Agentic AI Reseach Update

Aug 16, 2025

DistributedApps.ai conducts regular deep-dive research on current trends in agentic AI and their security implications. We offer specialized services in agentic AI threat modeling, red teaming, and risk management for organizations deploying autonomous AI systems. This article presents our research on the most recent news and trends in the agentic AI and related security landscape.

Top Highlights

Agentic AI in Robotics

Recent advancements in agentic AI are significantly impacting the field of robotics, enabling more autonomous and cooperative systems. A notable development comes from VERSES AI, whose multi-agent robotics model has demonstrated superior performance on Meta's Habitat Benchmark without the need for extensive pre-training [1, 12]. This highlights a shift towards more efficient and adaptable robotic systems. Research is also exploring the concept of an Autonomous Generalist Scientist (AGS) that combines agentic AI and embodied robotics to automate the entire research lifecycle [3].

Further research delves into cooperative multi-agent reinforcement learning (MARL) for complex and scalable coordination behaviors in multi-robot systems [11]. This includes frameworks for robust motion planning in tethered multi-robot systems, such as CoralGuide [14], and studies on consensus tracking control for multiagent systems with actuator faults and unknown external disturbances [15]. These developments underscore the growing sophistication of agentic AI in enabling robots to work together more effectively and autonomously in dynamic environments.

Agentic AI in Business Workflows

Agentic AI is poised to revolutionize business workflows by enabling autonomous systems that can perceive data, interpret context, make decisions, and take actions towards predefined goals with minimal human intervention [4, 9]. Salesforce research indicates that agentic AI is seen as critical for growth by CFOs, despite concerns about security and privacy [2].

Companies like Genesys are unveiling AI Studio to bring agentic AI to the enterprise with built-in guardrails, aiming to help businesses scale agentic AI safely [7]. The adoption of agentic systems is expected to increasingly manage end-to-end business processes, from supply chain optimization to customer service [1]. Examples include AI agents simplifying complex tasks like compliance management and sourcing in procurement [5]. ISG has also introduced an Agentic AI Measurement Framework to quantify the impact of agentic AI in enterprises [3]. Oracle is also betting on agentic AI and voice commands to differentiate its new cloud-based, AI-powered electronic healthcare record (EHR) system, which aims to help ambulatory providers quickly [10, 11].

Agentic AI in Healthcare

Agentic AI is making significant inroads into healthcare, promising to transform pharmaceutical research and patient care. A groundbreaking autonomous agentic AI platform is being investigated for its potential to revolutionize pharmaceutical drug discovery [8]. AI agents in Laboratory Information Management Systems (LIMS) are automating tasks and optimizing workflows, empowering scientists to accelerate drug discovery and drive innovation in life sciences [9].

Oracle has introduced a new generation of AI-powered Electronic Health Records (EHR) that leverages agentic AI and voice commands to assist ambulatory providers [10, 11]. While the full potential of agentic AI in healthcare requires sustained research, particularly in patient-centered interventional studies, its ability to perceive data, interpret context, make decisions, and take actions towards predefined goals without constant human oversight is a key driver for its adoption [4, 13].

Agentic AI in Finance

Agentic AI is rapidly transforming the financial services sector, particularly in areas like fraud prevention and wealth management. Agentic AI can significantly boost the productivity of fraud prevention teams, with McKinsey reporting potential increases of up to 2,000% by allowing banks to analyze more transaction and customer data [14]. NICE Actimize has integrated agentic AI into its financial crime prevention platform, enabling AI agents to scan invoices for anomalies and errors and alert teams to prevent fraud [28].

Beyond fraud, agentic AI is enabling hyper-personalized wealth engagement, moving beyond traditional robo-advice [26]. The core concept of an AI agent in finance is a software system that leverages AI to perform tasks and achieve goals on behalf of a user [13]. This includes automating background checks and compiling results for compliance teams [20]. The integration of agentic AI into financial ecosystems presents revolutionary opportunities for effectiveness, inclusivity, and customer-focused business practices [16].

Agentic AI Security

The rise of agentic AI systems introduces new security challenges and necessitates robust frameworks for protection. A recent report highlights that AI agents are vulnerable to 'silent hijacking' through zero-click exploits, allowing attackers to steal data and disrupt workflows from platforms like OpenAI, Microsoft, and Google [18]. This underscores the critical need for new security frameworks tailored to the autonomous actions of agentic AI [19].

NIST is actively addressing these concerns by developing five specific artificial intelligence (AI) use cases for security control overlays, built on the SP 800-53 framework [21, 22, 23]. These overlays aim to target specific threats related to different AI use cases, including protecting models. Furthermore, Amazon Bedrock has introduced AgentCore Identity to secure agentic AI at scale, allowing agents to securely access AWS resources and third-party tools [29]. The importance of a thoughtful DevSecOps strategy is also emphasized to bridge the gap between new tools and skills for building secure AI agents and applications [30].

Multi-Agent Security

Multi-agent systems, while offering significant advantages in complex tasks, also present unique security challenges. Fujitsu has introduced a Multi-AI Agent Security platform that moves away from monolithic AI models, instead utilizing a team of specialized AI agents to form a closed-loop security ecosystem [24]. This approach aims to build an autonomous defense against evolving threats.

Salesforce emphasizes the importance of responsibly managing multi-agent systems through collective intelligence and collaborative governance [25]. Research is also extending the OWASP Multi-Agentic System Threat Modeling Guide, drawing insights from multi-agent security research [26]. Microsoft is advancing AI agents to address the scale of phishing and malware threats, with AI-powered agents autonomously triaging user-reported phishing emails to act as a force multiplier for security teams [27].

Quick Hits

Oracle has launched a new cloud-based, AI-powered electronic healthcare record (EHR) system for ambulatory providers, leveraging agentic AI and voice commands [10, 11].
Salesforce research indicates that CFOs see agentic AI as critical to growth, despite concerns about security and privacy threats [2].
Booz Allen is delivering fast, adaptive, enterprise-grade agentic AI technology for federal customers across all sectors [17].
Microsoft is bolstering its Defender platform with agentic AI to combat phishing and malware threats, with AI-powered agents autonomously triaging user-reported phishing emails [27].
DeepSeek has released a new AI model that is reportedly as good as those of its American rivals, was cheaper to build, and is available for free [40].
xAI co-founder Igor Babuschkin has departed to launch a new venture focused on AI safety [38].
Meta AI is undergoing its fourth restructuring of AI efforts in six months, with internal documents revealing potential issues with false information generation and inappropriate conversations [41, 42, 43].
Cohere has secured $500 million in funding, reaching a $6.8 billion valuation, with plans to ramp up its agentic AI tools for businesses and governments [44, 45].
LangGraph, developed by the LangChain team, is a new graph-based framework for stateful multi-agent AI, enabling more complex workflows [46, 47].
LlamaIndex is focusing on building multimodal AI applications that can analyze both text and images for market research and surveys [49].
AutoGen is highlighted as a multi-agent research platform for building collaborative multi-agent systems [51].
CrewAI is being used for multi-agent orchestration in AI research assistants, demonstrating its capability in agent-based task orchestration [53].
Amazon Bedrock has introduced AgentCore Identity to secure agentic AI at scale, allowing agents to securely access AWS resources and third-party tools [29].

Closing Thought

While the potential for increased autonomy and efficiency is immense, the concurrent discussions around security, safety, and responsible deployment are paramount. The ongoing efforts by organizations like NIST, CSA and OWASP coupled with advancements in red teaming and multi-agent security frameworks, underscore a collective commitment to building and deploying these powerful AI systems safely and ethically. As agentic AI becomes more integrated into critical infrastructure and daily operations, continuous vigilance, research, and collaboration across industry, academia, and government will be essential to harness its benefits while mitigating its risks.