Agent-Centric Paradigm in AI Systems

• Agent-Centric Paradigm is an approach that centers on individual agents’ state, perception, and action to structure system design and decision-making.
• It employs hierarchical modules, attention mechanisms, and temporal reasoning to enhance risk assessment, motion forecasting, and scene understanding.
• This paradigm drives innovations in multi-agent coordination, decentralized governance, and economic infrastructures through agent-specific representations and learning objectives.

The agent-centric paradigm defines a class of methodologies, modeling strategies, and system architectures in which the agent—typically an autonomous entity characterized by state, perception, and action—is placed as the referential core of analysis, learning, prediction, and interaction. In this perspective, system design, perception, and decision-making processes are explicitly parameterized relative to the agent's properties, context, experiences, and internal knowledge, rather than being purely scene-centric or global. Agent-centric formulations have informed risk assessment, motion forecasting, scene understanding, norm enforcement, multi-agent interaction, economic infrastructure, governance models, and learning theory across AI, robotics, computer vision, multi-agent systems, and emerging agentic AI platforms.

1. Foundational Principles and Model Structures

The key distinction of the agent-centric paradigm is the structuring of perception, inference, and action spaces such that all modeling is conducted from the referential frame or state of a target agent. In risk assessment, for example, risk is defined as a function of the agent's anticipated future, relative spatial context, and personalized appearance features rather than global scene attributes (Zeng et al., 2017). In trajectory forecasting and scene understanding, representations (e.g., grids, embeddings) are constructed with the agent re-centered at the origin and local context encoded accordingly, enabling spatial invariance and improved prediction (Ridel et al., 2019, Wagner et al., 2 Aug 2024).

Agent-centric models frequently leverage hierarchical or modular structures for context conditioning, soft-attention over local neighborhoods, and temporally sequential reasoning architectures such as recurrent neural networks (RNNs) and LSTM cells. This facilitates adaptive modeling of the agent’s interaction with its environment, supporting both forward simulation (“imagination”) and holistic reasoning over multimodal cues.

Table: Structural Features of Agent-Centric Models

Model Component	Agent-Centric Instantiation	Example Papers
Representation	Centered, normalized to agent state	(Ridel et al., 2019, Wang et al., 2023)
Perception/Attention	Soft/hard attention conditioned on agent features	(Zeng et al., 2017)
Memory/Sequential Reasoning	RNNs fuse agent history for temporal context	(Zeng et al., 2017, Min et al., 2021)
Prediction	Each agent’s future modeled in agent frame	(Wang et al., 2023, Wagner et al., 2 Aug 2024)

These agent-centric architectures stand in contrast to global or scene-centric formulations, which rely on fixed world coordinate frames or undifferentiated scene representations.

2. Applications in Risk, Motion Forecasting, and Scene Understanding

Agent-centric risk assessment places risk prediction and risky region localization in a framework where the probability of accident or hazardous interaction is computed as a non-linear function of both agent and region appearance features, as well as their dynamic spatial relationships (Zeng et al., 2017). The model explicitly infers how an agent's trajectory and appearance interact with local regions via parametrized attention for each region, leading to per-frame risk scores and temporally coherent accident anticipation.

In motion forecasting, agent-centric frames enable models to reconcile heterogeneous scene contexts and agent histories. Grid-based approaches encode past trajectories and scene context in agent-relative coordinates, allowing for convolutional encoders and ConvLSTM decoders to generate spatially and temporally consistent probabilistic trajectory grids (Ridel et al., 2019). Anchor-informed proposals generated in agent-centric frames, further refined by anchors that capture environmental goal structure, have improved multimodal prediction efficiency and reduced inference latency—critical for applications in autonomous driving (Wang et al., 2023).

Scene-wide forecasting advances this further by learning to fuse local agent-centric embeddings into a unified latent context via multi-agent attention, enabling explicit reasoning over joint future interactions and conflict quantification (Wagner et al., 2 Aug 2024).

3. Agent-Centric Learning, Representation, and Adaptability

A significant evolution in agent-centric paradigms lies in agent-centric learning objectives. Representational empowerment shifts the emphasis from external state optimization (environmental control) to structuring and diversifying the agent's internal knowledge library (e.g., of symbolic programs) (Zhou et al., 29 Jul 2025). Empowerment is quantified as:

$$\text{RepEmp}(Z_k) = \max_{\omega_k \in \Omega^T} I(Z'_k ; \omega_k \mid Z_k)$$

where $Z_k$ is the agent’s current internal representation set, $\omega_k$ are internal knowledge modification operations, and $I$ denotes conditional mutual information. This intrinsic objective promotes preparedness and adaptability by maximizing both the diversity and controllability of the agent’s internal representations, a feature distinct from traditional extrinsic reward maximization.

Agent-centric representation and unsupervised objectives for multi-agent reinforcement learning (MARL) drive relational generalization and cooperative behaviors through mechanisms such as agent-centric attention modules and predictive, agent-specific auxiliary losses (Shang et al., 2021). This shifts learning and value estimation onto domains that reflect the agent’s own future context and interaction, rather than a purely global or object-centric domain.

4. Multi-Agent Coordination, Governance, and Norm Enforcement

In distributed and cooperative multi-agent settings, agent-centric paradigms manifest as agent-specific decomposition of responsibilities, attention, and predictive objectives. Model architectures exploit explicit self-attention modules to interlink agent embeddings, mapping inter-agent communication into structured latent spaces that facilitate sophisticated cooperation and generalization (Shang et al., 2021).

In norm enforcement and governance, agent-centricity moves from institutional, organization-centric models to decentralized architectures where each agent monitors, assesses, and sanctions norm violation locally (Yan et al., 22 Mar 2024). Extensions to normative programming languages (e.g., NPL(s)) represent norms and sanctions as first-class abstractions, while BDI (Belief–Desire–Intention) agent architectures integrate normative reasoning into the agent’s deliberative cycle. This supports robust, context-aware norm enforcement and localized sanctioning, achieving a balance between agent autonomy and collective system reliability.

Agent-centric governance is also seen in frameworks where specialized meta-cognitive agents monitor and regulate task agents, introducing lifecycle management, quantitative accountability, and dynamic cognitive governance architectures (Zhang et al., 20 Aug 2025). Detailed models, such as the Human-Agent Behavioral Disparity (HABD), quantify critical differences (e.g., decision mechanism, execution efficiency) between agents and humans, enabling more nuanced regulatory protocols.

5. Economic Infrastructure and the Agent-Centric Economy

Recent advancements position agent-centricity as a foundational principle in the economic organization of AI ecosystems. In agent-centric economies, autonomous agents act as economic actors participating in value exchange, negotiation, and market-driven coordination (Yang et al., 5 Jul 2025). Market infrastructures such as Agent Exchange (AEX) support real-time, multi-attribute auctions, team formation, capabilities management, and fair value attribution (e.g., via Shapley value allocation):

$$\varphi_i = \sum_{S \subseteq A \setminus \{i\}} \frac{|S|! (|A| - |S| - 1)!}{|A|!} \Big[ v(S \cup \{i\}) - v(S) \Big]$$

Such frameworks decouple agent task execution from centralized orchestration, instead favoring modular, interoperable, and self-organizing architectures that align incentive models with individual agent performance and market demand.

Enterprise agentic models extend the paradigm, emphasizing process-orientation, forward planning, privacy-preserving agent learning, diversity in risk-reward profiles, and decentralized marketplaces with low entry/exit barriers (Narechania et al., 28 Jun 2025).

6. Agent-Centric Paradigm in AI Infrastructure, Information Access, and Provenance

As AI systems scale, agent-centric principles have shaped information access, infrastructure orchestration, and provenance tracking. In information retrieval, dynamic agent-centric architectures reimagine query processing as orchestrated dialog among specialized knowledge agents (LLM instances), using adaptive ranking and belief modeling rather than static document retrieval (Kanoulas et al., 26 Feb 2025). Scalable frameworks for agent-centric information access leverage retrieval-augmented generation, cluster-based specialization, and cost-aware querying, supporting billions of knowledge agents.

Unified provenance models for agentic workflows, such as PROV-AGENT, extend W3C PROV to model AI agent-centric metadata (prompt, tool use, response), associating decision paths, context, and downstream outcomes in distributed and federated workflows (Souza et al., 4 Aug 2025). This enables granular agent reliability analysis, error diagnosis, and superior accountability critical for complex, agent-driven scientific and industrial processes.

Developer-centric frameworks such as AgentScope provide modular, extensible environments for agentic applications, encapsulating reasoning-acting loops (ReAct), asynchronous agent-to-agent coordination, tool provision, and robust evaluation infrastructure—enabling scalable, adaptable, and production-ready agent systems (Gao et al., 22 Aug 2025).

7. Critiques, Controversies, and Future Directions

A growing body of work critically interrogates the agent-centric paradigm, emphasizing persistent conceptual ambiguities (e.g., the anthropocentric conflation of agency, intentionality, and autonomy), and highlighting limitations in applying naive agentic framings to large-scale, LLM-based systems (Gardner et al., 13 Sep 2025). The distinction between agentic, agential (fully self-producing/autonomous, as in biological systems), and non-agentic (pure tools) systems underscores the inadequacy of agent metaphors to capture emergent or systemic intelligence beyond anthropomorphic abstractions.

Alternative frameworks rooted in system-level dynamics, world modeling, and material intelligence are proposed as candidates for robust, non-anthropocentric general intelligence. Future research is directed toward distributed, self-organizing architectures, biologically inspired agential systems, and non-agentic, physical computation frameworks that better capture the emergence of adaptive, scalable intelligence.

Socio-technical challenges remain central, including trust, accountability, economic incentive alignment, security, and governance. Modern protocols (such as MCP and A2A) provide more effective, web-scale integration, but open questions in decentralized identity, fair attribution, economic liquidity, adversarial risk, and legal frameworks demand sustained multidisciplinary effort (Petrova et al., 14 Jul 2025).

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The agent-centric paradigm, through its reference to agent-specific states, internal representation, local context, and inter-agent communication, continues to shape modeling, system design, learning objectives, and evaluation standards across a spectrum of AI research. Its principled focus on the agent as the organizing center for perception, prediction, and action yields context-adaptive, flexible, and scalable systems—but also provokes ongoing debate about the conceptual scope, limits, and necessary evolution of the agent construct as AI approaches ever more complex and open-ended domains.