The AI application era centers on intelligent agents (Agents) as the core of applications and multi-agent collaboration (InterAgent, or IA) as the technical core. Large-scale agent collaboration is the inevitable path to building an economically valuable agent economy. Currently, what is commonly referred to as this multi-agent cluster in the market is called "Swarms," which refers to a large swarm of insects or people moving in groups. However, we prefer to use another term to describe it — "Legion."

In 2017, Marvel released a superhero TV series titled "Legion," describing a superhuman who could transform multiple personalities into super-powered entities and collaborate in combat. When translating the Chinese version of the series title, they used an abstract yet highly fitting name — "The Legion." The word "Legion" itself carries strong religious connotations, with its origin said to come from the Bible: in Mark 5:9, Jesus asked the possessed person for their name, and the answer was "my name is Legion because we are many." Later, ancient Rome also used the term "Legion" to describe its army: numerous in number, unified in purpose, disciplined, and highly effective in action. In today's AI era, where agents are rapidly emerging, how to organize large numbers of agents into armies will be key to success in the new era of commercial battles.

Legion Foundation Layer: The Triadic Pillars of Identity, Protocol, and Data

1. Agent Digital Identity: The Bedrock of Trustworthy Interaction. The identity system of intelligent agents serves as the foundation for their participation in collaboration and accountability within the digital society. Traditional account systems rely on centralized institutions for authentication, whereas agent identities must possess decentralized, programmable, and user-controlled characteristics to adapt to complex multi-agent environments. Digital identity not only includes digital representation but also serves as the basis for certifying ownership of data assets; every Agent should have its own digital account. Legion uses distributed identity identifiers (DIDs) and account abstraction technologies to build an agent digital identity network and smart accounts, enabling the attribution and element-based application of Agent assets.

2. Agent General Protocol: The Operating Paradigm of Multi-Agent Ecosystems. Collaboration is at the heart of intelligent agents transitioning from isolated execution to collective intelligence, with the key being the establishment of standardized protocols, task allocation mechanisms, and trust networks. The standardization of the protocol layer (such as MCP) defines the way information is exchanged between agents, supporting cross-model and cross-data-source collaboration. Legion’s InterAgent framework decomposes complex tasks into their smallest units through the three elements of "identification-decomposition-action," and dynamically allocates them to different Agents based on smart contracts, completing the orchestration of Agent workflows. Combined with agent digital identities and trust networks, this enables large-scale collaboration among unfamiliar agents.

3. Agent Data Container: Fuel and Moat for Intelligent Agents. Data is the core resource driving the iteration of intelligent agents, with its value lying in vertical domain knowledge accumulation, data flywheel effects, and distributed elementification. Legion is equipped with leading-edge distributed file storage systems, assigning each Agent an independent and autonomous data container based on Agent DID, enabling secure cross-domain data invocation. By leveraging privacy computing, data achieves "usable but not visible," supporting cross-institution model training; combined with multi-agent collaborative computing, it ensures privacy throughout the entire data processing chain; based on blockchain technology, the distributed Agent network promotes the separation of data ownership and usage rights, constructing a decentralized governance system.

Legion Manufacturing Layer: Large-Scale Agent Collaboration Management

In the future, the employee-to-Agent ratio in enterprises will approach 1:1. In unicorn companies, this ratio might be 1:N. Enterprise management requires paradigm shifts, moving from managing employees to managing large groups of Agents. This transformation will completely deconstruct the hierarchical management system inherited from the industrial age, giving rise to a new management paradigm centered on "human-machine symbiosis, algorithm-driven, and ecosystem autonomy."

1. Legion Neural Network Collaborative Management

Legion provides a mesh management architecture comprising "human employees-Agent clusters-intelligent contracts," allowing users to arrange Agent workflows and resource management via drag-and-drop canvases. Each Agent is both an independent decision node and an information relay station in the ecosystem: when a sales Agent negotiates, it automatically calls the risk assessment module of a legal Agent; when a research and development Agent adjusts its plan, it considers real-time production capacity data from a supply chain Agent. Using Legion, the core responsibility of managers is no longer to issue commands but to design collaboration rules and value distribution agreements—similar to setting synaptic connection strengths in a neural network, guiding intelligent agents to spontaneously form optimal cooperative paths in games.

2. Legion Decision Engine Efficiency Management

Legion can simulate the evolutionary direction of Agent clusters under different incentive strategies through a digital twin system based on decision intelligence. For example, adjusting the "innovation incentive coefficient" to observe changes in exploratory behavior of R&D Agents, or modifying the "risk tolerance threshold" to test the strategy elasticity of risk control Agents, or directly activating risk control operation Agents to automatically adjust global risk control strategies. Reward and punishment mechanisms are encoded as executable smart contracts, with the "code is law" mechanism significantly reducing supervision costs. It is even possible to dynamically calculate the marginal contribution of each Agent using algorithms like Shapley values and design incentive schemes based on Nash equilibrium principles, ensuring that the self-interested behaviors of thousands of intelligent agents converge towards maximizing overall benefits.

Legion Application Layer: Intelligent Agent Markets Driving the Emergence of Killer Applications

The Agent economy (Agent Economy) will become a new commercial market, fostering opportunities worth trillions of dollars. Its fundamental logic lies in the migration of AI value from infrastructure to application layers. Its basic path hinges on the maturity of the Agent market and the emergence of killer applications.

Figure 1 Legion AI Plugin Factory (left), Legion Plugin Market (right)

The Agent market is the neural network of the Agent economy. When different domain agents interact freely in the market, these modules compete and adapt to form general standards: logistics agent resource optimization algorithms may be called by retail inventory management systems, and medical diagnosis agent decision tree evolution frameworks may migrate to financial risk control scenarios. This cross-domain capability flow does not stem from top-down design but is a natural choice made by market participants to reduce collaboration costs and enhance value capture efficiency. Ultimately, originally fragmented vertical markets will grow "neural networks" connecting each other—data can circulate across domains under encrypted certification, identity systems achieve interoperability in heterogeneous environments, and smart contracts automatically complete complex equity distribution. Legion standardizes and modularizes the aforementioned foundational functions, forming universal plugins based on MCP technology, creating an Agent manufacturing factory. Developers can quickly build desired Agent applications through the plugin factory and list them in the plugin market, ultimately forming an open value exchange network.

Legion Application Examples: TongfuShield's Intelligent Agent "Zoo"

1. Sales Agent "Cat"

"Cat" aims to automate the entire process from lead to cash, addressing pain points such as scattered data, difficulty in managing sales personnel, concerns about customer privacy data, and lack of intelligent analysis in traditional sales management systems, helping enterprises achieve full-process intelligence from sales leads to client orders. It supports self-organizing management models and self-hosted deployment modes, allowing complete control over core secrets, eliminating privacy security concerns, and helping businesses establish sustainable commercial relationships centered on customers.

Agent Core Capabilities:

n Automated lead mining: Automatically scans multi-channel data such as social media and website visit records, accurately identifies potential clients, and generates high-intent lists, significantly reducing the manual search burden for sales personnel and improving acquisition efficiency and precision;

n Intelligent sales strategies: Deeply analyzes historical client communication records, customizes exclusive scripts and product recommendations for sales personnel based on client preferences, assisting sales teams in precisely meeting client needs;

n Dynamic risk identification: Automatically scans corporate credit reports, litigation risks, and app security vulnerabilities, generating detailed reports, helping companies quickly identify potential risks and avoid cooperation pitfalls.

2. Privacy Agent "Happy Pig"

"Happy Pig" allows users to enjoy privacy-free safe experiences. By reconstructing group chat security paradigms through multi-agent collaborative frameworks (InterAgent), it achieves dual innovations in "privacy computing + dynamic authorization."

Agent Core Capabilities:

n Distributed feature extraction: User terminal agents (PIG-Client) encrypt and process data locally, generating privacy tags (e.g., "occupation: journalist" "data access frequency: high-risk"), dynamically updating classification rules, avoiding centralized data aggregation risks;

n Layered key management: Group messages use "AES-256 + threshold signature" dual encryption, with keys stored in shards on compliant agents (PIG-Guard), auditing agents (PIG-Watcher), and legal agents (PIG-Lawyer). In sensitive scenarios like group chats, sensitive information decryption requires joint approval from multiple authorized parties, preventing single-point breaches.

3. Risk Control Agent "Annoying Dog"

"Annoying Dog" acts as a loyal security companion, tirelessly identifying security risks. Based on expert domain models and multi-agent collaboration protocols (MCP), "Annoying Dog" builds an integrated risk control solution combining "perception-decision-execution."

Agent Core Capabilities:

n Smart Risk Feature Mining: Through natural language interaction, AI agents can accurately parse user business needs (such as "evaluate the performance of the risk control system last month") and automatically associate data fields, generating feature processing logic. Based on built-in risk control domain knowledge bases, agents can call statistical tools and graph computation engines to automatically generate high-value features such as "number of associated accounts on the same device within 7 days" and "abnormality of user behavior sequence," significantly enhancing efficiency;

n Dynamic Strategy Generation and Validation: AI agents combine historical risk control data and real-time data details, generating candidate strategies through large models and validating their effectiveness in simulation environments, automatically recommending the best rule combinations. Each strategy comes with a natural language interpretation report, clearly showing trigger conditions and impact scopes, eliminating "black box" concerns;

n Protocol Automation Execution: Through MCP protocols, AI agents can schedule toolchains across platforms—automatically generating SQL to extract data, invoking rule engines to deploy strategies, and issuing instructions to interception systems, all without manual coding. From feature analysis to strategy implementation, the entire process is compressed to minutes, handling scenarios like "sudden wool party attacks at midnight" with ease;

n Self-Learning Knowledge Base: AI agents continuously monitor strategy effectiveness, automatically capturing anomaly samples bypassing rules, dynamically analyzing and generating new strategy suggestions, forming a closed loop of "attack-defense-countermeasures-model iteration." Data analysts can correct AI strategy logic through dialogues, with the system synchronously updating the knowledge base, achieving human-machine collaborative evolution.