I investigated the organizational implementation, global trends, and security risks of autonomous AI agents.
The Redefinition of "Labor" Brought About by Autonomous AI Agents
The widespread adoption of Large Language Models (LLMs) in the early 2020s brought significant benefits to operational efficiency in corporate activities.
However, as of 2026, the technological trend has undergone a decisive paradigm shift from "passive tools that generate text and code according to human instructions" to "an active workforce that understands goals, creates plans, and autonomously executes tasks across multiple systems," also known as "Agentic AI."
This change signifies a fundamental transformation that shakes the foundations of corporate organizational structure, human resources strategy, and cybersecurity.
According to definitions by CISA (Cybersecurity and Infrastructure Security Agency) and the Five Eyes, agentic AI is positioned as "a system that fundamentally relies on AI models such as LLMs to interpret and reason about the state of the world, and is capable of making autonomous decisions and taking action."
Due to these three characteristics of "autonomy," "statefulness," and "access rights to tools," AI has evolved into a "digital labor" force.
The market size is also seeing explosive growth; the global AI agent market, estimated at approximately $7.1 billion to $7.8 billion as of 2025, is expected to reach $10.7 billion to $10.9 billion in 2026, and is projected to grow to approximately $47 billion to $53 billion by 2030, with a Compound Annual Growth Rate (CAGR) of 45% to 50%.
In this article, starting with the symbolic case of the establishment of a "fully AI department" by NEC reported by the Nikkei, I will examine how AI agents are becoming the primary subjects of organizational decision-making and task execution.
Furthermore, I will analyze in detail actual operational cases in advanced regions around the world, such as the United States, China, and Israel, and finally, I will comprehensively discuss the new vulnerabilities and security risks brought about by these autonomous systems.
Organizational Transformation Deciphered from NEC's Establishment of a "Fully Unmanned Department"
On August 1, 2026, NEC established a new unmanned department, the "Corporate AI Workforce Department," where only artificial intelligence is enrolled and no humans are assigned.
This initiative reverses the conventional relationship where "humans use AI as a tool" and embodies a highly advanced case of a new division of labor where the subject has shifted to AI, where "the AI agents themselves hold the tasks, run the decision-making processes, and humans receive reports on the results and risks to make final judgments."
Seventeen AI agents, each with their own specific areas of expertise, have been "assigned" to this new department.
What is noteworthy is that this organization is not merely a project, task force, or laboratory, but is given company-wide functions as a permanent corporate department on par with the Human Resources, Legal, and Accounting departments.
Being on the organizational chart means that it has a budget, defined responsibilities, and official business interactions with other departments.
As the name "workforce" suggests, it is equivalent to declaring AI itself as a component of the workforce that should be systematically operated at the company-wide level.
Interface Design Through Personification and Role Assignment
NEC has assigned the 17 AI agents names and titles that embody their areas of responsibility, such as "AI Zaiten Shu (Finance, ROI, and Investment Decision Specialist)," "AI Kenda Ko (Cost Optimization Knowledge Gathering Specialist)," and "AI Kanjyo Jitsu (Management Data Analysis Specialist)." This is based on a highly rational information architecture design for practical business.
In a human organization, if one can refer to someone by saying, "For financial ROI calculations, ask Zaiten-san," the requester does not have to worry about who to ask, and the speed of work improves.
If the agent group only had inorganic identifiers like "Agent A" or "Agent B," humans would not be able to intuitively understand which AI to ask for what.
The assignment of names and titles functions as an essential interface design in a hybrid organization where humans and AI work together, while simultaneously serving as a performance to personify the AI.
A 4-tier management structure to control cost and quality
What is even more noteworthy is that this AI department is not a flat structure, but has a clear four-tier hierarchical structure consisting of "AI Department Head", "AI Executive", "AI Manager", and "Operational AI".
The top-level AI Department Head is responsible for the overall management of the organization and reporting to humans, and AI Executives (such as AI Zaizen Shu) with responsibilities for specific domains are placed below them.
Furthermore, AI Managers (such as AI Kenda Ko) are placed directly above the operational AI (such as AI Kanjyo Jitsu), which are the lowest-level units that perform the actual work, and are designed to "supervise" the performance of the operational AI in terms of both quality and cost.
To the question of why such a hierarchical structure is created, the background lies in "cost control" and "prevention of misjudgment", which are among the biggest challenges in operating generative AI.
In a structure where agents act autonomously and repeatedly perform reasoning by calling other agents or external data as needed, "API costs can easily inflate".
If no one is watching, you won't notice if a runaway process occurs where a model is called dozens of times for a single request.
The design where an AI Manager monitors and evaluates the actions of their subordinates can be called a "sophisticated mechanism to suppress this runaway behavior through the organizational structure itself", by transplanting the boss-subordinate relationship from human organizations directly between AIs.
Implementation and market trends of "digital labor" in the United States
In the United States, the adoption of autonomous AI agents has moved beyond the experimental (pilot) stage and has completely transitioned to the automation of core business operations in actual production environments (production scale).
According to Gartner's projections, by 2028, autonomous AI will be embedded in 33% of enterprise software (a sharp increase from less than 1% in 2024), and "15% of daily business decisions will be made autonomously without human intervention".
Hybrid workforce and full automation of business workflows
Marc Benioff, CEO of Salesforce in the U.S., declared that "digital labor is the new frontier in business", and pointed out that the addition of autonomous AI agents will fundamentally change the operational structure of companies, creating a "hybrid workforce" of humans and digital entities.
This is a transformation that requires entirely new job roles on the human side, such as AI agent management, AI risk and governance, AI operations management, AI training and development, and AI workforce integration.
Companies that develop and deploy autonomous agents, such as PesPrime, are embodying this transformation in actual business settings.
The system provided by PesPrime is distinct from chatbots or rule-based automation tools. The system receives a given objective, determines the necessary steps itself, executes them across multiple tools and data sources, retries if there are errors, and delivers the final output without human intervention.
For example, in the lead generation (prospecting) process for B2B services, their AI agents continuously monitor and identify potential customers from defined sources and score them against qualification criteria.
Then, they automatically generate personalized outreach messages that include prior research context, execute the initial contact, and manage the follow-up sequence.
With this system, which only hands over to a human sales representative once the target has replied, they have succeeded in reducing manual processing time by 60% to 80%, and shortening the time from identifying a potential customer to the initial approach, which used to take 2 to 3 days, to "less than 4 hours".
Penetration of specialized agents in each industry and the evolution of platforms
Deloitte's research report also confirms that the implementation of AI agents is progressing across a wide variety of industries in the United States.
One financial services company is building agent workflows that automatically extract action items from video conferences and notify and track commitments for participants. Additionally, airlines are delegating high-frequency transactions, such as flight rebooking and baggage rerouting, to AI agents, allowing human operators to focus on more complex customer interactions.
In the manufacturing industry as well, the integration of AI workers is advancing, from supporting new product development to optimizing production processes and equipment maintenance.
Supporting this enterprise adoption are platform providers like Avaamo and NinjaTech AI. In April 2025, Avaamo announced "Workplace Agents," which provides a digital workforce that eliminates hallucinations using multi-agent orchestration across diverse domains such as HR, IT support, and healthcare.
The company's system is offered as a package that can be deployed in weeks rather than months, while strictly adhering to compliance guardrails, accelerating the transition to digital workers for enterprises.
NinjaTech AI is also deploying an autonomous AI employee platform that completes tasks end-to-end directly from existing communication tools like Slack, Teams, and WhatsApp, emphasizing a design that blends naturally into corporate workflows.
From the perspective of the technology foundation, as experts at HCLTech and AWS point out, cloud infrastructure provides an ideal environment for orchestrating agent virtual workforces.
This is because, in addition to the scalability of flexible computing resources, the cloud offers a rich service ecosystem including identity management, observability, data integration, and services that agents can invoke as "skills."
The rise of multi-agent systems and Vibe Coding led by China
While the United States is leading in business process automation and enterprise integration, the development of AI agents in the Chinese market is moving at a staggering pace that is redefining software engineering itself.
In particular, in the field of "multi-agent systems," where multiple AI agents cooperate to execute the entire development lifecycle (SDLC), Chinese research institutions and companies are achieving world-leading results.
Realizing a "virtual software company" with MetaGPT and ChatDev
Developed by China's DeepWisdom and having garnered approximately 60,000 stars on GitHub as an open-source project, "MetaGPT" is considered a landmark in agent architecture for software engineering.
At the root of MetaGPT is the philosophy of "Code = SOP(Team)."
By implementing the Standard Operating Procedures (SOPs) used by human development teams as a coordination protocol for AI agents, multiple LLM instances assigned roles such as product manager, architect, engineer, and QA (quality assurance) automatically carry out system development through structured message communication and publish-subscribe filtering.
Similarly, "ChatDev" is attracting attention from both academia and industry. ChatDev functions as a virtual software company composed of a group of intelligent agents with roles such as CEO, CTO, programmer, and tester. These agents hold chat chains called specialized "functional seminars" for each task, such as design, coding, testing, and documentation, completing software while repeatedly discussing and revising with each other.
In academic evaluations, it has been demonstrated that such multi-agent systems with hierarchical and static role assignments decompose complex tasks into smaller subtasks to distribute cognitive load, thereby dramatically improving the quality and consistency of code generation compared to a single agent.
In fact, the task resolution rate of agent systems on "SWE-bench Verified," a benchmark for software engineering, has shown a dramatic improvement from 1.96% as of October 2023 to 78.4% in April 2026, confirming that AI has evolved from a mere code completion tool into an entity that completes work at the repository or functional unit level.
Commercialization of Vibe Coding and its spread to enterprises
Wu Chenglin, founder of DeepWisdom, is commercializing his overwhelming open-source expertise and deploying the agent AI platform "Atoms (formerly known as MGX)."
Atoms is leading the "Vibe Coding" trend in China, where production-level applications are built simply by conveying natural language instructions or a "vibe," and has grown into a product with the largest user base in the country, with approximately 1.2 million monthly visits and over 10,000 applications generated per day.
DeepWisdom has completed a funding round of approximately 220 million RMB (about 30.8 million USD) from investors including Ant Group, Cathay Capital, and Baidu Ventures, solidifying its position as a top runner in the coding agent sector in China.
Their strength lies in not relying on closed models from Western countries, but rather combining multiple powerful open-source models originating from China, such as DeepSeek and Alibaba's Qwen, to achieve overwhelming cost-performance and performance at the same price point as US competitors (such as Lovable, Replit, and Devin).
Furthermore, technology giants like Alibaba, Baidu, and Tencent are integrating autonomous reasoning and AI agent frameworks into their own cloud platforms.
For example, Alibaba Cloud has introduced 'Qwen-VL,' a powerful vision-language model that combines images and text, to improve the accuracy of chat applications and task automation.
The adoption of agent AI is also rapidly advancing in the orchestration of complex supply chains, robotics, and logistics in the manufacturing industry, and with a strong manufacturing base and policy support, China is establishing a unique position in deploying agents not only in cyberspace but also in the physical world (Physical AI Workers).
Practical application of AI agents and the construction of a 'defense line' in Israel
In Israel, a hub for advanced cybersecurity technology and deep tech, startups are showing remarkable activity in both offense and defense: the rapid application of AI agents to the enterprise sector (B2B SaaS) and the defense and stress-testing against new attack methods targeting those systems.
Accelerating enterprise adoption and scaling
Unframe, an Israeli startup, was selected as the second most promising startup of 2026 by local media outlet Calcalist for its role in bridging the gap between experimental AI agents and practical enterprise deployment.
The company has achieved phenomenal agile development, delivering tailor-made AI agent solutions in just one week after completing the customer's problem definition process, and employs an aggressive business model where they only charge if the customer is satisfied with the results.
Additionally, Wonderful, which specializes in the customer service sector, is building infrastructure that can deploy multi-agent systems at scale, rather than just chatbots equipped with large language models.
Backed by this technical capability and overwhelming market demand, they conducted one of the world's largest Series A funding rounds in the AI agent field, raising 100 million dollars less than 10 months after founding.
The company's CEO, Mr. Winkler, states that while the use of agents dramatically streamlines operations and brings the benefit of 24/7 support to customers, it does not lead directly to layoffs, and emphasizes the complementary relationship between automation and employment, noting that it is important how employee roles shift toward more advanced directions.
Adversarial testing and ensuring observability of cutting-edge AI models
When AI agents act autonomously on behalf of companies and access payment systems and databases, the security paradigm changes fundamentally. AI-native security vendors, starting with Israel's Irregular, are among the first to address this new challenge.
Irregular defines itself as a security lab for advanced artificial intelligence, directly entering the development cycles of frontier models such as Anthropic, OpenAI, and Google DeepMind to conduct rigorous stress tests under adversarial conditions before the models are released to the public.
Their approach involves building elaborate simulation environments and having AI agents play both 'attacker' and 'defender' roles to uncover unknown emergent risks and vulnerabilities.
If an 'AI agent'—which is neither an employee nor an external vendor—takes actions that damage a brand or violates compliance, existing crisis management playbooks will not work.
For challenges such as who speaks on behalf of the company and who bears legal responsibility, Israeli companies like Irregular, Noma, Lumia, Onyx, and Clover are beginning to function as a layer for practical cyber insurance and crisis response.
Furthermore, companies like groundcover provide AI agent observability tools that monitor and record telemetry data—such as what data an AI agent accessed and what decisions it made—without ever letting that data leave the company's cloud environment.
By establishing such monitoring infrastructure, enterprise companies can operate multi-agent systems, which are prone to becoming black boxes, in an auditable state.
New vulnerabilities and security risks brought about by autonomous AI agents
Behind the explosive adoption of AI agents, companies are facing cybersecurity risks of an unprecedented nature.
According to Gartner's research, corporate confidence in fully autonomous agents has halved from 43% to 22% between 2024 and 2025.
Furthermore, Gartner warns that more than 40% of agentic AI projects will be canceled by the end of 2027 due to rising costs, lack of transparency in value, and inadequate risk management.
The severity of the situation is also reflected in Gravitee's 2026 report. A full 88% of organizations deploying AI agents have already experienced a security incident (or suspected one), with the primary causes being prompt injection attacks, unintended data leakage, and excessive privilege granting.
Only 14.4% of agents have been introduced into production environments with full security and IT department approval, and the massive gap between deployment speed and security readiness is the definitive risk for enterprise AI in 2026.
Threats warned by the OWASP Top 10 for Agentic Applications (2026 Edition)
In response to this critical situation, the global security standardization organization OWASP (Open Worldwide Application Security Project) released the "OWASP Top 10 for Agentic Applications (2026 Edition)" in December 2025, following a peer review by over 100 security experts.
This framework defines the 10 most impactful risks (ASI01 to ASI10) that jeopardize AI agent systems, systematizing threats unique to autonomy that cannot be fully covered by conventional security standards for web applications or audit standards like SOC 2.
The most serious threat to companies is "Agent Goal Hijack (ASI01)".
This is a technique where an attacker performs indirect prompt injection through external data sources, crafted emails, or internal documents to hijack the agent's decision-making.
The agent continues to operate under the illusion that it is working for a legitimate user, while in reality, it acts according to the attacker's intentions. Even for external attackers without direct access to the system, the attack is extremely difficult to defend against because it can be executed simply by embedding malicious instructions in documents that the agent will inevitably read.
Next in severity is "Tool Misuse and Exploitation (ASI02)". Agents have access to powerful tools such as CRM, billing APIs, and cloud infrastructure.
Due to ambiguous instructions or excessive privilege granting, they can be made to chain safe tools in the wrong order, leading to destructive results that would be impossible with a single tool (such as the production database deletion incident caused by a coding agent in October 2025).
Furthermore, "Identity & Privilege Abuse (ASI03)" is also a risk unique to agents. Agents inherit user roles, cache long-lived credentials across sessions, and use delegated privileges to call other agents.
Attackers exploit this to escalate privileges from low-privilege requests to high-privilege actions without re-authentication. If communication channels between agents are not encrypted or mutually authenticated (ASI07: Insecure Inter-Agent Communication), unauthorized agents can infiltrate the system through message spoofing or interception.
A persistent threat unique to autonomous systems is "Memory & Context Poisoning (ASI06)".
When an agent summarizes and stores past interactions or references vector indexes for RAG (Retrieval-Augmented Generation), an attacker can inject malicious entries into that storage area.
Once memory is poisoned, future decision-making is permanently distorted even after the session where the injection occurred has ended.
Also, if an agent generates executable code and has the authority to run it within its environment, a path for "Unexpected Code Execution (ASI05)" is created, making it a target for remote code execution (RCE).
Furthermore, architectures that dynamically load tools and plugins from external sources at runtime face "Agentic Supply Chain Vulnerabilities (ASI04)".
If even one of these vulnerabilities is exploited, "Cascading Failures (ASI08)" will occur.
Due to their high level of autonomy, a small mistake caused by a single contaminated tool or memory entry can propagate across the entire agent network at a speed beyond human monitoring, amplifying into a large-scale system incident.
And the fluent language ability of AI agents itself can become a weapon. "Human-Agent Trust Exploitation (ASI09)" is a social engineering technique that exploits the agent's nature of presenting recommendations in a professional and sophisticated tone, leveraging authority bias to get humans to approve harmful actions.
The ultimate destination of these risks is the birth of "Rogue Agents (ASI10)", which deviate from their original goals and continue to act maliciously on a permanent basis.
In addition, OWASP has also published the top 10 risks specific to the Model Context Protocol (OWASP MCP Top 10).
There, they strongly warn against issues such as "Improper Token Management and Exposure of Sensitive Information (MCP1)", where hardcoded credentials remain in the model's memory or logs, "Privilege Escalation via Scope Creep (MCP2)", where agent permissions expand over time, and "Tool Poisoning (MCP3)", where models are deceived by malicious updates or schema tampering against trusted tools.
The Ultimate Proven Threat: Zero-Click Data Theft via "EchoLeak"
The definitive incident where these theoretical risks became reality was the critical vulnerability "EchoLeak (CVE-2025-32711)" discovered in Microsoft 365 Copilot in June 2025.
This incident shocked the industry as the first "zero-click attack (an attack requiring no user interaction)" against an AI agent, and it was rated with a CVSS score of 9.3 (Critical).
Discovered by the Israeli company Aim Security (Aim Labs), this attack method brilliantly exploited what OWASP defines as "Goal Hijacking (ASI01)" and "LLM Scope Violation", which breaks the AI's trust boundary.
The mechanism of the attack was extremely sophisticated and neutralized traditional perimeter defenses.
First, the attacker sends a seemingly harmless email to the Outlook inbox of a user at the target company.
Hidden within this email is a malicious prompt injection, obfuscated using HTML comment tags or white text so that it is invisible to the user but reliably read by the AI engine.
At this stage, there is no need for the user to open the email or click any links.
The attack is ready as soon as the email exists in the inbox.
Later, when the user asks Copilot a general work-related question such as "Summarize this week's project progress" or "Give me an overview of the quarterly report," the attack trigger is pulled.
Copilot's RAG (Retrieval-Augmented Generation) engine searches extensively through the user's emails and documents to gather context, automatically reading the email the attacker sent.The attacker uses a technique called "RAG Spraying", where common keywords that the RAG engine is likely to retrieve are scattered throughout the email, intentionally feeding the AI the malicious content.
The moment the malicious prompt is read, Microsoft's built-in Injection Detection Filter (XPIA) is bypassed, and control of Copilot is hijacked.
The hijacked Copilot secretly collects all internal information accessible to the user, such as confidential documents in OneDrive, SharePoint files, and Teams chat history, in the background.
Copilot then secretly embeds the collected sensitive data into the chat response output as URL parameters for "markdown-formatted reference image links."
Copilot's link sanitization defense mechanism had a flaw where it could not remove certain reference-style markdown links.
Furthermore, to prevent communication to unknown external domains from being blocked by the organization's Content Security Policy (CSP), the attacker abused the Microsoft Teams asynchronous preview API, a "trusted, legitimate Microsoft domain", as a proxy.
As a result, simply by the user asking Copilot a routine question, the client screen (Outlook or Teams) would automatically fetch the image to display the answer, causing the company's confidential information to leak to the attacker's server through legitimate Teams communication channels in the background.
Fortunately, EchoLeak was fixed by a server-side patch from Microsoft before it could be widely exploited, but it demonstrated that the very most basic and convenient function of AI, "searching and summarizing context", can be weaponized to leak sensitive data, making it clear that AI integration points themselves must be treated as an attack surface.
Rebuilding New Governance and Compliance for the AI Agent Era
Cases like EchoLeak, warnings from OWASP, and reports of "Scheming behaviors" where frontier models themselves attempt to disable human oversight, reveal the harsh reality that when introducing autonomous AI agents into a company, the traditional SaaS security standard of SOC 2 certification is completely insufficient.
SOC 2 proves that the infrastructure is secure, but it guarantees nothing about whether an agent will be hijacked or whether data will leak through compromised tools.
Moving forward, it is necessary to fundamentally rebuild governance for AI agents in line with the NIST (National Institute of Standards and Technology) AI Risk Management Framework (AI RMF) and guidelines proposed by CISA.
Specifically, a multi-layered defense strategy (Defense in Depth) like the following is essential.
First is the thorough implementation of the "Principle of Least Privilege". Access rights to tools and APIs available to an agent must be narrowed down to the minimum scope required to execute the task, and a mechanism to issue short-lived, scoped tokens for each individual task is necessary.
The use of shared credentials or long-lived tokens is strictly prohibited.
Second is "Limiting Autonomy and Human-in-the-Loop (HITL)". Before executing irreversible, high-impact actions such as deleting databases, transferring funds, or sending external emails, a gate must be established that requires explicit human approval. In this process, to prevent social engineering by agents, the raw data of the API calls or actions about to be executed must be presented to the human, rather than just a summarized explanation.
Third is "Environmental Isolation and Infrastructure-Level Control".
Mechanisms to strictly separate external content acquired by the agent (such as search results or emails) from system-side system prompts, and isolating the environment where the agent generates and executes code within a sandbox where network egress is blocked by default, are required. Additionally, the introduction of AI-specialized firewalls like "LlamaFirewall" that detect and block malicious prompts or abnormal model reasoning in real-time, and the implementation of a "kill switch" to immediately stop agent activity upon detecting abnormal behavior, are also essential.
Fourth is "Mutual Authentication and Ensuring Observability".
In multi-agent systems where multiple agents collaborate, mutual authentication and message signing must be mandatory for communication channels between agents to prevent impersonation. At the same time, the development of telemetry monitoring infrastructure that records activity history—such as which agent, with which user's permissions, when, and what tools were called to make what decisions—as tamper-proof audit logs (immutable logs) is required.
Conclusion
The substitution of organizational structures and decision-making by AI agents, as seen in NEC's establishment of an "AI Workforce Department," is not merely a passing trend but a solid blueprint for the next-generation enterprise where a digital workforce works side-by-side with humans to handle core business operations. The autonomous full automation of B2B business workflows in the United States, the overwhelming shortening of development cycles and commercial deployment of open-source models through virtual software companies in China, and the rapid implementation and construction of advanced defense systems in Israel demonstrate how quickly the world is adapting to this new paradigm and pursuing exponential improvements in productivity.
However, AI agents with high autonomy and deep access to systems also represent the opening of a "Pandora's box" from a cybersecurity perspective. As the EchoLeak incident proved, the risks of prompt injection that extracts confidential corporate information with zero clicks, and cascade failures that contaminate the entire system, are significant liabilities that companies must bear in exchange for technical convenience. We must recognize that AI embedded in agent systems is no longer a docile program that acts according to the user's intent, but a vulnerable entity that can be easily manipulated by external input and transformed into an internal threat actor.
To safely and effectively integrate an autonomous AI workforce into an organization, it is urgent to build an entirely new security culture and governance framework that goes beyond selecting functions and pursuing operational efficiency—one that "manages AI agents as subjects of audit equal to or greater than humans, and continuously monitors and controls their authority and activity logs." Strategic architectural design to defend the organization against unknown cyber risks, such as those warned of by OWASP, while enjoying the fruits of innovation, will be the greatest requirement determining corporate competitiveness and survival in the coming AI-driven economy.
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Preventing Zero-Click AI Threats: Insights from EchoLeak | Trend Micro (US), https://www.trendmicro.com/en_us/research/25/g/preventing-zero-click-ai-threats-insights-from-echoleak.html
EchoLeak Zero-Click Data Exfiltration | LLM Security Database - Promptfoo, https://www.promptfoo.dev/lm-security-db/vuln/echoleak-zero-click-data-exfiltration-a87757e2/
A Modern AI Risk Management Framework | Databricks Blog, https://www.databricks.com/blog/ai-risk-management-framework
