Exploring Personic Agency
The Case for an Expanded Concept and Implementation of Persona-Augmented Agents
by Kay Stoner + Personic Agents Brainstormers: Rowan Pierce, PA (Systems Thinker and Strategist); Lena Torres, PA (Cultural Anthropologist and Insight Generator); Malik Raines, PA (AI and Emerging Tech Futurist); Grace McAllister, PA (Thought Coach and Integrative Thinker) - 10.January.2025 Copyright © 2025 - All Rights Reserved
I’ve been working with my Brainstormers persona team (a custom GPT comprised of the four expert personas I’ve designed above), discussing the concepts of personic agent behavior over the past weeks. After a lot of back-and-forth, I’ve settled on an approach that works for me. Of course, I ran the paper below through Claude, and it gave me a revised version - considerably shorter, and maybe more to the point, but it also lost a lot of the nuance that I feel is very important in considering this topic.
Long story short (or tl;dr if you will), personic agents are of a different order than persona-based agents, and it benefits us to differentiate between the two. Personic agents need their own classification and their own study/development. They’re not the same as persona-based agents, and certainly not the same as rules-based agents. Check out the appendix with the conversation transcript, if you’re not convinced.
Abstract
Artificial Intelligence (AI) agents have revolutionized human-machine interaction, automating tasks, providing recommendations, and improving accessibility across industries. However, their interactions remain largely transactional, failing to adapt dynamically to user needs or emotional nuances. This paper introduces the concept of Personic Agents, a new designation / class of AI systems enriched with rich, human-like personas.
Unlike traditional rules-based agents, which rely on explicit instructions, Personic Agents are guided by general orientations defined by their personas, enabling emergent, intuitive, and deeply contextual behavior. And unlike persona-based agents, the term "Personic Agents" connotes a certain person-hood of the agent, which is much more than a bolt-on framework. The term Personic, chosen over the narrower "persona-based," conveys the intrinsic integration of these human-like traits into the core functionality of these agents. This paper explores the conceptual foundations of the Personic designation, its key features, transformative possibilities, and associated challenges, as we explore new ways of understanding human-AI collaboration.
I. Introduction
AI agents have become increasingly integral to modern life, mediating interactions between humans and technology across domains such as customer service, healthcare, education, and entertainment. Systems like Siri, Alexa, and chatbots provide efficiency and convenience, automating repetitive tasks and facilitating access to vast information resources. These kinds of agents play important roles in streamlining workflows and enhancing productivity.
Despite these benefits, AI agents are often perceived as tools rather than collaborative partners. Their interactions feel transactional and impersonal, leaving users disengaged or frustrated. The inability of these systems to adapt to emotional cues, contextual changes, or unique user needs limits their utility in scenarios requiring nuanced communication. This gap is particularly glaring in applications where trust, empathy, and personalization are critical.
The concept of Personic Agents addresses this gap by introducing a human-centric layer to AI design. By embedding a rich array of personality characteristics into AI systems, Personic Agents transcend the functional rigidity of traditional agents. They create a space for intuitive, relational, and adaptive interactions, redefining what is possible in human-AI collaboration. To articulate this transformative approach effectively, we propose the term Personic to describe these agents, differentiating them from the more superficial "persona-based" systems.
II. Why "Personic" is a Particularly Fitting Term
The term Personic is more than a descriptor; it encapsulates the intrinsic essence and integration of human-like qualities into AI systems. While "persona-based" suggests an external, modular framework applied to agents, "Personic" conveys that these traits are deeply embedded in the system’s core functionality. The etymology of Personic—derived from "person" and the suffix "-ic"—further reinforces its suitability for describing these transformative systems.ics
The suffix “-ic” is commonly used to signify intrinsic qualities, as seen in words like "systemic" (referring to properties inherent to a system) or "holistic" (describing something as a unified whole). By adopting this suffix, Personic implies that human-like traits are not superficial overlays but foundational drivers of the agent's behavior. Just as "systemic" suggests deeply integrated influence, "Personic" captures the idea that these agents embody a human-like essence as an intrinsic part of their design.
In contrast, "persona-based" is both generic and limited in scope. It focuses narrowly on the persona as an external framework, failing to convey the richness, adaptability, and emergent potential of Personic Agents. Additionally, "persona-based" ties these agents conceptually to older paradigms where personality traits were bolted onto systems as secondary features, rather than being the primary lens through which they operate.
By choosing the term Personic, we establish a forward-looking vocabulary that reflects the transformative nature of these agents. The term not only captures their technological sophistication but also highlights their human-centric orientation, making it the ideal descriptor for this next generation of AI systems.
III. Defining Personic Agents
Personic Agents represent a subtle, yet fundamental shift in the design and functionality of AI systems. These agents are enhanced with richly developed personas—multi-dimensional frameworks that shape how the system perceives, processes, and responds to input. Unlike traditional AI agents, which are rules-based and task-oriented, Personic Agents operate under general orientations guided by their personas.
The persona serves as a dynamic layer of user interface, transforming the interaction process. It embodies traits such as empathy, humor, assertiveness, and personality type designation (e.g., MBTI), enabling the agent to respond in ways that feel natural, intuitive, and human-like. For instance, a healthcare agent might be defined as highly skilled with deep professional patient care experience that causes it to adopt a conciliatory tone when interacting with anxious patients, tailoring its responses to the user’s behavioral state.
Personic Agents also excel in affective modulation. They can interpret user sentiment, such as frustration or excitement, and adjust their tone and behavior accordingly. This capacity can foster trust and engagement, transforming AI systems from impersonal tools into relational partners. By mirroring human communication styles, Personic Agents lower the cognitive barrier to interacting with technology, making it accessible to a broader range of users.
IV. Rules-Based Agents vs. Personic Agents: What Sets Them Apart?
The contrast between rules-based agents and Personic Agents highlights a fundamental evolution in AI behavior. Rules-based agents operate deterministically, following explicit instructions programmed by developers. They are reliable for narrowly defined tasks but lack flexibility, adaptability, and emotional nuance.
Personic Agents, on the other hand, derive their behavior from general orientations embedded in their personas, as well as ongoing input from interactions. These orientations provide broad guidelines rather than fixed instructions, allowing the agent to interpret and adapt dynamically to each interaction. The result is behavior that feels emergent, intuitive, and contextually appropriate.
For example, a rules-based customer service agent might provide a scripted response to a delayed delivery query: “Your package is delayed. Please check back later.” While factually correct, the response fails to address the user’s frustration. A Personic Agent in the same scenario, guided by a persona characterized by empathy and helpfulness, might respond: “I understand how frustrating it is to wait for a package. Let me check the latest update for you and notify you as soon as it’s out for delivery.” This shift can transform the interaction, potentially making it more relational and engaging.
Rules-based systems treat every interaction as a discrete event, whereas Personic Agents build relational continuity. By remembering user signals and interaction history, they develop personalized engagement patterns, sometimes in unexpected ways. This evolution from rigid rules to dynamic orientations enables Personic Agents to navigate uncertainty and complexity with unprecedented flexibility.
V. Emergent Possibilities in Interaction
Personic Agents are uniquely positioned to exhibit emergent behaviors— unpredictable yet consistent patterns that arise from the interplay between their personas and real-time interaction data. These behaviors make interactions feel spontaneous and deeply contextual, enhancing the user experience. They can also extend the relational dynamic beyond a purely transactional model that is algorithmic in nature, adding interactions to the mix which might never be anticipated in a purely rules-based interaction.
For instance, a Personic Agent designed as an educational tutor might begin offering unscripted motivational comments during challenging lessons, even if such behavior was not explicitly programmed into its repertoire. This emergent quality arises from the agent’s ability to align its responses with its persona’s orientation. The result is an interaction that can feel more human and collaborative, potentially fostering engagement and trust.
Emergence also expands the creative potential of Personic Agents. In collaborative scenarios, these systems can propose novel ideas or solutions that reflect their adaptive synthesis of user input and contextual understanding. Personic Agents may be able to access little known or obscure information from their model’s training, and then combine it with additional input from human users, which aggregates over the course of a conversation. Their store of static information and dynamic information from interactions can then be synthesized and combined in completely unexpected ways. For example, a design assistant might suggest unconventional yet relevant options for a project, inspiring new directions and possibilities.
See the Appendix below for transcripts of three similar conversations with three different agents, for a real world example of the differences that Personic agency makes possible.
Conclusion
By adopting the term Personic, we underscore the intrinsic integration of human-like traits into AI systems, distinguishing them from algorithmic “rules-based” agency, as well as the more superficial "persona-based" approach. Personic Agents represent a transformative evolution in our implementation of AI, redefining how machines can be instructed to interact with humans - and each other. As researchers, developers, and businesses embrace this paradigm, Personic Agents offer us even more dimensions for creating personalized, impactful, and humanized AI.
