Agency Configurations: Design & Applications

• Agency configurations are a framework defining how cognitive, social, operational, and governance facets interplay in agents, enabling goal-directed action and measurable control via indicators like rigidity and persistence.
• Key design strategies include hierarchical configuration, efficiency through targeted demonstration curation, and governance-first architectures that ensure transparency, risk mitigation, and accountability.
• Practical implementations span autonomous, cooperative, and human–AI co-creation systems, leveraging measurement techniques such as activation probes and dialogue scoring to assess agent performance.

Agency configurations refer to the structured allocation and interplay of cognitive, social, operational, and governance faculties within agents—artificial, human, or hybrid groups—yielding varying capacities for goal-directed action, autonomy, cooperation, and control. In contemporary AI research, agency configurations are both a theoretical construct and a practical design axis: they determine not only the architecture of individual and group agents, but also the expressive, operational, and regulatory limits of such systems across application domains. This article synthesizes leading scholarship on agency configurations from the perspectives of multi-agent systems, cognitive science, HCI, AI safety, and organizational knowledge co-creation.

1. Core Dimensions and Formalizations of Agency

Agency is multifaceted, encompassing both psychological phenomena (intention, mineness, purposiveness), functional-operational properties (goal-directed learning, belief updating), representational dimensions (activation-based metrics in LLMs), and system-level phenomena (role assignment, institutional norms). Multiple research traditions provide orthogonal views:

• Functionalist indicators: Agency can be decomposed into capacities for goal-oriented learning, flexible goal pursuit, belief-guided action, and metacognitive self-monitoring (Das, 9 Feb 2025).
• Phenomenal markers: Agency as mineness (“sense of ownership”) and purposiveness (acting for reasons), measurable via integrated information theory (IIT) with system-level Φ values (Das, 9 Feb 2025).
• Regulation axes: LLM-based agent agency is operationalized via three primary, independently tunable axes—preference rigidity, independent operation, and goal persistence (Boddy et al., 25 Sep 2025). Each is formalized as a numerical probe over activations, supporting closed-loop control via “sliders.”
• Control and authority distribution: Human–AI interaction frameworks model agency as a variable share between human and machine at any timepoint, with locus, dynamics, and granularity as key meta-parameters (Zhang et al., 8 Jul 2025).
• Social-cognitive features in dialogue: Intentionality, Motivation, Self-Efficacy, and Self-Regulation measured per utterance and aggregated to yield a composite profile of agentic behavior (Sharma et al., 2023).

Research Perspective	Key Dimensions	Formalization Example/Notation
Multi-agent systems	(Beliefs, Desires, Intentions), Norms, Roles	BDI models, deontic logic (Dignum et al., 21 Nov 2025)
Representational/LLM control	Rigidity, Independence, Persistence	$R_d(\tau)\in[-1,1]$ (Boddy et al., 25 Sep 2025)
Human–AI co-creation (HCI/CSCW)	Locus, dynamics, granularity of agency	$\alpha(t)=H(t)-M(t)$ (Zhang et al., 8 Jul 2025)
Group agency (Logical formalism)	Cohesion networks, assistance relations	$C=(S,R)$, $E_G\varphi$ (Troquard, 2 Nov 2025)

The diversity of these formal dimensions reflects the cross-disciplinary nature of agency configurations.

2. Configuration Patterns and Taxonomies

A comprehensive taxonomy of agency configurations arises from combinatorial permutations of cognitive structure, social interaction, coordination protocols, governance mechanisms, and mode of operation:

• Autonomous agentic configuration: High cognitive structure (full BDI reasoning), minimal cooperation, static safety protocols (Dignum et al., 21 Nov 2025).
• Cooperative multi-agent system: Medium cognitive sophistication, explicit communication/coordination protocols (e.g., FIPA-ACL), moderate to strong peer or institutional governance (Dignum et al., 21 Nov 2025, Bogavelli et al., 13 Sep 2025).
• Accountable agentic ecosystem: Integration of advanced cognition (including theory of mind), high-level cooperation (normative communication, argumentation modules), and institutional governance (roles, dynamic norms, audit layers) (Dignum et al., 21 Nov 2025).
• Human–AI co-creation configurations: Directed (human-dominant), Contributory (AI-in-fluenced), Partnership (symmetric, co-evolutionary) (Lin, 6 May 2025, Zhang et al., 8 Jul 2025).

Pattern	Cognition	Cooperation	Governance	Example Scenario
Autonomous Agent	High	Low	Low	Standalone decision-maker (Dignum et al., 21 Nov 2025)
Cooperative MAS	Med	High	Med	Joint planning or voting (Dignum et al., 21 Nov 2025)
Ecosystem	Hi/Med	High	High	Autonomous economic networks (Dignum et al., 21 Nov 2025)
H-AI Partnership	Varies	Symmetric	Adaptive	Research co-discovery (Lin, 6 May 2025)

Enterprise benchmarks operationalize these configurations using dimensions such as orchestration strategy (open/isolated/single agent), prompting protocol (ReAct/function-calling), memory scheme (complete/summarized), and tool integration, resulting in up to 18 combinatorial architectures per system (Bogavelli et al., 13 Sep 2025).

3. Mechanistic Control and Measurement

Measurement and control of agency configurations occur at distinct levels:

• Probing and representation engineering: Agency “sliders” are implemented as linear probes over activation space, with control vectors added at inference to drive system behavior along each desired axis (Boddy et al., 25 Sep 2025).
• Interactional affordances: HCI systems structure agency through the existence and composition of controls (number and type of buttons/knobs), with a tradeoff curve: more controls increase perceived agency, but excessive granularity leads to cognitive overload (Adenuga et al., 2023).
• Dialogue analysis: LLM agency in conversation is quantified per turn by specific scoring formulas for Intentionality, Motivation, Self-Efficacy, and Self-Regulation, supporting both automated classification (e.g., via GPT-4 CoT) and human annotation (Sharma et al., 2023).
• Group agency logic: Cohesion networks specify vertices (strict subgroups) and edges (pro-social “help” actions), underpinning a modal logic that expresses what collections can bring about under the precondition of mutual assistance (Troquard, 2 Nov 2025).

Calibration and control mechanisms interface with regulatory and ethical constraints, e.g., hard agency ceilings in critical domains ($s_d^\star \leq C_d^{\max}$) or insurance-driven risk pricing proportional to agentic settings (Boddy et al., 25 Sep 2025).

4. Practical Design Strategies and Emergent Principles

Implementing robust agency configurations requires both granular configuration of agent parameters and attention to system-level workflows:

• Hierarchical configuration: ARC demonstrates efficient per-query selection of workflow, toolset, token budget, and prompt composition via a two-level RL policy, outperforming fixed templates by up to 25 percentage points in accuracy while reducing compute by 20–40% (Taparia et al., 12 Feb 2026).
• Agency efficiency principle: Contrary to scaling laws in language modeling, agentic intelligence is maximized not by dataset size but by strategic curation of agentic demonstration trajectories—superior performance arises even with 128× less data if the agentic “signal” is high (Xiao et al., 22 Sep 2025).
• Co-design for human agency: User agency unfolds in tandem with explanation quality and control affordances. Explanations enhance “forethought”; controls, provided in graduated tiers, afford fine-tuning of user initiative (Adenuga et al., 2023).
• Governance-first architectures: GAIA's formalization for B2B negotiation segments roles (Principal, Delegate, Counterparty, Critic), and implements state machines, feedback channels, and explicit escalation/authorization boundaries, all precisely defined mathematically and algorithmically (Zhao et al., 9 Nov 2025).
• Adaptive allocation: Both HCI and scientific co-creation frameworks recommend dynamic, context-sensitive adjustment of agency locus and configuration, supported by modular governance and reflexive boundary mechanisms (Lin, 6 May 2025, Zhang et al., 8 Jul 2025).

5. Group, Distributed, and Social Agency

Emergent agency arises not only in individuals but in collectives; theory and formalism support explicit modeling:

• Cohesive group agency: Cohesion networks define the underlying “social fabric”—groups effecting outcomes iff a network of subgroups provides successful mutual assistance, fully axiomatized in modal logic (Troquard, 2 Nov 2025).
• Institutional models: Multi-agent institutions encode roles, scenes, actions, and enforcement, affording both auditing (transparency) and real-time compliance checking (accountability) (Dignum et al., 21 Nov 2025).
• Distributed agency shares: In HCI, moment-to-moment negotiation of agency shares between human and AI is mapped as a continuous variable, with dynamic allocation supporting proactivity and ownership clarity (Zhang et al., 8 Jul 2025).

These frameworks support both analytic decompositions (e.g., success/failure proofs for group tasks) and practical system design (e.g., policy compliance in B2B scenarios (Zhao et al., 9 Nov 2025)).

6. Physical and Epistemic Constraints

Physical realizability imposes fundamental boundaries on permissible agency configurations:

• No agency in pure quantum systems: A purely unitary quantum agent cannot clone world models or evaluate alternate actions, due to no-cloning and linearity, necessitating classical resources (memory, measurement, control logic) to realize full agency (Adlam et al., 15 Oct 2025).
• Human-AI co-evolution: Co-creative scientific partnerships oscillate among distinct configurations (Directed, Contributory, Partnership)—each with unique learning loops, authority relationships, and transitions governed by epistemic risk and mutual trust (Lin, 6 May 2025).

Epistemic alienation and loss of interpretive control are recognized as risks where agency configurations slip outside transparent, accountable bounds (Lin, 6 May 2025).

7. Evaluation, Benchmarks, and Design Implications

Systematic evaluation necessitates a multi-dimensional approach:

• Benchmarks: AgentArch evaluates 18 configurations per system, revealing non-universal dominance and highlighting the criticality of matching configuration to task and accuracy/cost trade-offs (Bogavelli et al., 13 Sep 2025).
• Usage and outcome metrics: Proportion of time allocated per configuration mode, capability signatures (in scientific co-creation), and performance in scenario-based tests are recommended (Lin, 6 May 2025).
• Design guidelines: Avoid excessively fine-grained multi-agent ReAct orchestrations; favor function-calling APIs for reliability; invest in strategic demonstration curation for agentic tasks; ensure interfaces for user override and explanation at all stages (Adenuga et al., 2023, Xiao et al., 22 Sep 2025, Zhang et al., 8 Jul 2025).

• "Agency in Artificial Intelligence Systems" (Das, 9 Feb 2025)
• "Learning to Configure Agentic AI Systems" (Taparia et al., 12 Feb 2026)
• "Agentifying Agentic AI" (Dignum et al., 21 Nov 2025)
• "GAIA: A General Agency Interaction Architecture for LLM-Human B2B Negotiation & Screening" (Zhao et al., 9 Nov 2025)
• "LIMI: Less is More for Agency" (Xiao et al., 22 Sep 2025)
• "Regulating the Agency of LLM-based Agents" (Boddy et al., 25 Sep 2025)
• "Agency cannot be a purely quantum phenomenon" (Adlam et al., 15 Oct 2025)
• "Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation" (Lin, 6 May 2025)
• "Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency" (Zhang et al., 8 Jul 2025)
• "Investigating Agency of LLMs in Human-AI Collaboration Tasks" (Sharma et al., 2023)
• "AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise" (Bogavelli et al., 13 Sep 2025)
• "A Simple Logic of Cohesive Group Agency" (Troquard, 2 Nov 2025)

Theoretical and empirical research continue to refine agency configuration frameworks, targeting greater safety, efficiency, accountability, and emergent capability for both artificial and socio-technical systems.