledge development.

These responsibilities are fulfilled through different tasks and subtasks, and each task may require multiple actions with different skill requirements. Human skills and agentic skills can both contribute to performing these responsibilities, but their levels and forms of involvement may differ according to the characteristics of the responsibility, task, action, data, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Machine Learning Engineering

Machine learning engineering increasingly involves both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by machine learning engineers. They may include machine learning knowledge, statistical reasoning, software engineering, data reasoning, experimental design, system understanding, problem formulation, communication, contextual interpretation, creativity, and professional judgment.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In machine learning engineering, these skills may include information retrieval, dataset inspection, extraction, classification, summarization, comparison, calculation, code generation, test generation, experiment execution, metric analysis, documentation support, and tool use.

Both skill types can contribute to the same occupational responsibilities, but their involvement levels vary. Problem formulation may depend heavily on human understanding of objectives and context while using agentic skills for information retrieval and technical comparison. Model experimentation may involve high levels of both human and agentic skills, with the engineer forming hypotheses and the AI agent implementing and executing repeated experiments. Production monitoring may rely heavily on agentic analysis of large volumes of system information while requiring human judgment to determine the significance of detected changes.

The useful relationship is therefore not a fixed division in which certain responsibilities permanently belong to humans and others belong to AI. Human and agentic involvement can change from one task to another and even between different actions within the same task.

1.1. Generative AI Software Engineering as Professional Work

A generative AI software engineer designs, develops, evaluates, deploys, maintains, and improves software systems that use generative AI models to produce, transform, retrieve, reason over, or interact with information. The work can include building applications around large language models and other generative models, developing retrieval-augmented generation systems, constructing agentic workflows, designing prompts and model interactions, integrating external data and tools, implementing application logic, evaluating generated outputs, and operating generative AI capabilities in production. Generative AI software engineers also build the software infrastructure surrounding models, connect AI components with applications and services, establish testing and observability, investigate failures, optimize quality, latency, reliability, and computational cost, and adapt systems as models and application requirements change. Because application behavior emerges from interactions among models, instructions, retrieved context, tools, software components, data, and users, effective generative AI engineering requires understanding the complete system rather than treating the underlying model as an isolated component. The work consequently combines software engineering, AI and machine learning knowledge, experimentation, systems thinking, data reasoning, evaluation, problem solving, and technical judgment to create generative AI applications that are useful, reliable, scalable, maintainable, and appropriate for their intended purposes.

1.2. Human Skills, Agentic Skills, and Their Integration

Human skills are particularly important when generative AI software engineering requires problem formulation, architecture, interpretation of application requirements, experimental judgment, evaluation of open-ended behavior, or decisions about acceptable system performance and risk. Software engineering knowledge, understanding of generative models, systems thinking, data reasoning, analytical problem solving, and technical judgment allow an engineer to translate practical needs into application designs, select suitable model and system approaches, interpret unexpected outputs, identify weaknesses in retrieval or orchestration, and balance quality with latency, reliability, maintainability, security, and cost. These capabilities become especially important because generative AI behavior is probabilistic and context-dependent: an output can appear plausible while being incomplete, unsupported, inappropriate, or inconsistent with the application’s intended behavior.

At the same time, agentic skills can participate extensively in the engineering process itself. Agentic AI can inspect requirements, code, documentation, traces, datasets, prompts, evaluation results, and system configurations; generate or revise software; construct tests; explore candidate implementations; execute defined experiments through authorized tools; compare model outputs; analyze failure patterns; and assist with debugging, deployment, and monitoring. An agent may also use development environments, repositories, APIs, databases, retrieval systems, testing frameworks, and infrastructure tools when appropriately authorized. This ability to reason over technical context and act through tools can accelerate iterative software development and expand the number of alternatives, tests, and diagnostic paths that can be explored.

Generative AI software engineering can therefore involve a particularly close integration of human skills and agentic skills across responsibilities, tasks, and subtasks. An engineer might define an application’s intended behavior and architectural constraints while Agentic AI develops a candidate implementation and evaluation suite. Test results may reveal a retrieval problem that requires human reinterpretation of the system design, after which an agent modifies the pipeline and performs another evaluation cycle. In another workflow, agentic analysis may identify an unexpected interaction among prompts, context, tools, and application logic that changes what needs to be investigated. Human and agentic contributions can consequently shift as requirements, evidence, system behavior, and operational conditions develop.

1.1. Python Software Engineering as Professional Work

A Python software engineer designs, develops, tests, integrates, deploys, maintains, and improves software systems in which Python serves as a principal implementation language. The work may involve backend services, APIs, web applications, data-processing components, automation systems, internal platforms, distributed services, cloud-native applications, developer tools, and integrations with databases, external services, infrastructure, or other software systems. Responsibilities commonly extend across software requirements, architecture, application logic, data access, interface design, testing, debugging, performance, security, deployment, observability, and continuing maintenance. Python software engineers may also develop reusable libraries and frameworks, automate operational or development processes, participate in CI/CD workflows, investigate production problems, and collaborate with frontend, platform, infrastructure, data, security, product, and other engineering specialists. Because reliable software depends not only on individual functions but also on interactions among components, dependencies, data, external services, execution environments, and users, effective Python software engineering combines programming expertise with software architecture, systems thinking, analytical problem solving, testing discipline, communication, and technical judgment.

1.2. Human Skills, Agentic Skills, and Their Integration

Human skills are particularly important where Python software engineering requires interpretation of requirements, architectural reasoning, abstraction, problem decomposition, debugging of ambiguous behavior, or evaluation of technical trade-offs. Knowledge of Python, algorithms, data structures, software design, databases, networking, APIs, testing, distributed systems, and development practices enables a Python software engineer to translate requirements into software structures, choose appropriate abstractions, identify dependencies, reason across system boundaries, and determine whether an implementation remains maintainable as the system evolves. Human judgment becomes especially valuable when requirements conflict, technical evidence is incomplete, several implementation approaches are viable, or a change that improves one characteristic—such as performance—may reduce readability, reliability, security, or maintainability elsewhere.

For many implementation and analysis activities, agentic skills can contribute extensively. Agentic AI can inspect source code, specifications, tests, logs, dependency information, API documentation, database schemas, configuration files, and technical documentation; generate or revise Python code; create tests; identify likely defects; refactor implementations; compare alternative approaches; execute development commands through authorized tools; and analyze the results. It can also support repository navigation, dependency analysis, documentation, debugging, code review, deployment preparation, and investigation of production evidence. These capabilities are particularly valuable when engineering work involves large codebases, repeated transformations, broad information retrieval, systematic testing, or numerous implementation alternatives.

Rather than assigning entire software responsibilities permanently to either the engineer or Agentic AI, human skills and agentic skills can be dynamically combined across responsibilities, tasks, and subtasks. An engineer might establish an architectural boundary and define expected behavior while Agentic AI develops an implementation and corresponding tests. A failing integration test may reveal an assumption that requires human reconsideration of the interface design before the agent modifies the code. In another workflow, agentic analysis of logs and source code may identify several plausible causes of a production failure, allowing the engineer to determine which hypothesis best fits the operating context. Human and agentic contributions can consequently shift repeatedly as requirements, code, tests, execution results, and production conditions provide new information.

1.1. Robotics Motion Planning Engineering as Professional Work

A robotics motion planning engineer designs, develops, integrates, evaluates, and improves the algorithms and software that determine how robots move from one state or location to another while satisfying geometric, kinematic, dynamic, operational, and safety constraints. The work spans path planning, trajectory generation and optimization, forward and inverse kinematics, collision checking and avoidance, planning-scene construction, motion constraints, robot-state representation, controls integration, perception-informed planning, simulation, testing, debugging, and deployment on physical robotic systems. Motion planning engineers may work with robotic manipulators, mobile robots, autonomous vehicles, multi-robot systems, or other robotic platforms whose movement must remain feasible, smooth, efficient, responsive, and safe in changing environments. Because planned motion must ultimately be executed by physical hardware, the role also requires understanding actuator limitations, joint limits, velocity and acceleration constraints, sensor information, timing requirements, control behavior, and the interaction between planning algorithms and the surrounding robotics software stack. Effective robotics motion planning therefore combines robotics theory, algorithmic reasoning, software engineering, mathematics, optimization, systems thinking, experimentation, and hardware-aware technical judgment to transform desired robot behavior into executable motion.

1.2. Human Skills, Agentic Skills, and Their Integration

Human skills are particularly important when motion planning requires formulation of the planning problem, interpretation of physical constraints, algorithm selection, architectural reasoning, debugging of ambiguous behavior, or evaluation of trade-offs among safety, feasibility, smoothness, speed, robustness, and computational performance. Knowledge of robotics, kinematics, dynamics, optimization, algorithms, control systems, and software engineering allows a robotics motion planning engineer to determine how a planning problem should be represented, choose appropriate planning strategies, interpret planner failures, recognize unrealistic assumptions, and understand how planning decisions affect the physical robot. Human judgment becomes especially important when environments are partially known, sensor information is uncertain, multiple feasible trajectories exist, optimization objectives conflict, or technically valid motion produces undesirable physical or operational behavior.

Agentic skills can contribute substantially because motion planning development involves repeated experimentation, numerical analysis, code generation, simulation, parameter tuning, log inspection, and comparison of many candidate behaviors. Agentic AI can examine robot models, planning configurations, source code, trajectories, collision information, simulation results, sensor-derived environment representations, telemetry, and technical documentation; generate or revise planning and testing code; compare algorithms and parameter settings; analyze failed plans; and execute defined experiments through authorized development and simulation tools. It can also help construct test scenarios, inspect execution traces, identify recurring failure conditions, and maintain context across iterative debugging and optimization workflows.

Robotics motion planning engineering can consequently involve a dynamic integration of human skills and agentic skills across responsibilities, tasks, and subtasks. An engineer may define the desired robot behavior and physical constraints while Agentic AI generates planner configurations and evaluates candidate approaches in simulation. Unexpected collision behavior or infeasible trajectories may then require human reinterpretation of the robot model, planning assumptions, or constraints before another agentic testing cycle begins. In other cases, an agent may identify a pattern across failed planning runs that directs human attention toward a kinematic singularity, perception error, or control limitation. The balance between human and agentic capabilities can therefore change repeatedly as the robot, environment, planner, and intermediate results reveal new information.

1.1. Annuity and Securities Operations as Professional Work

An annuity and securities operations specialist supports the accurate, timely, controlled, and compliant processing of operational activities associated with annuity products, securities, investment accounts, regulatory communications, and related financial-service workflows. The role can include transaction and account servicing, regulatory reporting and shareholder communications, document and data processing, reconciliation, exception resolution, operational controls, quality review, vendor coordination, compliance support, workflow documentation, and recurring reporting-cycle management. The specialist often works across operations, compliance, legal, product, technology, financial professionals, service providers, and other stakeholders to ensure that operational requirements are completed correctly and that exceptions are identified and resolved before they create larger service, financial, regulatory, or operational problems.

1.2. Working with Agentic AI

Agentic AI can participate across annuity and securities operations by applying language comprehension, reasoning, classification, information extraction, comparison, generation, evaluation, and workflow-execution capabilities to operational procedures, transaction records, account information, regulatory materials, reporting schedules, control documentation, communications, and other authorized sources. An AI agent can help classify operational requests, extract required information from documents, compare records across systems, identify missing or inconsistent data, track regulatory communication activities, prepare preliminary reports, monitor workflow deadlines, reconcile structured information, detect exceptions, summarize unresolved cases, prepare stakeholder communications, and organize evidence for review or audit. These activities can be performed as interconnected workflows in which the agent retrieves information, applies relevant operational rules, identifies exceptions, generates intermediate outputs, and adjusts its next action when new documents, transaction results, regulatory requirements, or human decisions become available. The annuity and securities operations specialist remains important for interpreting unusual cases, evaluating operational and client consequences, applying product and regulatory knowledge, resolving ambiguous situations, coordinating stakeholders, and validating actions where accuracy, compliance, risk, or authority requires professional judgment.

1.1. business analysis as Professional Work

A business analyst works at the intersection of business needs, organizational processes, information, technology, stakeholders, and solution development. The role involves understanding what an organization or stakeholder is trying to accomplish, identifying business problems and opportunities, eliciting and analyzing requirements, examining existing processes and systems, identifying gaps between current and desired conditions, evaluating possible solutions, documenting requirements and process models, analyzing data and information flows, supporting solution design and implementation, facilitating communication among business and technical participants, and evaluating whether implemented changes satisfy intended objectives. business analysts may also contribute to business cases, risk and impact assessments, testing, change management, process improvement, technology transformation, data governance, and other initiatives in which business needs must be translated into actionable requirements and coordinated organizational changes. Their responsibilities therefore extend beyond documenting requirements: they continuously translate information among business objectives, stakeholder needs, operational processes, data, technology capabilities, organizational constraints, risks, costs, and proposed solutions while applying professional knowledge and judgment to determine what information matters, what problems should be addressed, what changes are feasible, and how organizational value can be achieved.

1.2. Working with Agentic AI

Agentic AI can participate in many parts of business analysis by combining the capabilities of increasingly advanced large language models and other foundation models with task instructions, contextual information, external knowledge sources, tools, software systems, and reusable or dynamically constructed agentic skills. Large models can provide broad parametric knowledge together with capabilities such as language comprehension, reasoning, comparison, generation, coding, evaluation, classification, and multimodal interpretation. When these capabilities are organized into goal-directed agentic workflows, an AI agent can perform more than an isolated response to a prompt: it can interpret a task, decompose it into subtasks, retrieve relevant information, select appropriate skills or tools, perform intermediate operations, evaluate partial results, respond to changing conditions, and continue working toward a defined objective.

Within business analysis, these capabilities can be applied to activities such as processing stakeholder information, extracting and organizing requirements, analyzing business processes, examining data and process flows, comparing current and future states, identifying gaps and dependencies, generating preliminary process models, evaluating solution alternatives, preparing business cases, analyzing risks and impacts, maintaining requirements traceability, supporting testing, preparing stakeholder communications, and consolidating information across organizational functions. Their practical value does not depend on transferring an entire professional responsibility to AI. Instead, agentic skills can participate selectively at the level of individual tasks and subtasks, allowing work to be distributed according to the capabilities, information, reliability, authority, and judgment required at a particular moment.

1.1. Cyber Threat Hunting as Professional Work

A cyber threat hunter proactively searches systems, networks, endpoints, cloud environments, identities, applications, security telemetry, and other authorized data sources for evidence of malicious activity that has not been adequately identified by existing automated security controls. Rather than depending entirely on predefined alerts, the role involves developing hunt hypotheses, investigating anomalous behavior, analyzing security telemetry, examining adversary tactics, techniques, and procedures, correlating evidence across multiple sources, identifying indicators of compromise or suspicious behavior, investigating possible attacker persistence and lateral movement, distinguishing malicious activity from legitimate behavior, and determining whether suspicious findings warrant escalation. Cyber threat hunters also contribute to threat intelligence application, detection engineering, incident investigation, security-control improvement, threat-hunting methodology, documentation, reporting, and collaboration with security operations, incident response, digital forensics, vulnerability management, security engineering, and other cybersecurity functions. Their work therefore combines proactive investigation, adversary understanding, technical analysis, contextual reasoning, and continuous improvement of defensive capabilities.

1.2. Working with Agentic AI

Agentic AI can participate across cyber threat hunting by applying language comprehension, reasoning, classification, information retrieval, coding, pattern analysis, comparison, generation, evaluation, and workflow-execution capabilities to security telemetry, endpoint events, network records, authentication activity, cloud logs, threat intelligence, detection rules, incident histories, vulnerability information, system inventories, and other authorized cybersecurity sources. An AI agent can help collect and normalize hunt-relevant information, generate preliminary hunt hypotheses, translate hypotheses into search strategies, construct or refine queries, correlate events across multiple data sources, identify unusual behavioral patterns, compare observed activity with known adversary techniques, retrieve relevant threat intelligence, summarize suspicious activity, organize investigation evidence, generate preliminary timelines, identify information gaps, prepare hunt reports, and convert validated findings into candidate detection improvements. These activities can operate as interconnected workflows in which the agent retrieves evidence, executes authorized analytical tools, evaluates intermediate findings, revises queries, expands or narrows investigative scope, and adjusts subsequent actions when new telemetry, intelligence, analyst judgments, or investigation results become available. The cyber threat hunter remains important for developing meaningful investigative hypotheses, understanding environmental context, distinguishing legitimate but unusual activity from adversary behavior, evaluating uncertain evidence, recognizing novel attack patterns, directing deeper investigation, determining the significance of findings, and making or escalating consequential cybersecurity decisions.

1.1. Data Engineering as Professional Work

A data engineer designs, builds, integrates, operates, and improves the technical systems through which data is collected, transported, transformed, stored, organized, governed, and made available for analytical, operational, reporting, machine-learning, and other computational uses. The role involves understanding data requirements, examining source systems and data structures, designing data architectures and models, developing batch and streaming pipelines, implementing data transformations, integrating heterogeneous data sources, managing databases and data platforms, establishing data-quality controls, optimizing data-processing performance, monitoring production data workflows, troubleshooting failures, maintaining metadata and documentation, and supporting secure and reliable access to data. data engineers also collaborate with data analysts, data scientists, machine-learning engineers, software engineers, platform engineers, business stakeholders, governance specialists, security teams, and other participants who produce, manage, or consume data. Their responsibilities therefore extend beyond moving data between systems: they continuously connect source data, transformation logic, storage technologies, schemas, pipelines, infrastructure, quality requirements, downstream consumers, security constraints, operational conditions, and changing organizational needs while applying engineering knowledge and judgment to ensure that data remains accurate, available, scalable, traceable, maintainable, and appropriate for its intended uses.

1.2. Working with Agentic AI

Agentic AI can participate across data engineering activities by applying the comprehension, reasoning, comparison, generation, coding, evaluation, classification, and tool-use capabilities of increasingly advanced large language models and other foundation models together with data context, engineering instructions, schemas, metadata, source-system information, technical documentation, data-platform tools, code repositories, monitoring systems, and reusable or dynamically constructed agentic skills. Within a data lifecycle, an AI agent can analyze data requirements, inspect schemas and metadata, discover relationships among data sources, generate preliminary data models, develop or modify SQL and data-processing code, construct transformation logic, assist with pipeline development, generate data-quality checks, compare schemas, identify anomalous data conditions, analyze pipeline logs and failures, inspect dependencies and lineage, prepare tests and documentation, support query and workload optimization, and consolidate information distributed across data platforms and engineering systems. These activities can also be organized into goal-directed workflows in which the agent decomposes a data engineering task into subtasks, retrieves relevant technical context, selects appropriate tools, generates or executes authorized code, evaluates intermediate outputs, detects failures or inconsistencies, and modifies subsequent actions as schemas, source systems, data volumes, quality conditions, pipeline results, or downstream requirements change. Their practical value does not depend on transferring an entire data engineering responsibility to AI. Instead, agentic skills can participate selectively across individual tasks and subtasks, while the data engineer provides architectural judgment, data-context interpretation, production awareness, technical validation, risk evaluation, and engineering decision-making where reliability, scalability, security, maintainability, or downstream consequences require them.