# Human-in-Command Governance for Multi-Agent Scientific Workflows

Samed Bayer, Ingo Weber

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# Human-in-Command Governance for Multi-Agent Scientific Workflows

Samed Bayer¹,², Ingo Weber¹,²

¹Technical University of Munich, School of Computation, Information and Technology (CIT)

²Fraunhofer Society, Munich, Germany

samed.bayer@tum.de, ingo.weber@tum.de

## Abstract

While AI can accelerate text-intensive research, unsupervised autonomous agents risk compounding errors and cannot be held accountable for the integrity of their findings. Accordingly, we propose a human-supervised, multi-agent model for Human-AI collaboration in science that reconciles the power of Large Language Models (LLMs) with the core principles of transparency and accountability. In our vision, a human researcher supervises a team of specialized AI agents that execute discrete tasks within a rigorously defined scientific methodology. Each step in the process is governed by human expert validation, in order to prevent error propagation and maintain conceptual integrity.

We instantiate this model in an end-to-end framework for systematic taxonomy generation by operationalizing the Nickerson et al. (2013) methodology. We provide a proof-of-concept (PoC) implementation and a qualitative case study that validates our framework by re-deriving a recently published taxonomy. Our analysis documents the framework's fidelity to the original method and illustrates how human-gated oversight is crucial for ensuring the scientific rigor of the final taxonomy. To support reproducibility, we release our prompts, agent roles, and workflow design as a generalizable blueprint for structuring human-AI teams. This work argues that method-bounded human-in-command (HIC) supervision offers a principled and practical path to developing reliable and accountable AI research assistants.

## Introduction

The past decade has witnessed an unprecedented surge in scientific output across nearly every discipline (Bornmann and Mutz 2015). This flood of publications creates a bottleneck for text-intensive research activities such as systematic reviews, evidence mapping, and taxonomy development. In particular, taxonomies are foundational instruments for organizing knowledge, structuring research agendas, and enabling cumulative science. However, developing taxonomies rigorously and transparently remains time-consuming and cognitively demanding. This challenge of scalable scientific knowledge structuring motivates our work.

Large language models (LLMs) and multi-agent AI systems offer the promise of scale. They can rapidly read, cluster, and propose concepts across thousands of documents,

potentially accelerating knowledge elicitation (Lieberum et al. 2025). However, unsupervised autonomy is ill-suited for scientific practice and prone to epistemic drift (Zhang et al. 2024). Without formal controls, autonomous pipelines may propagate early mistakes, deviate from the guiding research question, or generate outputs that lack auditability and accountability. These risks are amplified for knowledge-structuring tasks, where subtle conceptual errors can mislead downstream research and compromise scientific integrity. They also resonate with broader concerns about automation bias and the need for meaningful human oversight in high-risk AI systems (e.g., discussions around the EU AI Act) (Laux and Rus schemeier 2025).

AI governance work has articulated different modes of human oversight: Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human-in-Command (HIC) (Kandikatla and Radeljić 2025). It also emphasizes the need for human control in high-risk AI systems, including in the context of the EU AI Act (Zicari et al. 2021; Laux and Rus schemeier 2025). However, these discussions largely remain at the level of organizational policy or system-level design. They provide little guidance on how such oversight should be implemented and *bound* to the procedural steps of scientific workflows, particularly in complex, multi-agent LLM systems. As a result, human involvement often collapses into a final output check rather than process-level governance of scientific knowledge production.

We argue that the future of AI in science is neither manual effort nor fully autonomous AI agents, but a middle ground. To this end, we propose **Method-Fidelity Human-in-Command Governance (HIC-GOV)**, a form of HIC governance in which human oversight is structurally tied to each step of a recognized scientific methodology. Human experts do not merely correct AI outputs; instead, they supervise specialized AI agents and approve or reject their work products at method-defined checkpoints. Each agent operates within a narrow scope aligned with a specific step of the methodology, and no step proceeds without explicit human approval. This design enforces transparency, avoids compounding errors, and preserves human accountability, while still harnessing the scalability of modern AI systems.

We instantiate HIC-GOV in a multi-agent architecture for taxonomy development, where each specialized AI agent executes a narrowly scoped task corresponding to a spe-

Copyright © 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
cific step of the widely used Nickerson taxonomy develop-
ment method (Nickerson, Varshney, and Muntermann 2013).
Our workflow execution engine orchestrates these special-
ized agents to implement each step of the Nickerson method.
At each step, a mandatory human-gated HIC checkpoint en-
ables Accept/Refine/Stop decisions with rationale and
records a complete audit trail of human-agent interaction.
We evaluate this instantiation in a qualitative case study that
re-derives the taxonomy for AI-enabled workplace inclusion
(Lazazzara, Za, and Georgiadou 2025), using methodolog-
ical fidelity—adherence to the Nickerson method and the
original authors’ decisions—as the primary evaluation lens.

Contributions:

1. We present *Method-Fidelity Human-in-Command Governance (HIC-GOV)*, a paradigm for AI-assisted science that binds Human-in-Command oversight to the formal steps of scientific methods.

2. We instantiate HIC-GOV in a multi-agent framework for taxonomy development aligned with the steps of the Nickerson method.

3. We evaluate this framework in a qualitative case study that re-derives the published taxonomy for AI-enabled workplace inclusion (Lazazzara, Za, and Georgiadou 2025).

Background and Related Work

Human-in-the-Loop vs. Human-in-Command

Classical human-in-the-loop (HITL) positions humans as re-
active reviewers of AI outputs; human-in-command (HIC)
requires humans to retain ultimate authority over AI ac-
tions via explicit control points (Kandikatla and Radeljić
2025; Holmberg 2021). Behavioral evidence shows that
naïve HITL can fail to correct—and may even amplify—AI
errors without structured oversight (Dores Cruz et al. 2025).
Recent work therefore argues for sensemaking-centric col-
laboration, in which humans provide contextual judgment
and accountability while AI provides scale (Hao, Demir, and
Eyers 2025). Our work follows this trajectory by moving
from output-level checking to *process-level governance* by
operationalizing HIC with non-skippable checkpoints and
full audit logging.

Multi-Agent LLM Systems for Scientific Tasks

Multi-agent LLM teams can outperform single models on decomposed research tasks by combining role specialization, critique, and iteration. For example, teams of virtual scientists (VirSci) have been shown to generate more novel and higher-impact research ideas than single-agent baselines (Su et al. 2025). End-to-end AI Scientist pipelines go further by chaining research processes into largely autonomous workflows (Lu et al. 2024). These fully autonomous systems can plan and run experiments with minimal human intervention, but also raise open challenges around verification, safety governance, and reproducibility of such pipelines (Boiko et al. 2023; Szymanski et al. 2023; Tobias and Wahab 2025; Ramos, Collison, and White 2025; Piccialli et al. 2025). While fully autonomous frameworks

like *AI Scientist* (Lu et al. 2024) optimize for speed and discovery, our work prioritizes *auditability* by binding each agent to a specific, named step of a formal scientific method and gating progression through HIC-GOV.

AI for Literature Synthesis and Taxonomies

LLMs can substantially reduce systematic review (SR) screening workload while preserving recall when carefully configured (Delgado-Chaves et al. 2025), yet scoping reviews find full automation premature due to accuracy and transparency limits (Lieberum et al. 2025). For knowledge structuring, LLMs can *generate, validate, and apply* taxonomies with expert oversight (Shah et al. 2025), and recent methods align LLM-generated scholarly taxonomies to human-written structures with explicit structural metrics (Lahiri, Hou, and Sanyal 2025). Our approach differs by enforcing method-conforming roles and human-gated checkpoints to preserve methodological fidelity.

Taxonomy Development via Nickerson et al. (2013)

Nickerson et al.'s iterative method alternates empirical-to-
conceptual (E2C) and conceptual-to-empirical (C2E) cycles
under a guiding meta-characteristic with explicit objective
and subjective ending conditions (Nickerson, Varshney, and
Muntermann 2013). Prior applications often under-report in-
termediate decisions, making it difficult to assess how tax-
onomies were actually constructed (Kundisch et al. 2022).
Our instantiation codifies every step of the method, instru-
ments HIC checkpoints at each E2C and C2E transition, and
records a complete audit trail of AI-human interactions, en-
abling explicit assessment of methodological fidelity.

Method-Fidelity Human-in-Command
Governance (HIC-GOV)

The challenges of accountability and rigor in AI-assisted science are not mere technical issues that can be optimized away. Rather, they reflect a structural tension between scalable automation and method-driven scientific practice. In response, we propose *Method-Fidelity Human-in-Command Governance (HIC-GOV)*, a framework that reconciles the speed of LLMs with the procedural rigor and human accountability required for scientific inquiry by binding Human-in-Command oversight to the formal steps of scientific methods.

Rather than treating humans as reactive reviewers in a
simple HITL pattern, HIC-GOV positions the domain ex-
pert as a *supervisor* or *principal investigator* (PI) who holds
strategic command over a team of specialized AI agents.
This authority is exercised at method-defined checkpoints.
Agents execute narrowly scoped tasks aligned with specific
methodological steps, and the human PI reviews the cur-
rent work products and decides whether the workflow can
continue on this basis. Thus, the architecture leverages the
strengths of AI in large-scale text processing while ensuring
its contributions remain constrained and aligned with the un-
derlying scientific method.
<figure><img src="image_2.png" /><figcaption>Figure 1: High-level HIC-GOV architecture. The human-in-command (HIC) configures the Workflow Execution Engine (WEE), which dispatches specialized agents. Their outputs are wrapped into a checkpoint package and evaluated in the HIC decision gate (Accept/Refine/Stop); decisions and outputs are logged in the State + Audit Log and returned to the WEE for the next step.</figcaption></figure>

## Design Principles

The HIC-GOV framework is founded on four core design principles:

**1. Methodological Fidelity.** The AI workflow must not be an ad hoc black box. The multi-agent architecture is explicitly mapped to the discrete steps of a pre-existing, human-vetted scientific method (e.g., a systematic review protocol, a qualitative coding guide, or, as in our case study, the Nickerson taxonomy development method). This mapping makes each agent's role and each transition understandable, defensible, and aligned with established scientific practice (Kitchenham and Charters 2007).

**2. Scoped Agent Autonomy.** HIC-GOV does not rely on a single, monolithic, end-to-end autonomous agent for science. Such systems are often opaque and difficult to verify. Instead, it employs a team of specialized, modular agents. Each agent is assigned a narrow, well-defined task associated with a specific step of the scientific method (e.g., propose candidates, critique an artifact, synthesize findings), making its output more tractable for human validation. This principle is in line with framed or bounded autonomy as introduced by Dumas et al. (2023).

**3. Human-in-Command Governance.** The human expert retains ultimate authority over the workflow's progression. This authority is exercised through a mandatory, non-skippable protocol of human-gated checkpoints. At each method-defined checkpoint—typically at the conclusion of a methodological step—the workflow pauses for an explicit decision from the human supervisor. This design reduces the risk that errors, biases, or conceptual drift propagate to downstream steps without human review.

**4. Full Auditability.** The entire research process is logged to support transparency and reproducibility. This log serves as a digital lab notebook, capturing every AI-generated proposal, every piece of human feedback, and every *accept, refine, or stop* decision. The resulting audit trail enables rigorous post-hoc analysis, verification, and replication, addressing key concerns about reproducibility in science (Hensel 2021).

## A Generalizable HIC-GOV Architecture

Our HIC-GOV framework is computationally instantiated as a generalizable architecture, depicted in Figure 1. Although we concretely apply it to the Nickerson method in this paper, the structure is, in principle, applicable to other formal, step-based scientific methods encoded as a sequence of steps.

The architecture consists of four primary components:

1. **The Human-in-Command (HIC):** One or more domain experts who initiate the task, define high-level goals (e.g., the meta-characteristic), and retain ultimate authority over progression. They provide method-level guidance and make explicit decisions at each human-gated checkpoint, with decisions that may be distributed across a small team depending on task criticality and expertise.

2. **The Workflow Execution Engine (WEE):** The computational backbone, operating as a finite state machine (FSM), represents the scientific method as a graph of discrete steps and checkpoint transitions, manages workflow state, and routes tasks to the appropriate agents.

3. **The Specialized Agent Team:** A library of modular, reusable LLM-powered agents with narrow roles (Agent 1, Agent 2, ..., Agent N). Each agent is responsible for a specific, method-aligned task, making its outputs easier for the HIC to review and approve.

4. **The Governance Protocol:** The interaction layer through which the human exercises authority at method-defined checkpoints. It comprises (i) a *checkpoint package* that standardizes how artifacts are presented and evaluated (e.g., criteria, rubric, evidence, and rationale fields) and (ii) a *HIC decision gate* where the human issues an outcome (Accept/Refine/Stop), as detailed next.

All artifacts and decisions produced through this loop are persisted in a shared **State + Audit Log**, which provides the WEE with the authoritative workflow state and serves as a digital lab notebook for end-to-end transparency and reproducibility.
## The Governance Protocol

The Governance Protocol is the central mechanism that enforces both methodological fidelity and human authority. It is a container with two coordinated parts:

(a) **Checkpoint Package (process & evidence).** When an agent completes a method-defined task, the system assembles a structured *checkpoint package* that includes: (i) the artifact (e.g., a proposed dimension list), (ii) the method-specific criteria or rubric, (iii) evidence and differences since the last revision, and (iv) required fields for the human's rationale. This standardization supports consistent review and complete provenance. It structures what the human sees and how their assessment must be recorded.

(b) **HIC Decision Gate (human judgment).** The human supervisor is presented with the checkpoint package and issues one of three outcomes:

* **Accept:** The artifact satisfies the criteria and the workflow can proceed. The HIC has two options:

- **(i) Accept as-is:** The agent's artifact (*A*) is accepted without modification.

- **(ii) Override and accept:** The HIC edits the artifact to create a human-modified version (*A'*), providing a direct path to correct minor issues without rerunning the agent.

In both cases, the final artifact (either *A* or *A'* ) and the rationale are written to the *State + Audit Log*, and the WEE advances to the next step.

* **Refine:** The artifact is inadequate or misaligned. The HIC provides updated instructions; the WEE routes this feedback back to the *same* agent (a localized re-run) and records the rationale.

* **Stop:** The HIC terminates the run. The current state and rationale are persisted to the log.

This separation of *process capture* (checkpoint package) from *human judgment* (HIC decision gate) helps prevent cascades of errors, supports auditable decisions, and keeps the human in command while the WEE maintains deterministic control flow.

Algorithm 1 formalizes this governance protocol, specifying how checkpoint packages, HIC outcomes (*Accept/Refine/Stop*), and audit-log persistence control workflow progression.

## Instantiation: A Framework for Taxonomy Development

We instantiate *Method-Fidelity Human-in-Command Governance (HIC-GOV)* in a framework for systematic taxonomy development. The framework operationalizes the rigorous, multi-step Nickerson taxonomy development method (Nickerson, Varshney, and Muntermann 2013) as the guiding scientific protocol and uses it as a testbed to evaluate the feasibility and utility of HIC-GOV.

As depicted in Figure 2, we map the Nickerson process (panel a) to our governed, agent-based HIC-GOV architecture (panel b). This is defined by three primary actors:

<caption>**Algorithm 1: Governance Protocol at a HIC checkpoint**</caption>

1: **HIC defines agent instructions** $I_k$
2: **WEE composes Agent$_k$** prompt from ($I_k$, step definition, state)
3: **Agent$_k$ produces artifact** *A*
4: **Checkpoint package** $C \leftarrow \{ A, \text{criteria}, \text{evidence}, \text{diffs}, \text{required fields} \}$
5: **Present** *C* to HIC (decision gate)
6: **HIC provides outcome** $\in \{\text{ACCEPT}, \text{REFINE}, \text{STOP}\}$ and rationale
7: **if** outcome = ACCEPT **then**
8: **{Check if HIC provided a direct override}**
9: **if** HIC provided override artifact $A'$ **then**
10: Persist ($A'$, outcome, rationale) in State + Audit Log
11: **else**
12: Persist (*A*, outcome, rationale) in State + Audit Log
13: **end if**
14: Workflow Execution Engine advances to next step
15: **else if** outcome = REFINE **then**
16: HIC provides updated instructions $I'_k$
17: Persist ($I'_k$, outcome, rationale) in State + Audit Log
18: WEE re-invokes Agent$_k$ with updated instructions ($I_k \leftarrow I_k \oplus I'_k$)
19: **else**
20: Persist (state, outcome, rationale, terminated) in State + Audit Log; halt
21: **end if**

1. **Workflow Execution Engine (WEE).** The WEE runs the process as a state machine: it advances through methodological nodes, dispatches each node's specialized agent, and maintains the state of the research artifacts.

2. **Governance Protocol (HIC gate).** Every transition between nodes is intercepted by a mandatory human-gated checkpoint. At each such transition, the protocol presents a *checkpoint package* to the human supervisor, who then uses the HIC decision gate to issue an *Accept*, *Refine*, or *Stop* outcome, as described in the previous section.

3. **Specialized Agents.** Each node in the workflow (Figure 2b) is executed by a single-purpose agent with a clearly defined, method-aligned role:

* **Node 1: Meta Characteristic Designer.** Proposes the foundational meta-characteristic—the primary “lens” or purpose of the taxonomy—which the HIC must approve before the process can proceed.

* **Node 2: Ending Conditions Designer.** Drafts the objective and subjective ending conditions required by the Nickerson method. The HIC reviews and approves these criteria.

* **Node 3: Approach Advisor.** Analyzes the problem and recommends the next path: Empirical-to-Conceptual (E2C), which builds a taxonomy from objects in the corpus, or Conceptual-to-Empirical (C2E), which starts from theory. The HIC makes the final decision.
<figure><img src="image_3.png" /><figcaption>Figure 2: Mapping of our framework to Nickerson taxonomy development method. (a) Canonical process. (b) Our HIC-GOV instantiation with agents and HIC checkpoints. HIC diamonds in panel (b) mark human decision checkpoints on every transition.</figcaption></figure>

* **E2C path (Nodes 4e-6e):** inductive workflow.

- **4e. Object Identifier.** Selects a subset of objects (e.g., papers) from the corpus for the current empirical-to-conceptual cycle.

- **5e. Characteristic Identifier.** Reads the selected objects and induces a list of characteristics related to the meta-characteristic.

- **6e. Dimension Grouper.** Groups the characteristics into higher-level dimensions (aiming for mutually exclusive and collectively exhaustive (MECE) groupings where possible), proposing a draft of the taxonomy.

* **C2E path (Nodes 4c-6c):** deductive workflow.

- **4c. Dimension Conceptualizer.** Deductively proposes dimensions and characteristics based on existing theory or HIC guidance, before examining the objects.

- **5c. Object Examiner.** Tests the conceptual taxonomy against the objects, identifying gaps, redundancies, or ambiguities.

- **6c. Taxonomy Reviser.** Revises the taxonomy to resolve the issues identified in the previous step.

* **Node 7: Ending Conditions Evaluator.** Evaluates the current taxonomy against the HIC-approved ending conditions from Node 2 at the end of a full iteration. It presents its assessment in a checkpoint package, allowing the HIC to decide whether to finalize the taxonomy development process or continue iterating.

## Case Study and Qualitative Evaluation

To assess the feasibility, rigor, and methodological fidelity of our HIC-GOV framework, we conduct a qualitative case study. The goal is not to perfectly replicate an existing taxonomy, but to instantiate HIC-GOV on a complex real-world scientific example and assess whether a team of AI agents, under Human-in-Command oversight, can execute a rigorous taxonomy development process. By re-deriving an existing taxonomy from its original data and documented decisions, we highlight the strengths and limitations of this instantiation of HIC-GOV as a multi-agent system for scientific knowledge structuring.

## Target Taxonomy and Corpus

We selected the taxonomy from Lazazzara, Za, and Georgiadou (2025) on “AI-enabled workplace inclusion” as our test case. This study is well suited for three reasons: (1) *Methodological alignment:* it explicitly follows the Nickerson taxonomy development method (Nickerson, Varshney, and Muntermann 2013); (2) *Offline corpus:* its objects of analysis are a fixed set of 25 scholarly papers (empirical studies on AI in HRM and inclusion), which we can use directly as agent input without any web access; and (3) *Auditability:* the authors document their iterative decisions and publish the full list of the 25 papers, enabling us to closely mirror their procedure. Using the same corpus and published decision sequence allows a transparent comparison between the original human-only process and our HIC-governed pro-
<table><caption>Table 1: AI-enabled workplace inclusion taxonomy (dimensions and values) by Lazazzara et al. (2025).</caption><thead><tr><th>Dimension</th><th>Value</th></tr></thead><tbody><tr><td>Strategic inclusion goal</td><td>Inclusion<br>Performance<br>Recruitment</td></tr><tr><td>Human-AI interaction</td><td>Assisting AI<br>Augmenting AI<br>Automating AI</td></tr><tr><td>Inclusion typology</td><td>Socio-demographic characteristics<br>Work status<br>Inclusion meaning</td></tr><tr><td>Inclusion evaluation</td><td>Individual subjective evaluation<br>Objective evaluation</td></tr><tr><td>Mitigation strategy</td><td>AI data<br>AI design<br>AI usage<br>None</td></tr></tbody></table>

cess. To reduce the risk of training-data contamination (i.e., the LLM having seen the target study during training), we selected a very recent Nickerson-based taxonomy paper by scanning the latest works citing Nickerson, Varshney, and Muntermann (2013) and conducted our run shortly thereafter.

Our objective was to re-derive a taxonomy for AI-enabled workplace inclusion by providing our offline multi-agent system with the same inputs and iteration structure described in the target study (Lazazzara, Za, and Georgiadou 2025). In essence, we treat the original study's methodology as a reference workflow. The HIC-GOV system was instructed to execute the same number and type of iterations, using the same corpus and intermediate theoretical inputs reported in the target paper. This setup lets us evaluate whether HIC-GOV can achieve comparable outcomes while strictly adhering to the established methodology, leveraging LLMs for scalable knowledge structuring.

## Experiment Setup

We instantiated our HIC-GOV architecture as an offline, closed-world pipeline in which the specialized AI agents in Figure 2b operated only on a local folder containing the 25 PDFs for the empirical-to-conceptual (E2C) cycles and 5 PDFs for the conceptual-to-empirical (C2E) cycles from the target study's corpus. To mirror the original Nickerson-based procedure, the Workflow Execution Engine followed the same five-iteration sequence reported by Lazazzara, Za, and Georgiadou (2025): E2C(25) → C2E(3) → E2C(25) → C2E(2) → E2C(25). All inputs and evidence were strictly constrained to these papers (no web retrieval), and all AI-generated content had to be grounded in them.

The HIC defined the same meta-characteristic as in the target study ("How artificial intelligence (AI) reshapes workplace inclusion through human resource management (HRM) practices") and adopted the ending conditions from Nickerson, Varshney, and Muntermann (2013) as formal stopping rules. Agent prompts and model versions were fixed throughout the run, and all agents used GPT-5 as the

<table><caption>Table 2: Taxonomy of AI-enabled workplace inclusion in HRM, generated by the HIC-GOV framework.</caption><thead><tr><th>Dimension</th><th>Value</th></tr></thead><tbody><tr><td>HRM Lifecycle Position of AI Inclusion</td><td>HRM Functional Area<br>Work Lifecycle Stage</td></tr><tr><td>Work and Institutional Context</td><td>Sector and Job Type<br>Work Modality<br>Technology Intensity/Exposure</td></tr><tr><td>AI System and Data Foundation</td><td>System Type and AI Method<br>Input Modalities and Context Scope<br>Data Provenance</td></tr><tr><td>Interaction Configuration and Decision Automation</td><td>Level of Automation and Decision Role<br>Interaction and Mediation Channel<br>Identity Visibility and Consensus Controls</td></tr><tr><td>Inclusion Mechanism</td><td>Inclusion Focus<br>Targeted Groups or Process<br>Mitigation Features</td></tr><tr><td>Inclusion Evaluation and Impact</td><td>Evaluation Method<br>Evaluation Metric<br>Perceived Attribution</td></tr><tr><td>Governance</td><td>Design Principles<br>Oversight and Monitoring<br>Privacy, Accessibility, and Transparency Controls</td></tr><tr><td>Adoption-Response Type</td><td>Performance Efficiency Orientation<br>Fairness/Compliance Orientation<br>Risk Mitigation Orientation<br>Inclusion Enhancement Orientation<br>Job Diversity/Redesign Orientation</td></tr></tbody></table>

underlying LLM; all artifacts were logged. After each step, the Governance Protocol presented a checkpoint package to the HIC, who explicitly chose Accept, Refine, or Stop with accompanying rationale. HIC guidance was intentionally kept less restrictive to enable assessment of the strengths and weaknesses of the agent-based system, not the human.

We did not use Lazazzara et al.'s intermediate taxonomic artifacts or intermediate iteration results to steer the workflow, and we did not attempt to match the target taxonomy after each iteration. Instead, all intermediate artifacts were produced and refined within our governed process; we compare against the target study only at the level of the final taxonomy. In short, the pipeline was configured to replicate the original procedure as closely as possible, with any divergence attributable to the agents' analysis under Human-in-Command oversight.

## Final Taxonomy Reconstruction

The HIC-GOV run completed five Nickerson-style iterations and produced an eight-dimension taxonomy with 25 characteristics, shown in Table 2. Across the run, the system annotated all 25 studies and involved a total of 147 HIC interactions. Most HIC interactions resulted in Accept decisions, with Refine decisions concentrated in taxonomy construction and revision steps.

The resulting taxonomy can be criticized, among other reasons, for its lack of conciseness, especially in compar-
son to the taxonomy from Lazazzara, Za, and Georgiadou (2025). Many *values* in the HIC-GOV generated taxonomy function as sub-dimensions, several are quite generic (e.g., broad HRM context descriptors), and some are not sharply distinct, which can make classification ambiguous. This suggests that a broad meta-characteristic combined with relatively permissive HIC guidance encouraged the agents to admit any loosely related characteristic during the iterations.

At the same time, the taxonomy exhibits strong clustering structure and contextual richness—exactly where LLM-based agents excel—and could plausibly serve as a high-level, extensible taxonomy for AI-enabled HRM applications if organized hierarchically. Notably, certain constructs that were dimensions in the original taxonomy (Table 1; e.g., Human-AI interaction) appear as more granular values in the HIC-GOV-created schema (Table 2; e.g., “Level of Automation and Decision Role”). This illustrates both the system’s ability to refine concepts and the need for a focused HIC expert to keep the representation aligned with the intended level of abstraction.

## Lessons Learned

Beyond the taxonomy structure itself, the run surfaced several design lessons for AI-assisted scientific workflows. First, shorter, narrowly scoped texts and prompts led to more stable behavior, suggesting that future HIC-GOV deployments should favor finer-grained, focused steps over broad, open-ended ones. Second, the process was highly sensitive to prompt wording and human feedback. Poorly phrased guidance could substantially change the resulting taxonomy, indicating that domain experts using AI research assistants need at least basic prompt-engineering literacy.

Third, the agents showed a tendency to drift off-topic unless actively steered, underscoring the importance of a vigilant HIC expert who closely monitors the process and intervenes early when refinements diverge from the research focus. Finally, occasional format errors and schema violations encountered in the case study highlight the need for robust validation and error-handling logic in AI-for-science systems, so that methodological constraints are enforced by the tooling rather than assumed.

# Discussion

## Principal Findings

Our case study suggests that HIC-GOV is a viable pattern for AI-assisted taxonomy development that can preserve methodological rigor while leveraging LLM-based agents for scale. Under strict knowledge containment and a fixed Nickerson-style iteration schedule, the framework produced an eight-dimension, 25-characteristic structure that covers all objects and shows substantial overlap with the core dimensions of the original taxonomy from Lazazzara, Za, and Georgiadou (2025), while adding contextual and governance-related structure. Across five iterations and 147 HIC interactions, HIC-GOV did not simply “hallucinate” a taxonomy with compounding errors; it executed a formal method and derived a recognizably valid schema from the original corpus under Human-in-Command oversight.

However, the artifact looks more like a hierarchical coding schema than a compact taxonomy, suggesting that broad meta-characteristics and permissive HIC guidance can produce rich but unfocused structures unless the HIC keeps the scope tight.

More broadly, the study illustrates how a human-in-command, multi-agent design can address central concerns in AI for scientific research: accountability, transparency, and control. HIC checkpoints ensure that no major step proceeds without explicit human approval, and comprehensive logging makes the reasoning process auditable end-to-end. While a high number of HIC interactions (147 in our experiment) may rival manual curation in time cost for small corpora, the latency of HIC steps is a deliberate feature for the domain of high-integrity science and the fundamental value proposition of HIC-GOV is auditability and systematic methodology adherence, not raw speed. The decomposition of scientific endeavours into specialized agents (for extraction, grouping, conceptual revision, and ending-condition checks) creates natural points for critique and error correction. Together, these design choices support a hybrid workflow in which AI provides scalable analysis and clustering, while the human provides guidance and accountability, aligning with a broader shift away from fully autonomous “AI scientists” toward trustworthy AI research assistants embedded in human-led workflows.

## Limitations

Our case study is a proof of concept with several limitations. First, the case study covers a single taxonomy in a specific domain (AI-enabled workplace inclusion) with a small corpus (N = 25), so scalability to larger reviews (e.g., N > 1000) and the resulting cognitive load on the HIC remain unknown. Second, the quality of the outcome depends strongly on (i) the HIC's domain and methodological expertise, (ii) the quality of the prompts for the agents, and (iii) the capabilities of the underlying LLM, with weaker models requiring more *Refine* cycles. Finally, we observed a small number of format errors and schema violations in agent outputs, indicating that more robust automated validation would be needed before deploying HIC-GOV in higher-stakes contexts such as clinical practice guideline recommendations.

## Future Work and a Research Agenda

We see HIC-GOV as a general blueprint for AI-assisted scientific workflows rather than a taxonomy-specific tool. Future work should extend the architecture to other methods, such as literature reviews or qualitative coding protocols, by mapping their procedural steps to agent roles and HIC checkpoints. A key next step is empirical evaluation: controlled studies comparing manual baselines, naive HITL assistance, and HIC-GOV on the same tasks, measuring output quality, time, cognitive load, perceived trust, and methodological fidelity. Finally, aligning HIC-GOV with emerging evaluation practices for AI research assistants could help position this approach more firmly within the broader AI-for-scientific-research ecosystem.
Conclusion

We introduced Method-Fidelity Human-in-Command Gov-
ernance (HIC-GOV), a framework that binds Human-in-
Command oversight to the formal steps of scientific meth-
ods rather than to final outputs alone. Instantiated on the
Nickerson taxonomy development method, HIC-GOV used
a multi-agent LLM workflow with 147 human-gated check-
points to (re-)derive a recognizably valid taxonomy for
AI-enabled workplace inclusion while maintaining method-
ological fidelity and full auditability. More broadly, HIC-
GOV offers a generalizable blueprint for AI-assisted sci-
ence in which specialized agents provide scalable analy-
sis and humans retain methodological control and epistemic
accountability. This work offers a path toward developing
trustworthy AI research assistants.

Reproducibility Statement

To support transparency and reproducibility, we provide (i) the frozen prompts and agent role definitions, (ii) the workflow design as depicted in Figure 2, and (iii) all checkpoint packages and HIC rationales from the case study in an open-source repository. This release allows the community to audit not just the final taxonomy, but the specific decision path that produced it. The code is available at https://github.com/ISDO-TUM/hybrid-taxonomy-generation (corresponding to tag v1.0.0-aaai25). We invite the community to adapt and extend this framework to other scientific methodologies.

Acknowledgments

The authors acknowledge the use of ChatGPT and Gemini to assist with writing clarity, code generation, and idea framing. All AI-generated output was verified and approved by the authors.

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