Agentic AI: Orchestrating the Future of Intelligent Systems

Latest 52 papers on agentic ai: Aug. 17, 2025

Agentic AI is rapidly transforming from a theoretical concept into a practical reality, promising a new era of autonomous, collaborative, and highly capable artificial intelligence. These systems, defined by their ability to reason, adapt, and interact dynamically, are moving beyond mere automation to reshape how we tackle complex challenges across diverse domains. From cybersecurity to healthcare and urban planning, recent research highlights the profound impact and ongoing innovations in this exciting field.

The Big Idea(s) & Core Innovations

The core innovation across recent agentic AI research lies in empowering AI systems with enhanced autonomy, collaboration, and adaptive decision-making capabilities. A major theme is the shift from single-purpose, pre-programmed AI to self-governing agents that can respond dynamically to complex environments and achieve higher-level goals. This requires robust communication, ethical alignment, and sophisticated reasoning.

One critical area of focus is communication and interoperability within multi-agent systems. The paper “Agentic AI Frameworks: Architectures, Protocols, and Design Challenges” by P. P. Ray, G. Research, Agent Network Protocol Contributors, A. Ehtesham, A. Singh, G. K. Gupta, and S. Kumar emphasizes the need for standardized protocols for effective agent-to-agent (A2A) communication. Building on this, “Towards Multi-Agent Economies: Enhancing the A2A Protocol with Ledger-Anchored Identities and x402 Micropayments for AI Agents” from A. Vaziry et al. (University of California, Santa Barbara) introduces a novel architecture that uses blockchain-based AgentCards and x402 micropayments to enable decentralized agent discoverability and secure, verifiable transactions, laying the groundwork for autonomous AI economies.

Another significant thrust is AI safety and ethical alignment. Nell Watson et al. (University of Gloucestershire) in “Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values” propose a “superego” agent that aligns AI behavior with diverse human values through personalized “Creed Constitutions,” demonstrating significant reductions in harmful outputs. W. Zeng et al. (Hunan University, China) further delve into this with “Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives”, highlighting value alignment as a systemic governance issue critical for domains like finance and healthcare. The importance of ethical frameworks is also explored in “Advancing Responsible Innovation in Agentic AI: A study of Ethical Frameworks for Household Automation”, emphasizing a multidisciplinary approach to ensure user trust and prevent misuse.

Addressing security and resilience is paramount for agentic AI. “MI9 – Agent Intelligence Protocol: Runtime Governance for Agentic AI Systems” by Charles L. Wang et al. (Barclays, AI Governance, Columbia University) introduces MI9, a groundbreaking runtime governance framework that provides real-time controls and monitoring for safe deployment, tackling dynamic alignment risks. Similarly, “QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI” by Hammad Atta et al. (Qorvex Consulting) identifies and proposes solutions for “Cognitive Degradation”—internal failures in agentic AI like memory starvation and planner recursion, ensuring system robustness.

Beyond foundational challenges, agentic AI is unlocking new applications. Sameer Patel (Research Institute for AI and Cybersecurity) showcases this with “MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection”, where a novel Multi-Agent Coordination Protocol (MCP) enhances real-time disinformation detection. In medical AI, “Agentic AI framework for End-to-End Medical Data Inference” from M. Marks et al. (Harvard University) develops a framework for privacy-preserving medical data analysis, compliant with HIPAA and GDPR.

Under the Hood: Models, Datasets, & Benchmarks

The advancements in agentic AI rely heavily on sophisticated models, robust datasets, and rigorous benchmarks. Here’s a glimpse into the key resources driving this progress:

• GLM-4.5 (355B parameters) and GLM-4.5-Air (106B parameters): Introduced by the GLM-4.5 Team (Zhipu AI & Tsinghua University) in “GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models”, these Mixture-of-Experts (MoE) based large language models excel in agentic, reasoning, and coding tasks, outperforming many open-source models. The authors provide a toolkit for reproducibility at https://github.com/zai-org/glm-simple-evals.
• MAPS (Multilingual Benchmark for Agentic AI): From Omer Hofman et al. (Fujitsu Research of Europe, Fujitsu Limited, Cohere) in “MAPS: A Multilingual Benchmark for Global Agent Performance and Security”, MAPS extends four widely used benchmarks into eleven typologically diverse languages to evaluate performance and security gaps in multilingual settings. The dataset is available on Hugging Face: https://huggingface.co/datasets/Fujitsu-FRE/MAPS.
• AIDev Dataset: Curated by Hao Li et al. (Queen’s University, Kingston) in “The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering”, AIDev is a large-scale dataset containing 456,535 Agentic-PRs from five leading autonomous coding agents on GitHub. Code is accessible at https://github.com/SAILResearch/AI_Teammates_in_SE3.
• REPRO-BENCH & REPRO-AGENT: Introduced by Chuxuan Hu et al. (University of Illinois Urbana-Champaign, Shanghai Jiao Tong University, University of Chicago) in “REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research?”, this benchmark evaluates agentic AI systems for assessing reproducibility in social science research, with code available at https://github.com/uiuc-kang-lab/REPRO-Bench.
• NetMoniAI Framework: P. Zambare et al. (Indian Institute of Technology Bombay (IITB)) contribute “NetMoniAI: An Agentic AI Framework for Network Security & Monitoring”, an open-source framework leveraging LLMs for real-time network security and anomaly detection. Code can be found at https://github.com/pzambare3/NetMoniAI.
• AURA (Multi-Modal Medical Agent): In “AURA: A Multi-Modal Medical Agent for Understanding, Reasoning & Annotation”, N. Fathi et al. propose an agentic AI system for medical imaging analysis, capable of segmentation, counterfactual generation, and self-evaluation. Related code is on Hugging Face: https://github.com/huggingface/smolagents.
• GenoMAS (Agentic AI System for Transcriptomic Analysis): Keeee Chen (University of California, San Francisco) presents this system in “Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI”, enabling large-scale transcriptomic analysis to uncover disease relationships. Code is available at github.com/KeeeeChen/Pathway_Similarity_Network.

These resources are not just academic contributions; they are foundational tools that enable empirical validation, foster open research, and accelerate the development and deployment of agentic AI systems across various industries.

Impact & The Road Ahead

The research in agentic AI is pushing the boundaries of what autonomous systems can achieve, promising transformative impacts across industries. From accelerating drug discovery with systems like Tippy from Yao Fehlis et al. (Artificial, Inc.) in “Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation”, to enhancing cybersecurity with intelligent threat responses (as discussed in “Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats” by Quanyan Zhu, New York University (NYU)), the shift towards goal-oriented, self-managing AI is profound.

Agentic AI also promises to revolutionize critical services like elderly care, as explored in “Redefining Elderly Care with Agentic AI: Challenges and Opportunities”, leading to personalized assistance and health monitoring. In software engineering, autonomous coding agents (as seen in “The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering”) are reshaping workflows and productivity, while tools like AutoCodeSherpa by Sungmin Kang et al. (National University of Singapore) in “AutoCodeSherpa: Symbolic Explanations in AI Coding Agents” aim to improve trust and interpretability in AI-generated code.

However, the road ahead is not without its challenges. “Prompt Injection 2.0: Hybrid AI Threats” by Jeremy McHugh et al. (Preamble, Inc.) highlights the evolving landscape of adversarial attacks on agentic systems, stressing the need for robust runtime security. Similarly, “Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System” (Author et al.) calls for proactive risk assessment tailored to AI-driven security operations. Ethical considerations, including multi-level value alignment and privacy norms (as discussed in “Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective” by Sarah Tran et al., University of Washington), remain central to responsible AI deployment.

The future of agentic AI points towards increasingly dynamic, adaptable, and human-aligned systems. Concepts like “From Autonomy to Agency: Agentic Vehicles for Human-Centered Mobility Systems” by Jiangbo Yu (McGill University) envision a future where vehicles reason and interact ethically. The integration of generative AI in complex applications, such as “Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning” by Haowen Xu et al. (UNSW Sydney), further illustrates the broad potential. Ultimately, the emphasis is on creating a scalable, secure, and ethical “Web of Agents” as envisioned by “From Semantic Web and MAS to Agentic AI: A Unified Narrative of the Web of Agents”, where human-AI cocreation thrives, supported by sophisticated orchestration and trust mechanisms. The rapid pace of innovation suggests that agentic AI will continue to be a cornerstone of future intelligent systems, unlocking unprecedented capabilities while necessitating diligent attention to safety and societal impact.