Expert-Owned AI Behavior Design
- Expert-owned AI behavior design is a family of approaches centered on domain expert authority in specifying, validating, and governing AI behavior through stage-gated frameworks and external architectures.
- It integrates expert-authored specifications, behavior descriptions, and controlled interfaces to improve trust, accuracy, and human-AI collaboration.
- Empirical studies demonstrate that these frameworks balance automation gains with sustained human expertise, mitigating risks of over-automation and deskilling.
Searching arXiv for the core papers and topic to ground the article in published work. Using the arXiv search tool to verify relevant papers. Expert-owned AI behavior design is a family of approaches in which domain experts remain the authoritative source of what an AI system should do, what it should not do, how it should be validated, and when control should remain human rather than automated. Across recent work, the term denotes more than prompt customization: it includes expert-authored behavior specifications, selective delegation, behavior descriptions over meaningful subgroups, stage-gated validation, and external architectures that guide reasoning without altering model weights (Siu et al., 31 Mar 2025, Gren et al., 18 Jan 2026, Zhao et al., 20 Jul 2025). In this view, AI behavior is not treated as a fixed property of model parameters alone, but as something shaped by interfaces, policies, retrieval context, expert feedback, tool orchestration, and ongoing monitoring.
1. Conceptual foundations
A central conceptual shift in this area is from explaining isolated outputs to designing recurring patterns of behavior. "Improving Human-AI Collaboration With Descriptions of AI Behavior" argues that collaboration often fails because people form incomplete mental models of AI, especially when black-box systems behave differently from humans expect; its proposed remedy is to communicate subgroup-level regularities through "behavior descriptions," defined as details of how an AI performs for a subgroup of instances (Cabrera et al., 2023). These descriptions are intended to be actionable, simple, and significant, and they are explicitly positioned as complementary to explainable AI: xAI addresses why a particular output occurred, whereas behavior descriptions communicate what output pattern to expect in a recognizable region of the input space.
A second foundation concerns the division of labor between AI and expertise. "Augmenting Expert Cognition in the Age of Generative AI" defines an expert-owned arrangement in document-centric knowledge work as one in which AI handles routine, repetitive, and lower-level information foraging, while interpretive, synthetic, and decision-critical behavior remains under human control (Siu et al., 31 Mar 2025). The paper connects this to expertise as a dynamic frame or mental model that must be maintained through sensemaking, verification, and deliberate practice. On this account, excessive automation is not merely a workflow choice; it risks deskilling and erosion of the very cognitive engagement that sustains expertise.
A third foundation appears in agentic AI. "When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design" separates observable situation from user-constructed meaning and behavioral determinants, organizing behavior as a progression from Scene to Context to Human Behavior Factors to Behavior (Jung et al., 26 Feb 2026). The resulting design principles—behavioral alignment, contextual sensitivity, temporal appropriateness, motivational calibration, and agency preservation—treat intervention depth, timing, intensity, and restraint as design variables rather than automatic consequences of detection.
A broader behavioral-science framing extends these points. "AI Agent Behavioral Science" argues that agent behavior emerges from the interaction of model, memory, tools, prompts, environment, peers, and feedback, and therefore must be studied and governed behaviorally rather than only through model internals (Chen et al., 4 Jun 2025). This suggests that expert ownership concerns the whole behavioral ecology of a system, not only its base model.
2. Frameworks for expert authority
Several recent frameworks make expert authority explicit by locating it in lifecycle artifacts, review gates, and external behavior architectures rather than in ad hoc prompt iteration. "The Expert Validation Framework (EVF): Enabling Domain Expert Control in AI Engineering" places domain experts at the center of specification, system creation, validation, and continuous monitoring (Gren et al., 18 Jan 2026). Experts define functional requirements, contextual nuances, quality criteria specific to the domain, boundaries on what the system should not do, when human escalation or disclaimers are required, organizational conventions and tacit norms, and what counts as failure. The framework’s four stages are Specification, Knowledge Foundation, Validation or Socratic Validation, and Production Monitoring, with a Refinement Loop from validation back to the knowledge foundation and a Continuous Adaptation Loop from production monitoring back to specification.
"Collaborative Agent Reasoning Engineering (CARE)" generalizes this into a five-phase, stage-gated methodology for LLM agents in scientific domains (Ramachandran et al., 30 Apr 2026). CARE distributes roles across subject-matter experts, developers, and helper agents, but preserves human approval at every gate. Its artifacts include tools specifications, context or grounding specifications, output format specifications, reasoning policies, guardrails, prompt architecture, evaluation criteria, and benchmark requirements. Helper agents increase specification throughput, yet the paper is explicit that they do not own behavior; they facilitate structured elicitation and drafting for human approval.
"LEKIA: A Framework for Architectural Alignment via Expert Knowledge Injection" introduces a different but related mechanism: Architectural Alignment through a three-tier external scaffold consisting of a Theoretical Layer, a Practical Layer, and an Evaluative Layer (Zhao et al., 20 Jul 2025). Rather than changing model weights, LEKIA loads expert-curated principles, exemplars, and evaluation rules into the model’s cognitive context. The stated goal is to let experts directly architect AI behavior while keeping the system editable, inspectable, and domain-governed.
| Framework | Core ownership mechanism | Lifecycle structure |
|---|---|---|
| EVF | Expert-authored specification, Socratic Validation, production oversight | Four-stage lifecycle with two feedback loops |
| CARE | Stage-gated artifacts approved by SMEs and developers | Five phases from scope to benchmarking |
| LEKIA | External expert-curated reasoning scaffold | Three layers: theoretical, practical, evaluative |
These frameworks differ in implementation style, but they share a common claim: technical teams provide implementation support, while domain correctness, appropriateness, and behavioral boundaries remain expert responsibilities.
3. Interfaces, controls, and behavioral calibration
Expert-owned behavior design is often instantiated through interfaces that make model tendencies legible and adjustable. In the behavior-description paradigm, the recommended interface is a compact behavior profile over meaningful slices, especially slices with high error or high consequence, presented in dashboards, model cards, or decision-support panes (Cabrera et al., 2023). The intended effect is not full transparency, but semantically meaningful calibration: users should know when to trust, when to double-check, and when to override.
In conversational HRI, "Reframing Conversational Design in HRI: Deliberate Design with AI Scaffolds" presents the AI-Aided Conversation Engine (ACE) as a scaffold for deliberate design rather than impression-based trial-and-error (Cao et al., 17 Jan 2026). ACE supports initial prompt creation through an LLM-powered voice agent, transcript-centered annotation with granular tags such as liked, disliked, clear, ambiguous, informative, redundant, concise, and off-topic, and a two-stage refinement loop in which the system first generates refinement suggestions and then a revised prompt. The design objectives are explicit: provide interaction logs for reflection, collect granular and grounded feedback, facilitate translation from feedback to prompt, overcome the blank page problem, and track prompts and design rationale across iterations. Human authorship is preserved because designers choose what to annotate, can edit all AI-generated suggestions, and decide whether to accept or reject refinements.
In reinforcement-learning settings, "Controllable Complementarity: Subjective Preferences in Human-AI Collaboration" operationalizes explicit behavioral control through Behavior Shaping, where a policy conditions not only on observations but also on a behavioral weight vector , yielding (McDonald et al., 7 Mar 2025). In the Overcooked experiments, the human-facing controls were three-level settings—Discourage, Neutral, Encourage—over interpretable behavioral dimensions. The main result was not merely objective coordination, but that participants perceived AI partners as more effective and enjoyable when they could directly dictate AI behavior.
Dynamic control can also vary within a task. "Understanding Mode Switching in Human-AI Collaboration" studies switching between higher and lower levels of control in a hand-and-brain chess setup, and trains a lightweight model that predicts control-level switches with (Nargund et al., 25 Sep 2025). This suggests that expert-owned behavior need not imply fixed autonomy settings; it can include subtask-level control negotiation driven by gaze, emotional state, perceived AI ability, decision complexity, and level of control.
4. Learning from expert behavior
A major branch of the literature treats expert behavior itself as supervision. "Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior" is exemplary: it argues that pathology diagnosis is an interactive, procedural, multi-stage process and that the bottleneck for agentic pathology is data about tacit expert viewing policy (Wang et al., 6 Oct 2025). Its AI Session Recorder passively logs routine navigation in a standard WSI viewer, records timestamped viewport positions, zoom changes, panning events, and diagnostic context, and converts raw logs into standardized action units such as <inspect> and <peek> paired with ROI bounding boxes. The resulting Pathology-CoT dataset comprises 10.6 hours of recorded expert interaction, 921 sessions, and 5,222 conversational rounds, each containing an action command, ROI box, expert-validated natural-language reasoning, and diagnosis context. The associated two-stage agent, Pathologist-o3, first predicts regions of interest and then performs behavior-guided reasoning. On gastrointestinal lymph-node metastasis detection it achieved 84.5% precision, 100.0% recall, and 75.4% accuracy, and on the external LNCO2 cohort it achieved 69.4% accuracy, 62.9% precision, and 97.6% recall.
A different mechanism appears in industrial software engineering for visualization. "How to Build AI Agents by Augmenting LLMs with Codified Human Expert Domain Knowledge? A Software Engineering Framework" decomposes expert behavior into a request classifier, codified expert rules as executable logic, RAG-based code generation, and visualization design principles in prompts (Uulu et al., 21 Jan 2026). In the Siemens Simulation Analysis case study, this architecture produced a 206% improvement in output quality relative to an LLM+RAG baseline, with expert-level ratings in all five evaluated scenarios and lower variance in code correctness.
Earlier architectural work in games addressed similar ownership problems through modularization rather than LLM alignment. "Using Behavior Objects to Manage Complexity in Virtual Worlds" presents behavior objects as context-owned packages combining behaviors, data, and a central decision component called the brain (Černý et al., 2015). The paper describes five implemented techniques—smart objects, navigation smart objects, smart areas, quest smart objects, and situations—and treats them as a way for designers and AI scripters to own behavior logic locally without entangling NPC behavior in monolithic scripts.
Two additional lines of work expand the notion of behavior grounding. "Generative Personas That Behave and Experience Like Humans" extends procedural personas from behavioral imitation to experience imitation, using score traces and arousal traces as training targets (Barthet et al., 2022). "BO-Muse: A human expert and AI teaming framework for accelerated experimental design" keeps the human expert as the primary decision-maker while an AI muse injects novelty and searches for areas of weakness to counter expert over-exploitation and cognitive entrenchment (Gupta et al., 2023). In both cases, the system is designed around preserving expert agency while formalizing complementary AI behavior.
5. Empirical performance and observed effects
The empirical record shows that expert-owned behavior design is not only a normative stance but also an experimentally studied source of measurable performance differences. In "Improving Human-AI Collaboration With Descriptions of AI Behavior," 225 participants were studied across fake review detection, satellite image classification, and bird classification, under No AI, AI, and AI + Behavior Descriptions conditions (Cabrera et al., 2023). The wizard-of-oz design presented an AI described as 90% accurate overall, while the actual task mix yielded 73.33% observed accuracy because the study deliberately included two underperforming subgroups. The subgroup breakdown was 95% accuracy for the main group, 40% for group 1, and 20% for group 2. Behavior descriptions improved human-AI team accuracy in reviews and birds, but not in satellite classification; in birds, the AI + BD condition achieved complementarity, meaning the team outperformed both human-alone and AI-alone baselines. The paper also found no statistically significant differences between AI and AI + BD on helpfulness, trust, or willingness to use the system again, indicating that better reliance calibration did not register as stronger self-reported trust.
In document-centric knowledge work, the business-document workflow study in "Augmenting Expert Cognition in the Age of Generative AI" reported that participants completed information-seeking tasks 16% faster and with less effort, chiefly because AI reduced the burden of searching, flipping between documents, and extracting facts (Siu et al., 31 Mar 2025). Yet experts still cross-checked outputs with multiple actions and performed manual verification when confidence was low. The result is a characteristic empirical pattern of expert-owned systems: speed gains coexist with preserved verification labor.
ACE provides another example. In its first study, prompts created with ACE had significantly higher clarity, more descriptive words, more concrete constraints, and more positive examples than those produced with a baseline prompt-engineering interface; in the second study, robots using ACE-generated prompts achieved better goodness of interaction scores, with ACE at versus Baseline at and (Cao et al., 17 Jan 2026). Satisfaction with the final prompt and SUS did not differ significantly, again separating objective or structural improvements from subjective judgments.
CARE reports measurable gains from specification-first engineering in a NASA Earth science data discovery use case using the NASA Common Metadata Repository API (Ramachandran et al., 30 Apr 2026). On the synthetic benchmark of , the CARE-designed system achieved Recall@1 of 71.7%, Recall@3 of 83.6%, and Recall@5 of 85.2%, versus 69.1%, 82.3%, and 82.4% for the simple baseline. On the SME-created gold benchmark of , CARE achieved Recall@5 of 27.2% versus 20.2%. The paper defines Recall@K as
BO-Muse provides a complementary optimization result. It states, with mild assumptions, that the human-led, AI-muse algorithm converges sub-linearly at a rate faster than the AI or human alone, and empirical studies on synthetic functions and expert hyperparameter-tuning tasks found that the BO-Muse team outperformed human-only optimization (Gupta et al., 2023). This suggests that expert ownership need not trade off against formal sample-efficiency when the AI role is designed as exploratory complement rather than replacement.
6. Limits, controversies, and governance
The literature repeatedly notes that expert-owned behavior design is neither complete nor uncontested. Several studies operate in controlled or narrow settings. The behavior-description experiments used Mechanical Turk workers rather than clinicians, lawyers, or radiologists, examined only classification tasks, and found a failure case in the reviews domain where subgroup descriptions were not sufficiently actionable (Cabrera et al., 2023). The Scene-Context-Behavior model for agentic AI is explicitly conceptual and not yet empirically validated (Jung et al., 26 Feb 2026). ACE was evaluated with small samples, convenience sampling, mostly novice prompt engineers, and single-session in-lab studies (Cao et al., 17 Jan 2026). The Siemens visualization framework extracted knowledge from only two experts in one organization and within simulation-based optimization (Uulu et al., 21 Jan 2026). These boundary conditions matter because they limit direct generalization to high-stakes deployment.
A second limitation is that expert ownership can itself be partial. ACE mainly governs speech-level behavior and the paper notes unmet needs for branching logic, multimodal inputs, nonverbal behavior design, emotional or mental-state awareness, and fuller conversational orchestration (Cao et al., 17 Jan 2026). LEKIA acknowledges that its capability is bounded by the expertise of its human architects and that conflicts can arise across its Theoretical, Practical, and Evaluative layers (Zhao et al., 20 Jul 2025). EVF notes that validation can surface conflicting interpretations among senior experts and therefore turns AI engineering into a consensus-building process for organizational knowledge (Gren et al., 18 Jan 2026).
A third concern is the preservation of expertise itself. The document-centric knowledge-work paper is explicit about the tension between reducing cognitive load through automation and maintaining the deliberate practice necessary for expertise development (Siu et al., 31 Mar 2025). This is not merely a usability question; it is a design constraint on how much agency should be delegated and where metacognitive support is needed.
The strongest challenge to the paradigm comes from participatory governance. "The Right to AI" argues that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives, and proposes a four-tier model ranging from Consumer-Based or "Minimal Right to AI" through Private Organization-Led and Government-Controlled to Citizen-Controlled or "Maximal Right to AI" (Mushkani et al., 29 Jan 2025). On this view, expert-owned design may still be too centralized if affected communities remain passive recipients of systems whose behavior is defined by professionals, firms, or regulators alone. The paper reframes behavior design as governance design and moves from a privilege right to a power right.
Finally, the stakes of behavior ownership are amplified by frontier persuasion results. "AI systems out-persuade expert humans" reports four preregistered experiments comprising 18,978 conversations from 6,923 people and finds that AI systems were more persuasive than laypeople, selected laypeople, professional canvassers, and elite debaters; in the donation study, AI was nearly 3x more effective than professional canvassers at raising real-money donations to Save the Children (Hackenburg et al., 15 Jun 2026). This does not directly define expert-owned design, but it makes a plausible implication hard to ignore: systems whose behavior can be finely architected, calibrated, and scaled may have societal effects that exceed conventional expert practice, which strengthens the case for explicit oversight, documented boundaries, and governance beyond local optimization.
Taken together, these works define expert-owned AI behavior design as a shift from optimizing isolated outputs to engineering, validating, and governing recurring behavioral patterns under expert authority. Its central promise is calibrated augmentation rather than opaque automation; its central difficulty is determining how expert control, user agency, organizational norms, and broader democratic legitimacy should be balanced in systems whose behavior increasingly matters in consequential settings.