1. Working with Agentic AI: Insect Production Scientist

How human skills and agentic skills are dynamically integrated and coordinated across insect production responsibilities

1.1. Insect Production Scientist as an Occupation

An insect production scientist designs, manages, and optimizes mass-rearing systems to generate scalable biological outputs for agricultural, nutritional, and industrial applications. This occupation connects biological sciences with industrial engineering by transforming insect life cycles, nutritional requirements, and environmental constraints into efficient and sustainable production processes. To ensure these systems operate effectively, the scientist must understand how biological questions, environmental data, rearing methods, quality measures, and regulatory requirements collectively determine the viability and usefulness of the production output.

Typical responsibilities of an insect production scientist include:

• insect colony maintenance and breeding optimization;
• diet formulation and nutritional analysis;
• environmental control and habitat monitoring;
• health management and disease prevention;
• data collection, phenotyping, and genetic tracking;
• production scaling and process automation;
• quality assurance and regulatory compliance; and
• harvesting, processing, and downstream application research.

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 Insect Production

Insect production increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by insect production scientists. They include biological understanding, ecological reasoning, experimental design, nutritional science, problem formulation, critical thinking, contextual interpretation, welfare assessment, 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 insect production, these skills include environmental data analysis, predictive modeling, image recognition, anomaly detection, literature retrieval, formulation calculation, simulation generation, report generation, and automation logic support.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding a loosely defined biological anomaly relies primarily on human knowledge of insect physiology and ecological stressors; an AI agent then uses agentic skills to retrieve environmental logs and calculate correlations that support the scientist’s interpretation. In diet formulation, the scientist first determines appropriate nutritional targets, and an AI agent subsequently searches literature databases, calculates macronutrient balances, and proposes alternative feedstocks. For production scaling, an AI agent simulates mechanical handling parameters, while the scientist interprets the output to ensure it preserves biological integrity and animal welfare. 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.

1.3. Dynamic Human–Agentic Skill Integration and Collaboration in Insect Production

Insect production functions can be understood as an occupation where responsibilities are fulfilled through tasks and subtasks, tasks and subtasks are executed through actions, and actions require appropriate configurations of human and agentic skills.

An occupation provides the overall professional context. For insect production science, this context encompasses designing and managing mass-rearing systems, optimizing biological outputs, maintaining colony health, and supporting sustainable industrial applications.

A responsibility represents an expected area of professional work or an outcome that the insect production scientist accomplishes or maintains. Responsibilities such as breeding optimization, environmental monitoring, health management, and production scaling each contain multiple tasks.

A task is a context-dependent unit of work used to fulfill part or all of a responsibility. When a task is too broad or complex for reliable execution, it is decomposed into smaller subtasks.

Tasks and subtasks are executed through one or more actions. Actions represent reusable and configurable operations such as retrieving environmental data, analyzing sensor logs, calculating feed conversion ratios, comparing genetic lineages, identifying potential pathogen indicators, generating a simulation, validating a compliance report, or summarizing phenotypic findings.

Each action has skill requirements. These requirements describe the capabilities needed for effective and reliable execution. Depending on the action and its context, human skills, agentic skills, or an integration of both can satisfy these requirements.

The skills available for insect production reside in a skill space containing both human skills and agentic skills. Relevant skills in this space can serve as candidate skills for executing the actions required by a particular task. A human skill in biological interpretation, for example, can serve as a candidate for determining the significance of a morphological change, while an agentic skill in image recognition can serve as a candidate for related phenotyping actions.

Candidate skills are evaluated relative to the requirements of the action and its occupational and task context. Appropriate skills are then selected and organized into a skill configuration for execution. Depending on these requirements, a configuration may rely mainly on human skills, mainly on agentic skills, or on an integrated arrangement of both.

1.3.1. Skills Competition and Skills Collaboration

Two important relationships can occur between human skills and agentic skills when constructing a skill configuration: skills competition and skills collaboration.

Skills competition occurs when both a human skill and an agentic skill can satisfy the same or similar requirements for an action, but simultaneous application of both is unnecessary. Under a particular occupational and task context, one skill type can provide a better fit for the action than the other.

For example, both an insect production scientist and an AI agent can generate a standard summary of weekly colony growth metrics. When rearing protocols, environmental baselines, and biological definitions are clearly specified, the agentic skill provides fast and scalable execution. When the report requires interpretation of unusual molting behaviors, shifting genetic traits, or ambiguous environmental stressors, the scientist’s human skills provide the necessary contextual understanding.

The purpose of skills competition is not to establish whether human or agentic skills are generally superior. Rather, it identifies which available skill provides the better fit for a particular action under particular conditions.

Skills collaboration occurs when one skill type alone cannot effectively satisfy the requirements of a task or its actions, or when combining complementary human and agentic skills produces stronger task performance.

Collaboration does not require human and agentic skills to operate simultaneously. Instead, they are activated at different stages of action execution in a sequential, iterative, or recursive manner according to evolving task requirements.

For example, a scientist first identifies an unexpected decline in pupation rates and determines which aspects of the life cycle require investigation. An AI agent then retrieves and compares relevant environmental, dietary, and density data and identifies initial patterns associated with the decline. The scientist subsequently interprets these patterns within their biological context and formulates possible explanations. Based on the scientist’s direction, the agent next performs additional cohort analysis to examine those explanations. Finally, the scientist evaluates the resulting evidence and determines which findings are sufficiently supported and relevant to communicate to production managers. Throughout this sequence, human and agentic skills are activated iteratively as task requirements evolve, while the scientist directs the investigation and evaluates its outcomes.

This dynamic relationship allows insect production scientists to leverage the complementary strengths of both skill types rather than relying on a permanent allocation of work.

1.3.2. Skill Configuration and Task Execution

A skill configuration specifies which human skills and agentic skills are used to execute the actions associated with a task or subtask and how those skills are coordinated.

The configuration depends on factors such as action requirements, data availability, biological variability, environmental complexity, analytical complexity, expected reliability, available tools, information sensitivity, decision consequences, stakeholder expectations, and intermediate results.

For one action, an agentic skill may independently satisfy the requirements. For another, a human skill may provide the better fit. A complex task may require several configurations across multiple actions, with human and agentic skills repeatedly interacting as the task progresses.

The configuration is therefore dynamic rather than permanent. It may change when the action changes, new information becomes available, an agentic result fails to satisfy expected requirements, or human interpretation identifies a new direction for the analysis.

Throughout this process, the insect production scientist remains the professional responsible for directing and controlling the work associated with the occupation and its responsibilities. Agentic AI provides powerful complementary capabilities that expand what the professional can retrieve, simulate, calculate, compare, analyze, visualize, summarize, and communicate.

1.3.3. Task Performance Evaluation and Skill Space Adaptation

The performance produced through a selected skill configuration is evaluated against predefined evaluation metrics. These metrics determine whether the human skills, agentic skills, or their integrated configuration are sufficiently capable of performing the relevant task and its required actions.

Evaluation criteria depend on the work being performed. In insect production science, they include biological accuracy, environmental consistency, calculation correctness, data completeness, reporting timeliness, reproducibility, interpretability, relevance to rearing requirements, technical reliability, and other task-specific expectations.

When a selected skill or skill configuration produces performance above the defined threshold, it passes the performance evaluation and is retained for future applications under appropriate conditions. Repeated successful use provides additional evidence about where that skill or configuration performs effectively.

When performance falls below the required threshold, the corresponding skill or configuration is not reused without modification. The relevant human or agentic skills may be refined or reconfigured, existing skills can be combined differently, or new human and agentic skills may be developed or constructed from scratch when the existing skill space lacks adequate capability.

For example, an AI agent first generates environmental control reports with correct sensor readings but repeatedly produces inconsistent interpretations of micro-climate stress impacts on larval development. Task performance evaluation identifies this interpretation problem as falling below the required threshold. The agentic skill is then refined through clearer biological metric definitions, stronger contextual instructions, validation procedures, or access to validated species-specific biological data. The revised skill is evaluated again, and once it satisfies the required performance criteria, it is retained for appropriate future monitoring tasks. Human skills follow a corresponding development process: when a scientist lacks the knowledge required to interpret a newly introduced automated harvesting mechanism, the relevant capability is developed through learning, collaboration, and practical experience before its performance is evaluated through subsequent work.

Refined and newly constructed skills that satisfy the required performance criteria are added to the skill space. In this way, task performance evaluation provides feedback not only for completing current work but also for continuously optimizing and expanding the available skill space.

The skill space is not constructed as an isolated collection of occupation-specific representations. Many human and agentic skills used in insect production science are reusable across occupations. Information retrieval, data analysis, statistical reasoning, calculation, summarization, visualization, communication, technical comparison, documentation, and tool use contribute to work in agriculture, biotechnology, ecology, food science, operations management, and many other fields.

Reusable skills can therefore be represented once and configured differently according to the responsibilities, tasks, actions, and contexts of different occupations. This allows the skill space to serve a growing range of occupations without unnecessarily duplicating or exploding the number of skill representations. Occupational specialization is introduced through task context, action requirements, parameters, data sources, tools, constraints, and skill configurations rather than by creating a completely separate skill representation whenever the same underlying capability appears in another occupation.

The resulting process is adaptive: occupational responsibility → task and subtask → actions → skill requirements → candidate human and agentic skills → skill configuration → task execution → task performance evaluation → skill refinement, reconfiguration, or construction → optimized and expanded skill space

This cycle allows human and agentic capabilities to evolve alongside the requirements of insect production work.

1.4. Essential Responsibilities of Insect Production Scientist

1.4.1. Insect Colony Maintenance and Breeding Optimization

Insect colony maintenance and breeding optimization establishes and sustains healthy insect populations, tracks life cycles, optimizes mating structures, and maintains genetic vigor. This responsibility includes defining breeding objectives, selecting breeding stock, tracking generational lineage, examining morphological traits, and preparing biological assets for subsequent production phases.

Agentic skills contribute extensively to image recognition, predictive modeling, database management, statistical calculation, and repeated phenotyping actions. Human skills remain essential for determining which biological traits are relevant, understanding the meaning and limitations of genetic markers, resolving ambiguous heritability definitions, and deciding whether the resulting cohorts appropriately represent the intended breeding objective.

For example, a scientist first defines a breeding objective to improve feed conversion efficiency and determines which morphological and behavioral traits are relevant. An AI agent then processes thousands of images to measure larval growth distributions and tracks lineage data across multiple generations. The scientist reviews these observed trait and lineage patterns and evaluates whether they provide sufficient evidence for the proposed selections. Based on the scientist’s decisions, the agent updates the breeding matrix and performs required statistical checks. Finally, the scientist verifies that the selected breeding cohorts provide an appropriate foundation for the intended genetic improvement. This sequence combines human definition of biological requirements and validation with agentic data processing, integration, and verification.

1.4.2. Diet Formulation and Nutritional Analysis

Diet formulation and nutritional analysis transforms available feedstocks into optimized nutrition to support insect growth, development, and reproduction. This responsibility includes calculating macronutrient balances, comparing alternative ingredients, identifying nutritional deficiencies, examining feed conversion ratios, and investigating possible explanations for observed biological responses.

Agentic skills are well suited to literature retrieval, large-scale formulation calculations, nutritional and compositional pattern detection, optimization algorithms, and exploratory nutritional analysis. Human skills contribute to determining meaningful biological questions, selecting appropriate nutritional targets, recognizing misleading chemical correlations, interpreting results within their physiological context, and deciding which formulations deserve further biological trials.

For example, an AI agent first detects an unexpected variance in larval weight gain during routine feeding trials. The scientist reviews the result and formulates several possible explanations, including nutrient degradation, anti-nutritional factors, and changes in feed moisture content. The agent then decomposes the nutritional variance across these dimensions and identifies contributing patterns in the feed composition. The scientist evaluates their biological significance and identifies formulations requiring deeper examination. The agent performs the requested compositional comparisons, after which the scientist determines which findings provide the most credible explanation. Human and agentic skills interact iteratively as the analysis progresses from broad pattern detection to focused biological interpretation.

1.4.3. Environmental Control and Habitat Monitoring

Environmental control and habitat monitoring transforms raw sensor data into understandable information for facility managers, operational teams, and other stakeholders. This responsibility includes generating standard and custom climate reports, organizing environmental results, creating visualizations of micro-climate fluctuations, explaining stress measures, and ensuring that relevant habitat intelligence reaches users when needed.

Agentic skills support sensor data analysis, anomaly detection, predictive modeling, formatting, recurring information updates, and adaptation of environmental content for different operational audiences. Human skills are essential for determining what stakeholders need to understand about insect stress, deciding which environmental deviations deserve emphasis, ensuring that explanations preserve biological meaning, and communicating uncertainty or sensor limitations appropriately.

For example, an AI agent first generates a daily environmental report containing key climate measures, micro-variance comparisons, and summaries of major HVAC fluctuations. The scientist reviews the report and identifies that a large increase in localized humidity resulted from a temporary biological respiration spike rather than a mechanical failure. The scientist then provides missing context and specifies how the interpretation should be revised. The agent regenerates the explanation and supporting visualization according to this direction. Finally, the scientist verifies the revised report for biological and contextual accuracy before distribution to the facility management team.

1.4.4. Health Management and Disease Prevention

Health management and disease prevention creates and maintains the biosecurity protocols and diagnostic frameworks through which users monitor and protect colony health. This responsibility includes gathering health observation requirements, defining mortality thresholds, designing biosecurity workflows, developing diagnostic queries, configuring automated visual inspections, and adapting protocols as biological threats change.

Agentic skills assist with literature retrieval, diagnostic logic generation, visual pattern recognition, system configuration, documentation, and testing. Human skills are especially important for understanding how pathogens spread, defining meaningful health indicators, structuring biosecurity appropriately, and ensuring that the resulting protocols reflect actual biological vulnerabilities.

For example, a facility first introduces a revised organic feedstock that affects multiple rearing chambers. The scientist defines the scope of the health observation and directs an AI agent to identify affected biological assets. The agent traces mortality rates, feeding behaviors, and environmental logs referencing the new feedstock. The scientist reviews identified dependencies, determines which health indicators require modification, and establishes the investigation sequence. The agent then prepares proposed diagnostic tests and validation checks. Finally, the scientist reviews results and controls implementation of approved biosecurity changes.

1.4.5. Data Collection, Phenotyping, and Genetic Tracking

Data collection, phenotyping, and genetic tracking maintains the biological and informational environment required for reliable selective breeding. This responsibility includes maintaining genetic databases, updating tracking systems, managing reusable biological assets, organizing metadata, documenting procedures, and supporting the reliable flow of phenotypic information to researchers.

Agentic skills assist with metadata extraction, documentation, image-based phenotyping, consistency checks, dependency inspection, information organization, and repeated tracking actions. Human skills remain important for understanding genetic dependencies, defining information governance requirements, resolving conflicting trait definitions, controlling consequential breeding changes, and ensuring that technical structures align with biological needs.

For example, a scientist first defines an investigation into whether a recent shift in larval coloration represents a meaningful genetic drift or an environmental response. An AI agent then gathers authorized imaging data and organizes changes in pigmentation, growth rates, and available genetic markers. The scientist evaluates initial evidence against the colony’s historical baseline and identifies areas requiring deeper investigation. The agent performs requested genomic comparisons and returns additional evidence. Finally, the scientist synthesizes results, evaluates their biological significance, and prepares an assessment for the breeding team.

1.4.6. Production Scaling and Process Automation

Production scaling and process automation examines internal and external information to identify developments affecting facility strategy, mechanical handling, and organizational performance. This responsibility includes analyzing biological limits, monitoring mechanical stress, examining automation strategies, identifying scaling bottlenecks, and evaluating technology developments.

Agentic skills retrieve and organize large volumes of engineering data, simulate bulk handling dynamics, generate automation code, optimize logistics, summarize developments, and detect changes across multiple mechanical systems. Human skills evaluate welfare relevance, distinguish meaningful biological constraints from superficial engineering limits, interpret developments in relation to facility strategy, and determine which findings warrant mechanical modification.

For example, a production manager first requests an automated harvesting system for a newly scaled rearing facility. The scientist clarifies the biological limits the system must respect and defines relevant stress metrics and handling speeds. An AI agent then generates candidate mechanical parameters, simulation logic, and automation sequences. The scientist reviews the proposed system and identifies an ambiguous definition of acceptable mechanical impact on pupae. After the scientist revises the requirement, the agent updates calculations and simulation configurations and executes relevant tests. Finally, the scientist evaluates whether the revised solution accurately represents approved biological limits and supports intended production scaling.

1.4.7. Quality Assurance and Regulatory Compliance

Quality assurance and regulatory compliance ensures that insect biomass, derived products, and production tools operate reliably and remain consistent with defined legal and safety requirements. This responsibility includes testing biological outputs, validating compliance calculations, investigating discrepancies, supporting auditors, documenting issues, and maintaining existing certification solutions.

Agentic skills generate compliance tests, compare expected and actual regulatory results, inspect documentation logic, identify anomalies, analyze technical information, and prepare troubleshooting summaries. Human skills define acceptable legal behavior, interpret ambiguous regulatory failures, understand market requirements, prioritize compliance issues, and determine appropriate corrective actions.

For example, auditors first report that two batch certificates show different values for what appears to be the same heavy metal threshold. The scientist confirms the discrepancy and directs an AI agent to trace relevant testing data and compliance logic. The agent examines laboratory inputs and regulatory definitions and identifies that the batches apply different regional safety frameworks. The scientist determines which definition matches approved market requirements and specifies the required correction. The agent then tests the corrected compliance logic across relevant historical batches. Finally, the scientist verifies results and controls the update to the affected certification documentation.

1.4.8. Harvesting, Processing, and Downstream Application Research

Harvesting, processing, and downstream application research converts biological evidence into information that supports market decisions and product recommendations. This responsibility involves combining findings from different processing trials, identifying the most important functional implications, explaining uncertainty, comparing possible interpretations, and communicating evidence relevant to potential commercial actions.

Agentic skills summarize multiple processing outputs, compare thermal scenarios, organize supporting chemical evidence, identify recurring functional patterns, and generate alternative applications. Human skills evaluate evidence strength, incorporate market context, understand practical consequences, resolve conflicting commercial objectives, and determine which conclusions or recommendations are sufficiently justified.

For example, an organization first considers whether to expand insect protein extraction into a new nutritional market. The scientist defines the decision context, relevant functional requirements, and major processing assumptions requiring examination. An AI agent then organizes relevant thermal inactivation curves, protein solubility indicators, historical yield performance, and scenario comparisons. The scientist reviews the evidence and identifies assumptions that substantially affect the initial conclusion regarding protein functionality. The agent performs additional sensitivity comparisons under revised processing parameters. Finally, the scientist synthesizes resulting evidence and communicates opportunities, limitations, uncertainties, and implications relevant to the market expansion decision.

1.5. Changing Human and Agentic Involvement Across Insect Production Responsibilities

Human skills and agentic skills are involved throughout insect production science, but their relative involvement differs across responsibilities and changes during task execution.

These involvement levels are not permanent allocations. A single responsibility may contain actions where human and agentic skills compete as alternative capabilities and other actions where they collaborate sequentially, iteratively, or recursively.

The practical objective is to construct skill configurations that use the most suitable available capabilities for each part of the work and revise those configurations when task performance evaluation shows that another arrangement performs more effectively or reliably.

1.6. Working as an Insect Production Scientist with Agentic AI

Agentic AI is becoming an increasingly powerful component of insect production science. Insect production scientists should therefore adopt and integrate AI agents as complementary participants in professional workflows rather than treating them only as occasional tools for generating summaries, simulations, or compliance reports.

This shift does not reduce the importance of entomological and biological expertise. Human professionals remain responsible for the occupation and its corresponding responsibilities. They understand biological objectives, define meaningful rearing questions, interpret ecological context, determine appropriate welfare measures, evaluate analytical evidence, communicate with stakeholders, control consequential decisions, evaluate task performance, and decide how human and agentic skills are configured and improved.

At the same time, agentic skills greatly expand the volume and variety of biological and environmental data that can be processed. AI agents retrieve and organize large volumes of sensor data, generate and execute simulation models, perform repeated chemical comparisons, identify patterns across many phenotypic dimensions, prepare reporting alternatives, test production outputs, and synthesize extensive analytical evidence at a scale difficult to achieve through direct human effort alone.

Strong insect production practice is therefore not based on maximizing human work or agentic work independently. It is based on finding appropriate configurations of human skills and agentic skills for the responsibilities, tasks, subtasks, and actions being performed.

Where a human skill and an agentic skill can independently perform the same action, skills competition identifies which is better suited to the current context. Where work requires complementary capabilities, skills collaboration results in human and agentic skills being activated sequentially, iteratively, or recursively to achieve stronger task performance than either skill type achieves alone.

Task performance evaluation then determines whether selected skills and configurations satisfy predefined expectations. Successful skills are retained and reused, while inadequate skills are refined, reconfigured, or newly developed. The resulting capabilities return to an increasingly useful skill space that supports insect production science and, where capabilities are reusable, other occupations as well.

For an insect production scientist, working effectively with agentic AI means treating AI as a powerful complementary capability integrated into everyday professional routines while maintaining human control of occupational responsibilities. Through appropriate skill selection, competition, collaboration, evaluation, and continuous improvement, human professionals and agentic AI work together to achieve insect production that is more efficient, scalable, adaptable, timely, and biologically sound.