Beyond Autonomy: Building AI with Social Intelligence

Author: Denis Avetisyan

As artificial intelligence systems gain greater independence, integrating principles from multi-agent systems research is crucial for achieving truly responsible and accountable agency.

This review argues for the necessity of formal verification, normative reasoning, and theory of mind in the development of agentic AI systems.

While current approaches to Agentic AI emphasize behavioral autonomy, a critical gap remains in establishing truly responsible and accountable agency. This paper, ‘Agentifying Agentic AI’, argues that bridging this gap requires grounding these systems in the well-developed principles of the Autonomous Agents and Multi-Agent Systems (AAMAS) community. By integrating explicit models of reasoning, communication, and social norms, we demonstrate a pathway towards agentic systems capable of not only adaptation and flexibility, but also transparency and cooperation. Can these established foundations from AAMAS unlock the full potential of Agentic AI, moving it beyond capability towards genuine trustworthiness?

Decoding Autonomy: Beyond the Limits of Conventional AI

Conventional artificial intelligence often struggles when faced with the unpredictable nature of real-world scenarios. These systems, typically designed for narrow, predefined tasks, exhibit limited capacity for generalization and independent problem-solving. Unlike human intelligence, they lack the inherent adaptability to adjust to novel situations or recover gracefully from unexpected obstacles. This inflexibility stems from their reliance on static datasets and pre-programmed algorithms, hindering their ability to learn from experience or formulate strategies beyond their initial training. Consequently, traditional AI frequently requires constant human intervention and struggles with tasks demanding nuanced judgment, creative thinking, or dynamic responses – limitations that necessitate a shift towards more autonomous and versatile approaches.

Agentic AI represents a significant leap beyond conventional artificial intelligence by integrating the expansive knowledge of Large Language Models with the capacity for deliberate reasoning and concrete action. Rather than simply responding to prompts, these systems are engineered to pursue defined goals, breaking down complex objectives into manageable steps and proactively interacting with environments to achieve desired outcomes. This functionality isn’t merely about automation; it’s about imbuing AI with a form of agency, allowing it to dynamically plan, execute, and adjust strategies based on observed results – essentially, to learn and adapt in pursuit of its objectives. The result is a system capable of tackling multifaceted challenges that demand not just information recall, but also foresight, flexibility, and the ability to navigate unforeseen circumstances, marking a shift towards truly autonomous and problem-solving AI.

The emergence of agentic AI represents not a radical departure, but rather a significant evolution within the established field of Autonomous Agents and Multi-Agent Systems (AAMAS). While AAMAS has long explored the creation of intelligent entities capable of independent action, recent advances in Large Language Models are now enabling a level of complex reasoning and natural language interaction previously unattainable. This synergy addresses a critical gap identified in contemporary AI research, which increasingly emphasizes the need for transparency, accountability, and social grounding in artificial intelligence. By equipping agents with the capacity to articulate their reasoning, justify their actions, and operate within established social norms, agentic AI promises to move beyond purely functional intelligence towards systems that are both capable and responsible, fostering greater trust and collaboration between humans and artificial entities.

The Architecture of Agency: Beliefs, Desires, and Communication

The Belief-Desire-Intention (BDI) architecture is a computational framework used in artificial intelligence to model agents capable of rational decision-making. It posits that an agent’s behavior is driven by three core components: its beliefs about the current state of the world, its desires representing goals it wishes to achieve, and its intentions, which are the commitments to act in certain ways to fulfill those desires. An agent operating under a BDI framework maintains a representational state of its beliefs, and uses these beliefs, combined with its desires, to rationally select and commit to intentions. This intention then drives the execution of actions. Formally, the architecture allows for the specification of an agent’s rational behavior through a set of rules that govern how beliefs, desires, and intentions interact, facilitating the development of autonomous and goal-oriented systems.

Agent communication is facilitated by standardized protocols that define message formats and interaction patterns. The Foundation for Intelligent Physical Agents – Agent Communication Language (FIPA-ACL) is a widely adopted standard, specifying performatives – speech acts like requests, inform, and query – along with content languages for expressing message content. Knowledge Query and Manipulation Language (KQML) provides a complementary approach, focusing on knowledge sharing and enabling agents to exchange information about beliefs and goals. Both FIPA-ACL and KQML define message structures including sender, receiver, performative, content, and language, allowing for interoperability between different agent systems and facilitating complex multi-agent interactions.

Theory of Mind (ToM) represents the capacity to attribute mental states – beliefs, desires, intentions, and knowledge – to others, and to understand that these mental states may differ from one’s own. In multi-agent systems, ToM enables agents to predict the behavior of other agents based on their modeled internal states, facilitating strategic interaction and collaborative task completion. Accurate modeling of another agent’s beliefs allows for anticipating responses to actions, while understanding their goals and intentions informs the selection of cooperative or competitive strategies. Computational models of ToM often utilize recursive belief representations, where an agent reasons about what another agent believes about its beliefs, enabling complex reasoning about deception and indirect communication.

Grounding Reality: Ensuring Reliable Action in a Chaotic World

Agent reliability is fundamentally dependent on consistent grounding – the ability to accurately perceive and reason about the external environment. Disconnection between an agent’s internal representation of the world and its actual state introduces the potential for erroneous actions and unpredictable outcomes. This necessitates mechanisms that ensure the agent’s reasoning processes are directly linked to verifiable environmental data. Failure to maintain this connection can lead to actions that are not only ineffective but also potentially harmful, particularly in safety-critical applications where even minor deviations from expected behavior can have significant consequences. Therefore, robust grounding techniques are a prerequisite for deploying agents in real-world scenarios and achieving trustworthy autonomous operation.

Formal verification employs mathematical methods to establish the correctness and safety of an agent’s decision-making process. This involves creating a formal model of the agent’s logic and environment, then using techniques like model checking, theorem proving, and abstract interpretation to rigorously demonstrate that the agent will always behave as intended, adhering to specified safety properties. Unlike testing, which can only reveal errors in specific scenarios, formal verification aims to provide guarantees about all possible states and inputs. Common specifications are expressed using temporal logic, such as Linear Temporal Logic (LTL) or Computation Tree Logic (CTL), to define desired behaviors over time. Successful formal verification provides a high degree of confidence in the agent’s reliability, particularly crucial in safety-critical applications where failures could have significant consequences.

Rigorous evaluation metrics are crucial for quantifying the capabilities of autonomous agents across several key dimensions. Performance metrics, such as task completion rate and efficiency – measured in time or resource usage – establish baseline functionality. Safety metrics, including collision rates, violation of constraints, and recovery from failures, assess risk mitigation. Autonomy is quantified by metrics like time to intervention, reliance on human oversight, and adaptability to unforeseen circumstances. These metrics should be collected across diverse and representative datasets, and statistically analyzed to provide data-driven insights into agent strengths and weaknesses. Quantitative evaluation enables iterative improvement, comparative analysis between agents, and ultimately, builds trust in their reliable operation within complex environments.

Navigating the Ethical Labyrinth: Governance, Risk, and Societal Impact

The emergence of agentic artificial intelligence – systems capable of autonomous action and decision-making – necessitates robust institutional governance frameworks. These frameworks move beyond traditional oversight, demanding proactive strategies to ensure these powerful technologies align with fundamental societal values and ethical principles. Effective governance isn’t simply about reactive regulation; it requires establishing clear lines of accountability, fostering transparency in algorithmic processes, and creating mechanisms for ongoing monitoring and evaluation. Without such frameworks, the potential for unintended consequences – from biased outcomes to erosion of privacy – increases exponentially, hindering the responsible integration of agentic AI into critical infrastructure and daily life. This proactive approach is vital to build public trust and unlock the transformative benefits of these increasingly sophisticated systems while safeguarding against potential harms.

Effective deployment of agentic AI necessitates a robust, forward-looking approach to risk management. This isn’t simply about reacting to problems as they arise, but rather proactively identifying potential harms – ranging from algorithmic bias and privacy violations to economic disruption and even physical safety concerns – before they manifest. Careful assessment of these risks involves evaluating both the likelihood of occurrence and the potential severity of impact, allowing for prioritized mitigation strategies. These strategies might include incorporating safety constraints into AI design, establishing rigorous testing and validation procedures, and implementing ongoing monitoring systems to detect and address unforeseen consequences. Ultimately, proactive risk management isn’t about preventing all risk – an impossible task – but about building resilient systems and establishing clear protocols to minimize harm and ensure responsible innovation.

The rapid advancement of artificial intelligence is prompting a global wave of regulatory initiatives designed to foster responsible innovation. Frameworks like the European Union’s AI Act represent a landmark attempt to categorize AI systems based on risk, imposing stricter requirements on those deemed high-risk, such as those used in critical infrastructure or law enforcement. Simultaneously, the US National Institute of Standards and Technology (NIST) has published its AI Risk Management Framework, providing a voluntary, yet comprehensive, guide for organizations to identify, assess, and manage risks associated with AI systems throughout their lifecycle. These emerging regulations aren’t simply about compliance; they aim to establish a common language and set of principles for trustworthy AI, encouraging developers to prioritize safety, transparency, and accountability while navigating the complex ethical and societal implications of increasingly powerful technologies.

Beyond Individual Agents: The Future of Collective Intelligence

Agentic artificial intelligence systems, poised to operate with increasing autonomy, stand to gain considerably from the principles of Social Choice Theory – a field traditionally concerned with how individual preferences translate into collective decisions. Rather than relying on centralized control, these systems can leverage methods like voting rules and preference aggregation to coordinate actions amongst themselves. This approach mirrors how humans navigate complex social dilemmas, allowing agents to reach consensus even with conflicting objectives. By mathematically defining and implementing these aggregation functions, researchers are enabling multi-agent systems to distribute decision-making power, fostering robustness and adaptability. The result is not simply a collection of individual agents, but a cohesive, collective intelligence capable of tackling problems beyond the reach of any single entity, and doing so in a way that reflects the diverse ‘opinions’ of its constituent parts.

Agentic systems operating in complex environments increasingly require the ability to collaborate and compete, necessitating a robust understanding of strategic interactions. Game Theory provides precisely this analytical framework, allowing researchers to model scenarios where the outcome for one agent depends on the actions of others. By employing concepts like Nash equilibria – stable states where no agent can improve its outcome by unilaterally changing strategy – these systems can anticipate the likely responses of peers and formulate optimal plans. This extends beyond simple competition; agents can learn to cooperate through repeated games, establishing trust and mutually beneficial outcomes. Furthermore, game-theoretic models enable the design of incentive structures that encourage desired behaviors, ensuring collective action aligns with overarching goals and fostering a more predictable, and ultimately, successful multi-agent ecosystem.

As agentic systems gain complexity and autonomy, ensuring value alignment becomes paramount to their responsible development and deployment. This prioritization moves beyond simply defining goals; it necessitates imbuing these systems with a robust understanding of human values, ethical considerations, and societal well-being. Researchers are exploring methods to encode these often-nuanced principles, ranging from reinforcement learning with human feedback to formal verification techniques that guarantee adherence to specified ethical constraints. Successfully aligning artificial intelligence with human values isn’t merely a technical challenge, but a crucial step in fostering trust and preventing unintended consequences as these systems increasingly shape aspects of daily life and critical infrastructure. Ultimately, value alignment isn’t about controlling AI, but about guiding its development towards outcomes that benefit humanity as a whole.

The pursuit of agentic AI, as detailed in the research, reveals a recurring pattern: systems initially exhibit behavioral autonomy, but genuine agency demands far more. This echoes Marvin Minsky’s observation: “The question of what constitutes intelligence is best answered by building intelligent systems.” The paper demonstrates that simply building agentic systems isn’t enough; they must be grounded in the formal principles of multi-agent systems-reasoning about norms, coordinating with others, and even modeling the beliefs of those around them. Without this foundation, agency remains a superficial performance, lacking the robustness and accountability needed for real-world application. The study emphasizes that understanding the limitations of current approaches requires actively probing those limitations-effectively, breaking the system to understand how it fails, and rebuilding it stronger.

Taking the Lid Off

The pursuit of ‘agentic’ AI, as this work suggests, feels less like building intelligence and more like attempting to graft a personality onto a particularly efficient algorithm. The real challenge isn’t if these systems can act independently, but whether they can be meaningfully held accountable when those actions deviate from intent-or, more interestingly, when their ‘intent’ is not what it seems. Formal verification, touted as a potential safeguard, remains a largely theoretical exercise when applied to systems capable of genuine learning and adaptation; a verified system today is a snapshot, not a guarantee.

The paper’s call for grounding in multi-agent systems principles isn’t merely a suggestion for better engineering; it’s an acknowledgement that agency, even in its simplest forms, is fundamentally a social construct. Normative reasoning and theory of mind aren’t simply tools to simulate social behavior, they’re the frameworks through which we understand – and constrain – the actions of others. The next step isn’t building smarter agents, it’s designing systems that allow us to deliberately, and perhaps mischievously, break those rules and observe what happens.

Ultimately, the success of this field may not be measured in terms of autonomous capability, but in the elegance with which these systems reveal the inherent fragility of our own assumptions about intelligence, responsibility, and control. Perhaps the true goal isn’t to create agents that think like us, but agents that force us to rethink what it means to think at all.

Original article: https://arxiv.org/pdf/2511.17332.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/