Agentic Artificial Intelligence

• Agentic AI is defined as a class of autonomous systems that integrate perception, reasoning, planning, memory, and tool use to achieve long-horizon goals.
• These systems employ iterative control loops, enabling proactive adaptation and coordinated multi-agent operations in complex digital and physical settings.
• Agentic AI architectures combine explicit governance, robust feedback mechanisms, and scalable coordination strategies to address challenges in diverse applications like cybersecurity, finance, and industrial automation.

Agentic Artificial Intelligence (Agentic AI) denotes a class of AI systems that move beyond stateless prompt-response generation or fixed task execution toward autonomous, goal-directed systems able to perceive context, reason over observations, plan multistep behavior, act through tools or environments, maintain memory, evaluate outcomes, and adapt over time. In recent literature, the term is used for closed-loop systems rather than for a vague label of “smart automation”: the model is treated as a cognitive controller embedded in a wider architecture of memory, policy, tool mediation, feedback, and often multi-agent coordination, so that execution becomes proactive, persistent, and capable of acting in digital and physical environments rather than merely advisory (Alenezi, 11 Feb 2026, J et al., 17 Apr 2026, V et al., 18 Jan 2026, Mukherjee et al., 1 Feb 2025).

1. Concept and distinguishing criteria

A central boundary condition across the literature is the distinction between agentic AI and conventional generative AI, algorithmic automation, or AI-assisted tooling. Generative systems typically generate content in response to prompts; fixed algorithmic systems execute predefined logic; AI-assisted workflows leave orchestration to the human. Agentic systems instead perform the orchestration loop themselves, sustaining long-horizon goals, deciding what to do next, and adapting when conditions change. This is why recent surveys describe the transition as a move from reactive systems to proactive, autonomous, goal-directed systems, and from prompt-driven assistance to systems that can reason, plan, act, and revise over time (Mukherjee et al., 1 Feb 2025, Aldridge et al., 23 Apr 2026, Bandara et al., 27 Jan 2026).

Several works also treat agenticness as a spectrum rather than a binary label. A typological account grounds it in interactivity, autonomy, adaptability, and normativity, while a broader survey distinguishes a single AI agent from Agentic AI as the wider architectural and research paradigm, often realized through multi-agent orchestration rather than one self-contained worker. In this framing, an individual agent may be one executable unit, but agentic AI denotes the design principle by which systems exhibit agency through memory, tool use, planning, environmental interaction, and coordination (Wissuchek et al., 7 Jul 2025, Ali et al., 29 Oct 2025).

A recurrent misconception is that agentic AI is simply “better prompting.” Architectural work rejects that reduction explicitly, arguing that agency emerges when a model is embedded inside an iterative control loop with persistent state, policy checks, execution mediation, and feedback, rather than being invoked as a one-shot text generator (Alenezi, 11 Feb 2026).

2. Architectural foundations and control loops

One formalization models an agentic system as a partially observed control process:

$$\mathcal{A} = \langle \mathcal{S}, \mathcal{O}, \mathcal{M}, \mathcal{T}, \pi \rangle$$

with partial observation

$O_t = \Phi(S_t),$

memory update

$M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$

latent reasoning trace

$Z_t \sim P_\theta(Z_t \mid M_t, O_t),$

and action selection

$A_t \sim \pi_\theta(A_t \mid Z_t, M_t).$

This formulation makes the defining point precise: perception, memory, reasoning, and action are not separate conveniences but coupled stages of an iterative loop in which environment state, internal state, and tool-mediated behavior co-evolve (V et al., 18 Jan 2026).

Architectural surveys further decompose production-grade systems into layered components. A representative reference architecture separates a human or interface layer, agent core, control layer, memory layer, tooling layer, governance and observability layer, and external environment. Its key principle is the separation of cognition from execution: the LLM proposes actions or subgoals, while actual side effects are mediated by typed tool interfaces with schemas, versioning, permissions, and sandboxing. The generic loop repeatedly builds context, plans a step, checks policy, executes tools when appropriate, updates state, writes memory, and stops or fails safe under explicit control logic (Alenezi, 11 Feb 2026).

Edge-oriented work describes closely related modules—perception, memory, reasoning, and action—connected by feedback and retrieval, while architecture taxonomies for LLM agents organize the design space around Perception, Brain, Planning, Action, Tool Use, and Collaboration. This suggests a relatively stable architectural core across enterprise, embodied, and edge settings: continuous perception–reasoning–action loops, persistent memory, external tool mediation, and explicit control mechanisms that bound autonomy while preserving adaptivity (Zhang et al., 26 Aug 2025, V et al., 18 Jan 2026).

3. Coordination, workflows, and multi-agent organization

A domain-specific but influential operationalization appears in business process development, where agentic AI is defined as a goal-driven, object-centered, multi-agent process model. In that formalism, an agent is represented as

$$\mathbf{Agent} = \mathbf{(aID, C_a, OT_a, OR_a, OF_a, g_a)},$$

a goal as

$\mathbf{g} = \mathbf{(gID, O_g, A_g)},$

and a merge goal by

$\mathbf{OG_g} = U_i \mathbf{OF_i}.$

The business process becomes a goal graph rather than a task graph: goals are nodes, agents are arcs, trigger objects induce execution order, and workflows are non-deterministic because multiple agents may provide alternative or collaborative paths to the same goal. This is a declarative reformulation of process automation, replacing rigid task sequences with object-mediated goal satisfaction (AzariJafari et al., 29 Jul 2025).

Educational systems exhibit a parallel logic at a different scale. The Agentic Unified Student Support System (AUSS) organizes a Student Agent, Educator Agent, and Institution Agent around a shared four-module loop of Perception, Reasoning, Action, and Evaluation. Its architecture is explicitly event-driven, with real-time information exchange across agents; a change detected at the student level can trigger educator alerts and institutional risk monitoring. The same paper uses a Q-learning style update to operationalize adaptation, making the loop of perception, decision, action, reward, and policy revision explicit (J et al., 17 Apr 2026).

Production taxonomies generalize these patterns into reusable multi-agent topologies: orchestrator–worker, router–solver, hierarchical command structures, and swarm or market-like systems. Each topology carries characteristic failure modes. The orchestrator can become a bottleneck or single point of failure; routers can misclassify tasks; hierarchical systems can suffer command distortion; swarms can exhibit herding, collusion, or unpredictability. For that reason, recent architectural work emphasizes explicit communication contracts, typed message formats, authority rules, heartbeats, backpressure, revocation, and escalation paths rather than relying on unconstrained natural-language interaction among agents (Alenezi, 11 Feb 2026).

At larger scales, the proposed Internet of Agentic AI (IoAI) extends bounded multi-agent systems into an open ecosystem in which heterogeneous agents discover one another, negotiate responsibilities, exchange context, invoke tools, and execute workflows across cloud, edge, device, organizational, and cyber-physical environments. The workflow lifecycle is described in four phases: discovery and negotiation; task allocation and delegation; execution and monitoring; composition and adaptation. This reframes multi-agent coordination as a distributed systems problem involving identity, trust, capability discovery, task semantics, governance, and incentives, not merely message passing (Zhu, 11 Jun 2026).

Embodied and bandwidth-constrained settings introduce an additional coordination problem: how much information should be exchanged at each subtask. HiTOC addresses this by combining a high-level planner, a low-level actor, and subtask-conditioned communication so that only subtask-relevant information is transmitted. On MAP-THOR, it reported a $+5.5\%$ success rate over ATROC under AWGN and $+11\%$ under Rayleigh fading at $O_t = \Phi(S_t),$0 SNR, while increasing transmitted bits by only about $O_t = \Phi(S_t),$1 relative to ATROC and VAE and reducing transmitted bits by $O_t = \Phi(S_t),$2 compared with JPEG2000. The result is a concrete instance of hierarchical agentic reasoning coupled to hierarchical communication (Huang, 20 Jan 2026).

4. Domain-specific instantiations and deployment settings

Application-level work shows that Agentic AI is not a single domain solution but a family of architectures whose concrete form depends on workflow structure, latency, governance, and environmental constraints.

Domain	Representative system	Distinctive features
Business processes	Goal-object-agent BP model (AzariJafari et al., 29 Jul 2025)	Goals replace fixed task sequences; split and merge goals support non-deterministic workflows
Education	AUSS (J et al., 17 Apr 2026)	Student, Educator, Institution agents; $O_t = \Phi(S_t),$3 recommendation Top-1 accuracy, $O_t = \Phi(S_t),$4 grading match rate, $O_t = \Phi(S_t),$5 risk detection F1-score
Industrial automation	Intent-based industrial automation (Romero et al., 5 Jun 2025)	Natural-language intent decomposition into expectations, conditions, targets, context, and information; PoC on CMAPSS with Google ADK and Gemini 2.0 Flash
Finance	Financial survey and trading architectures (Aldridge et al., 23 Apr 2026)	Trading, portfolio management, risk management, compliance; TIPP-MADDPG annual return $O_t = \Phi(S_t),$6, Sharpe $O_t = \Phi(S_t),$7
Cybersecurity	Defensive and dual-use agentic workflows (Lazer et al., 8 Jan 2026)	SOC triage agent with F1 $O_t = \Phi(S_t),$8 and MTTR $O_t = \Phi(S_t),$9 minutes; IDS agent with AUROC $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$0, F1 $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$1
Edge intelligence	Edge general intelligence and agentification (Zhang et al., 26 Aug 2025)	Distributed agents under 6G/IoT constraints; ACR improves task success by up to $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$2 and reduces communication by up to $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$3

Industrial automation makes the shift especially visible. In an intent-based framework, a human operator states what is to be achieved in natural language, a root agent decomposes the request into expectations, conditions, targets, context, and information, and specialized sub-agents invoke tools such as get_engine_data_json, predict_engine_rul, suggest_maintenance_action, and schedule_maintenance_task. In the reported predictive-maintenance proof of concept, the system produced actions such as MONITOR, REPAIR, and STOP, illustrating the move from protocol-heavy human-machine interaction to intent-mediated orchestration (Romero et al., 5 Jun 2025).

Financial work presents agentic AI as a qualitative departure from both fixed-strategy algorithmic trading and prompt-driven generative assistance. The cited architectures integrate reasoning, planning, memory, reflection, and multi-agent coordination for trading, portfolio management, risk control, and compliance. Examples include FS-ReasoningAgent, which separates fact reasoning and subjectivity reasoning, and FINMEM, which couples profiling, layered memory, and decision modules (Aldridge et al., 23 Apr 2026).

Deployment studies broaden the picture beyond application silos. IoAI work spans cloud agents, edge or fog agents, on-device agents, federated agents, hybrid or serverless agents, and agent economies (Zhu, 11 Jun 2026). A separate survey argues that agentic AI may reduce reliance on very large public cloud infrastructures and favor edge computing, on-premises computing, private or specialized cloud environments, and hybrid architectures because of local processing needs, reduced data consumption footprints, cost savings, and the requirement that agents continue operating during connectivity gaps (Murad et al., 20 Sep 2025).

5. Governance, security, liability, and socio-technical implications

The central governance claim in the security literature is that autonomy changes the threat model. Because agentic systems can take initiative, plan multi-step actions, select tools, interact with APIs, and adapt with little or no continuous human supervision, the attack surface expands from the model to the full stack of infrastructure, data pipelines, runtime execution, tools, permissions, logs, and governance processes. To address this, the MAAIS framework proposes a lifecycle-aware, defense-in-depth, zero-trust-oriented architecture built around CIAA—Confidentiality, Integrity, Availability, and Accountability—and seven interdependent layers: Infrastructure Security, Data Security, Model Security, Agent Execution and Control, Accountability and Trustworthiness, User and Access Management, and Monitoring and Audit. Its validation is preliminary and based on mapping to MITRE ATLAS tactics rather than on large-scale empirical deployment (Arora et al., 19 Dec 2025).

Cybersecurity surveys extend this analysis by identifying systemic failure modes particular to agentic workflows: collusion and covert coordination, cascading failures through shared memory or tools, oversight evasion, memory poisoning, prompt injection, unsafe tool invocation, and resource-abuse attacks that exploit long reasoning loops or excessive tool calls. The practical conclusion is that agentic AI is presently safest as bounded augmentation under layered defenses, immutable logging, sandboxing, identity controls, narrow task scopes, and human escalation for high-impact actions (Lazer et al., 8 Jan 2026).

Legal and ethical work emphasizes a corresponding accountability problem. Once systems can negotiate, purchase, schedule, deploy, or otherwise act without iterative human approval, responsibility can become diffuse across users, developers, providers, and organizations. One analysis uses the notion of the “moral crumple zone” to describe situations in which blame collapses onto the nearest visible human even though the autonomous system substantially shaped the outcome. The same paper also links agentic AI to authorship ambiguity, tacit collusion risks in two-sided algorithmic markets, and the prospect of an “algorithmic society” in which machine-mediated coordination increasingly structures social and economic life (Mukherjee et al., 1 Feb 2025).

Socio-technical analysis argues that these issues are not externalities added after the architecture is built. Perception, cognition, planning, execution, and memory each introduce dependencies related to privacy, bias, transparency, accountability, safety, and sustainability. To organize that relation, one paper proposes the MAD–BAD–SAD lens: motivations, applications, and dilemmas; biases, accountability, and dangers; societal impact, adoption, and design. The same work distinguishes “Agentic AI for Society” from “Agentic AI on Society,” underscoring that deployment contexts, institutions, and social norms partly constitute the system rather than merely constrain it from outside (Donta et al., 26 Dec 2025).

Insurance analysis translates these distinctions into underwriting language. It frames agentic AI as a continuum of autonomy, delegated authority, and external action capability, with the decisive boundary being whether a system merely produces information or can independently generate insured events by modifying external state. On that basis, the paper argues that the future insurance market will not center on a single monoline product but on a coordinated architecture spanning cyber insurance, technology E&O, product liability, performance warranty, and affirmative AI-liability coverages (Zhu, 3 Jun 2026).

6. Typologies, evaluation, and research trajectories

Recent survey work has challenged the tendency to treat all agentic systems as variants of a single lineage. A PRISMA-based review of $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$4 publications proposes a dual-paradigm framework that separates a Symbolic/Classical lineage, relying on algorithmic planning and persistent explicit state, from a Neural/Generative lineage, relying on stochastic generation and prompt-driven orchestration. It argues that paradigm choice is strategic: symbolic systems dominate safety-critical domains such as healthcare, neural systems dominate adaptive and data-rich environments such as finance, and hybrid neuro-symbolic architectures constitute a central research direction (Ali et al., 29 Oct 2025).

A complementary line of work argues that current foundation-model-based systems remain under-structured unless they are “agentified” with explicit models of cognition, cooperation, and governance. Drawing on the AAMAS tradition, it reintroduces BDI architectures, communication protocols such as KQML and FIPA-ACL, mechanism design, trust and reputation models, negotiation and argumentation, and institutional modeling of obligations, permissions, prohibitions, and roles. The core claim is that practical autonomy requires explicit commitments and social structure, not only behavioral flexibility (Dignum et al., 21 Nov 2025).

More operational typologies attempt to classify existing systems by degree and form of agency. One framework defines eight ordinal dimensions—knowledge scope, perception, reasoning, interactivity, operation, contextualization, self-improvement, and normative alignment—and then reduces them to cognitive agency and environmental agency. From these two axes it derives four constructed types: Simple agents, Research agents, Task agents, and Complex agents. The underlying claim is that “agenticness” is best analyzed as a multidimensional spectrum, not as a yes-or-no label (Wissuchek et al., 7 Jul 2025).

Evaluation work increasingly argues that accuracy alone is inadequate. One architecture survey proposes the CLASSic frame—Cost, Latency, Accuracy, Security, and Stability—and highlights benchmarks such as GAIA, SWE-Bench Verified, SWE-Bench Pro, OSWorld, OSWorld Verified, $M_t = \mu(M_{t-1}, O_t, Z_{t-1}, E_{t-1}),$5-bench, FrontierMath, AgentBench, and MultiAgentBench. Across these evaluation efforts, the most persistent open problems are hallucination in action, infinite loops, prompt injection, interoperability, verifiability, and safe autonomy. Architectural research therefore points toward shared protocols, typed contracts, registries, auditable control planes, and layered governance as the infrastructural basis of scalable and composable autonomy rather than isolated chatbot-like systems (V et al., 18 Jan 2026, Alenezi, 11 Feb 2026).