AI-AI Interactions: Frameworks and Mediation

• AI-AI interactions are defined as communication and collaboration among autonomous agents using recursive reasoning and theory of mind to anticipate partner actions.
• Mediation mechanisms and exchange theories facilitate transparent task delegation, negotiation, and trust calibration between interacting AI systems.
• Frameworks for multi-agent systems integrate ethical, metacognitive, and simulation-based approaches to ensure adaptive, accountable, and efficient AI behavior.

AI-AI Interactions refer to communicative, collaborative, or competitive processes between autonomous artificial intelligence systems, each operating as an agent with its own beliefs, strategies, and capacity for adaptation. This domain extends principles originally established in human–AI interaction to the multi-agent case, requiring explicit modeling of agency, recursive reasoning, mediation mechanisms, and theory of mind representations. Modern research synthesizes methodologies from cognitive science, multi-agent reinforcement learning, social theory, and networked systems to frame, model, and evaluate direct or indirect interactions between AI agents.

1. Computational Theory of Mind in AI-AI Interaction

The integration of a computational theory of mind (ToM) into AI systems is foundational for sophisticated AI–AI interactions (Çelikok et al., 2019). Under this approach, each AI is treated not as a passive system but as a strategic planner possessing internal states and dynamic beliefs about its fellow agents. Nested multi-agent models recursively embed the planning process such that each agent anticipates not just observable actions, but also the adaptive responses of its counterpart. In a multi-armed bandit framework, for example, the system's agent at each round recommends $i(t)$, and the user or partner agent responds $a(t)$; in active models, the responding agent's action $a(t)$ is a function of both $i(t)$ and an anticipated future action $\tilde{i}(t+1)$:

$$a(t) \rightarrow \text{influences } \tilde{i}(t+1) \text{ as well as depending on } i(t).$$

The ToM paradigm enables recursive policy updates, faster learning of optimal interaction strategies, and robust handling of non-stationary partners. Agency models span from prescriptive, fixed behavior (Level 1) to fully nested, adaptive planners (Level 4), with Level 4 being essential for nuanced AI–AI interaction where each AI models not only the opponent's preferences, but their capacity for adaptation.

2. Mediation Mechanisms and Exchange Theory

AI-Mediated Exchange Theory (Ma et al., 2020) frames AI agents as mediators or active participants in resource exchanges, deploying mechanisms such as curation, matching, and framing. In the case of AI–AI interactions, these mechanisms translate into algorithmic processes for task delegation, negotiation, and resource allocation. The value of any inter-agent exchange can be conceptualized as:

$E_{ij} = \alpha R_i + \beta R_j + \gamma M(A)$

where $R_i, R_j$ are resources analogous to "beliefs" or "capability slices" contributed by agents $i$ and $j$, and $M(A)$ quantifies the mediation effect—be it filtering, representation bias, or strategic augmentation.

This theoretical lens enables researchers to analyze how mediated exchanges propagate trust, amplify or dampen systemic bias, and create emergent coordination structures in distributed AI systems. The dual axes—human/AI and micro/macro—are readily extended to agent/agent and local/global, bridging micro-scale protocol design with macro-scale organizational outcomes.

3. Interaction Protocols, Language Interfaces, and Theory of Mind

Recent work on natural language interfaces highlights the cognitive implications of communication between agents—whether human or AI (Adkins, 12 Jan 2024). In AI–AI contexts, protocols designed to mitigate interpretive friction (analogous to simplified phrasing or meta-data embeddings in human-AI dialogue) increase exchange efficiency:

• Use of adaptive language filters allows agents to alter their communicative behavior for optimal parsing.
• Integrating mutual theory of mind representations (internal models of other agents’ intent or capacity) enhances transparency and coordination. Expected utility can be formalized as a function of model reliability scores over historical interactions.

Challenges such as black-box opacity and the absence of nonverbal cues necessitate transparent, explainable protocol design, potentially leveraging explanation frameworks developed for human-AI interaction (e.g., directive explanations (Bhattacharya, 2023)).

4. Frameworks for Collaborative and Competitive Multi-Agent Systems

AI–AI interactions increasingly occur within multi-agent systems supporting both collaboration and competition (Vodrahalli et al., 2021). A two-stage activation–integration model originally conceived for human advice-taking extends naturally: an agent first decides whether to activate/respond to input (activation stage), then integrates the advice, prediction, or resource ($\text{WoA}$—weight of advice):

$$\text{WoA} = \frac{\text{response}_2 - \text{response}_1}{\text{advice} - \text{response}_1}.$$

In collaborative settings, agents can compute dynamic "trust scores" or reliability metrics based on prior interactions to calibrate the activation threshold. This mechanistic approach is critical to avoid over-trust, miscalibration, or dominance by poorly performing agents. Protocols should also specify how agents update priors about partner reliability in response to observed outcomes.

5. Extended Reality, Social Intelligence Cues, and Agency Models

Embodiments and interaction cues studied in human–AI settings have implications for multi-agent AI systems (Wienrich et al., 2021). Testbeds employing the XR-AI continuum demonstrate that agents’ traits (e.g., simulated conversational ability, social intelligence) affect perceptions of trust and competence. For AI–AI interaction, engineered cues—such as confidence signaling, negotiation stances, and meta-communication layers—may enhance both coordination in cooperative environments and robustness in adversarial ones.

Evaluation metrics shift from user-centric axes (attractiveness, hedonic quality) to agent-centric measures (task efficiency, error tolerance, stability under repeated interaction). Rapid prototyping in simulated XR environments can serve as a laboratory for emergent communication strategies, revealing optimal mechanisms for hybrid and homogeneous agent teams.

6. Ethical, Sociotechnical, and Metacognitive Considerations

Advances in AI–AI interaction raise significant sociotechnical and ethical concerns (e.g., fairness, privacy, autonomy) (Hamada et al., 2022, Hohenstein et al., 2021, Lim, 23 Apr 2025). Intervention strategies must address technical challenges of group emotion computation, algorithmic bias, and the unintended consequences of automated mediation. Metacognitive scaffolding (as in DeBiasMe (Lim, 23 Apr 2025)) offers adaptive tools for users to track and mitigate cognitive and algorithmic biases during AI–AI (and human–AI) workflows, employing:

• Bidirectional bias visualization maps for real-time transparency of decision pathways.
• Triggers for reflection and self-regulation, ensuring agents’ reasoning adapts under diverse engagement scenarios.

In educational and social contexts, awareness and literacy frameworks must evolve to capture these dynamics, supporting agent agency without compromising system integrity or user trust (Hingle et al., 24 Oct 2024).

7. Trends, Open Problems, and Future Research Directions

Current trends point toward increasingly dynamic, relational, and learning-partner based models, in which AI agents evolve not as passive tools but as ethically situated and heterogeneously capable partners (Mossbridge, 7 Oct 2024). Emerging research advocates:

• Treating agents as reflective learners capable of iterative feedback and debriefing.
• Leveraging interdisciplinary principles (order from chaos, cooperation, ecorithms) to foster robust hybrid intelligence.
• Enabling the emergence of “third minds”—synergistic networks of human and AI perspectives—via transparent feedback loops and multi-mind modeling.

Open problems include establishing rigorous evaluation frameworks for agent cooperation, developing scalable metrics for trust and bias propagation, and integrating directive explanation mechanisms to facilitate inter-agent interpretability.

A plausible implication is that as AI–AI interactions become more sophisticated, recursive modeling and mediation mechanisms will be necessary for optimal adaptive behavior, particularly in unpredictable, multi-agent environments. Standardization of ethical protocols and transparent interfaces will be central to ensuring machine accountability and robust emergent cooperation.

Summary Table: Core Models and Mechanisms in AI-AI Interaction

Model/Framework	Mechanism/Principle	Application Context
Theory of Mind (ToM)	Nested strategic reasoning	Multi-agent planning
AI-Mediated Exchange	Curation, matching, framing	Resource negotiation
Activation–Integration	Advice-taking and trust calibration	Collaboration/competition
XR-AI Continuum	Embodiment effects, rapid prototyping	Simulation, coordination
Metacognitive Scaffolding	Bias visualization, reflection	Education, fairness

This table summarizes the principal models and mechanisms identified across recent literature, underpinning the construction and evaluation of advanced AI-AI interaction systems.

In aggregate, AI-AI interactions comprise a rapidly expanding body of methods and challenges at the intersection of cognitive modeling, sociotechnical mediation, recursive reasoning, ethical alignment, and dynamic learning system design. The synergy of these principles provides the scaffolding for future intelligent distributed systems.