The Genesis of Agent Governance: How MCP Offers a Technical Blueprint for Compatibility and Security
As the demand for intelligent agents diversifies across user groups, effective governance must address the unique concerns of each community. By leveraging technological safeguards such as the Model Context Protocol (MCP), fostering open-source collaboration, and implementing human-in-the-loop oversight, we can ensure the trustworthiness and controllability of agent applications while promoting a healthy ecosystem.
An intelligent agent, or AI Agent, is a system powered by large language models (LLMs) that interacts with the external environment through tools, acting on behalf of the user.
In November 2024, Anthropic introduced the Model Context Protocol (MCP), an open-source protocol that offers a technical solution to improve efficiency and security for general-purpose agents.
While MCP lays the groundwork for agent governance, it does not solve every challenge.
Challenges Faced by General-Purpose Agents
Agents are systems leveraging large language models to interact with the outside world through various tools, representing users and executing actions. These agents possess memory, planning, perception, tool invocation, and action capabilities.
Manus, for example, is positioned as a general-purpose agent, distinct from workflow-oriented agent products.
The industry’s expectation for agents, especially general-purpose ones, stems from their ability to satisfy various stakeholders’ needs.
However, general-purpose agents face three key challenges: compatibility, security, and competition.
The MCP protocol, which enables efficient collaboration among models across different tools and data sources, and ensures secure responsibility allocation in multi-party data aggregation, is more worthy of in-depth study than the Manus product itself.
MCP: A Technical Solution for Compatibility and Security
In November 2024, Anthropic open-sourced the Model Context Protocol (MCP), allowing systems to provide context information to AI models in a standardized and secure manner across different integration scenarios.
MCP uses a layered architecture to address standardization and security issues in Agent applications. A host application (such as Manus) connects simultaneously to multiple service programs (MCP Servers) via an MCP client. Each server specializes in providing standardized access to a specific data source or application.
First, MCP solves the compatibility issue in Agent data/tool invocation through standard consensus.
Second, MCP has three security considerations. First, the data link isolates the model and the specific data source, and the two interact through the MCP Server protocol. The model does not directly depend on the internal details of the data source, clarifying the source of multi-party data mixing.
Second, the communication protocol enhances the transparency and auditability of the command control link, solving the information asymmetry and black box challenges of user-model data interaction.
Third, the authorization link is secured by responding according to permissions, ensuring the user’s control over the Agent’s use of tools/data.
MCP builds a standardized interface and security protection mechanism through a layered architecture, achieving a balance between interoperability and security in data and tool invocation.
MCP as a Foundation for Agent Governance
MCP offers compatibility and security for data and tool invocation, laying a foundation for Agent governance, but it does not solve all the challenges faced in governance.
First, in terms of trustworthiness, MCP has not yet formed normative standards for the selection of called data sources and tools, nor has it evaluated and verified the execution results.
Second, MCP cannot temporarily adjust the new type of commercial competitive cooperation relationship brought about by Agent.
Overall, MCP provides an initial technical response to the core security concerns faced by users using Agent, and has become the starting point of Agent governance.
Deep Dive into the Challenges of General-Purpose Agents
General-purpose agents, while promising, encounter several hurdles that necessitate careful consideration and innovative solutions. These challenges span across compatibility, security, and competition, each demanding a unique approach to ensure the responsible and effective deployment of these agents.
Compatibility Conundrums
The compatibility challenge arises from the diverse ecosystem of tools, data sources, and platforms that agents must interact with. Each of these components may have its own unique protocols, formats, and interfaces, creating a complex web of dependencies that can be difficult to navigate.
For instance, an agent designed to manage a user’s calendar, email, and social media accounts must be able to seamlessly integrate with each of these services, despite their disparate APIs and data structures. This requires the agent to possess a high degree of adaptability and the ability to translate between different formats and protocols. Consider the scenario where an agent needs to retrieve information from a legacy database with a proprietary format and then present it in a modern web interface. The agent must effectively bridge the gap between these two systems, handling data conversion and protocol translation on the fly. This might involve using specialized libraries or middleware to interact with the legacy system and then transforming the data into a JSON format suitable for the web interface.
Furthermore, the compatibility challenge extends beyond technical considerations to encompass semantic interoperability. Agents must be able to understand the meaning of data and instructions across different contexts, even when expressed in different terms or formats. This requires advanced natural language processing (NLP) capabilities and the ability to reason about the relationships between different concepts. For example, if a user asks an agent to ‘schedule a meeting for next Tuesday’, the agent must understand what ‘next Tuesday’ refers to in the user’s current time zone and calendar context. It must also be able to resolve any ambiguities in the user’s request, such as whether the meeting is a recurring event or a one-time occurrence. This requires the agent to have a rich understanding of natural language and the ability to infer the user’s intent based on the available information.
To address the compatibility challenge, several approaches have been proposed, including the development of standardized protocols and interfaces, the use of ontologies and knowledge graphs to represent semantic relationships, and the adoption of machine learning techniques to automatically adapt to new data sources and tools. Standardized protocols like REST and GraphQL provide a common way for agents to interact with web services, while ontologies and knowledge graphs provide a structured representation of knowledge that can be used to reason about the relationships between different concepts. Machine learning techniques, such as transfer learning and domain adaptation, can be used to train agents to adapt to new data sources and tools with minimal effort.
Security Safeguards
Security is paramount when deploying agents, as they often have access to sensitive data and the ability to perform actions on behalf of users. The security challenge encompasses a range of threats, including unauthorized access, data breaches, and malicious manipulation. Imagine an agent that manages a user’s financial accounts and is authorized to make payments on their behalf. If this agent is compromised, an attacker could gain access to the user’s funds and make unauthorized transactions. This highlights the importance of implementing robust security measures to protect agents from malicious attacks.
Agents must be designed with security in mind from the outset, incorporating mechanisms to authenticate users, authorize access to resources, and protect data from unauthorized disclosure or modification. This requires the use of strong encryption, access control policies, and intrusion detection systems. Multi-factor authentication can be used to verify the user’s identity, while role-based access control can be used to restrict access to sensitive resources based on the user’s role. Encryption can be used to protect data in transit and at rest, while intrusion detection systems can be used to detect and prevent malicious activity.
In addition, agents must be resilient to attacks that attempt to exploit vulnerabilities in their code or logic. This requires rigorous testing and validation, as well as the implementation of security updates and patches. Regular security audits and penetration testing can help identify vulnerabilities in the agent’s code, while security updates and patches can be used to fix these vulnerabilities and prevent them from being exploited.
Furthermore, the security challenge extends to the supply chain of agent components, as agents often rely on third-party libraries and services. It is essential to ensure that these components are secure and trustworthy, and that they are not compromised by malicious actors. Software Composition Analysis (SCA) tools can be used to identify vulnerabilities in third-party libraries, while security certifications and audits can be used to assess the security of third-party services.
To address the security challenge, several approaches have been proposed, including the use of secure coding practices, the implementation of security audits and penetration testing, and the adoption of security standards and certifications. Secure coding practices, such as input validation and output encoding, can help prevent common vulnerabilities like SQL injection and cross-site scripting. Security audits and penetration testing can help identify vulnerabilities in the agent’s code, while security standards and certifications can provide a framework for building and deploying secure agents.
Competitive Cooperation
The competitive landscape for agents is rapidly evolving, with numerous companies and organizations vying to develop and deploy the most capable and effective agents. This competition can lead to innovation and improvement, but it can also create challenges related to fairness, transparency, and accountability. Consider the case of two competing agents that are both designed to provide financial advice. If one agent is biased towards recommending products from a particular company, it could mislead users and create an unfair advantage for that company. This highlights the importance of ensuring that agents are fair, transparent, and accountable in their decision-making.
One challenge is the potential for agents to engage in unfair or deceptive practices, such as price discrimination, data manipulation, or the spread of misinformation. This requires the implementation of ethical guidelines and regulatory frameworks to ensure that agents are used in a responsible and transparent manner. Ethical guidelines can provide a framework for developing and deploying agents that are aligned with human values, while regulatory frameworks can provide legal mechanisms for holding agents accountable for their actions.
Another challenge is the potential for agents to exacerbate existing inequalities, such as bias in hiring or lending decisions. This requires careful attention to the design and training of agents, as well as the implementation of fairness metrics and auditing procedures. Fairness metrics can be used to measure the bias in an agent’s decisions, while auditing procedures can be used to identify and correct any unfairness.
Furthermore, the competitive landscape can create challenges related to data privacy and ownership. Agents often collect and process vast amounts of data, raising concerns about how this data is used andprotected. It is essential to establish clear guidelines for data privacy and ownership, and to ensure that users have control over their data. Privacy-enhancing technologies, such as differential privacy and federated learning, can be used to protect user data while still allowing agents to learn and improve.
To address the competitive challenge, several approaches have been proposed, including the development of ethical guidelines, the implementation of regulatory frameworks, and the promotion of open-source collaboration. Open-source collaboration can foster transparency and innovation in the agent ecosystem, while ethical guidelines and regulatory frameworks can provide a framework for ensuring that agents are used in a responsible and beneficial way.
The Model Context Protocol: A Deeper Dive
The Model Context Protocol (MCP) represents a significant step forward in addressing the challenges of compatibility and security in agent applications. By providing a standardized and secure way for agents to interact with different data sources and tools, MCP enables the development of more robust, reliable, and trustworthy agents.
A Layered Architecture for Standardization and Security
MCP employs a layered architecture that separates the agent from the underlying data sources and tools, creating a clear separation of concerns. This architecture consists of three main layers:
The Host Application: This is the agent itself, responsible for coordinating the overall task and interacting with the user. The host application is the central orchestrator of the agent’s activities, responsible for interpreting user requests, planning the execution strategy, and presenting the results to the user. It interacts with the MCP client to access data and tools, but it does not need to know the details of how these resources are implemented. This allows the host application to be more generic and adaptable to different environments.
The MCP Client: This component provides a standardized interface for the host application to communicate with the MCP servers. The MCP client acts as a proxy between the host application and the MCP servers, translating the host application’s requests into the MCP protocol and forwarding them to the appropriate server. It also handles the responses from the servers and translates them back into a format that the host application can understand. This standardized interface simplifies the development of host applications and allows them to interact with a wide range of data sources and tools.
The MCP Servers: These components provide access to specific data sources or tools, translating between the standardized MCP protocol and the native protocols of the underlying resources. The MCP servers are responsible for managing access to the underlying data sources and tools, ensuring that they are used in a secure and authorized manner. They translate the MCP protocol into the native protocols of the underlying resources, allowing the host application to interact with them without needing to know the details of their implementation. Each MCP server is responsible for a specific data source or tool, allowing for a modular and scalable architecture.
This layered architecture provides several benefits, including:
Improved Compatibility: By using a standardized protocol, MCP allows agents to interact with different data sources and tools without having to worry about the details of their specific interfaces. The standardized protocol provides a common language for agents and data sources to communicate, eliminating the need for custom integrations and reducing the risk of compatibility issues.
Enhanced Security: By isolating the agent from the underlying resources, MCP reduces the risk of unauthorized access and data breaches. The layered architecture ensures that the agent can only access data and tools through the MCP servers, which enforce access control policies and protect sensitive information. This reduces the attack surface and makes it more difficult for malicious actors to compromise the agent.
Increased Flexibility: The layered architecture allows for easy addition and removal of data sources and tools, making it easier to adapt to changing requirements. New data sources and tools can be added by simply creating new MCP servers, without requiring any changes to the host application. This makes it easier to adapt to changing requirements and to integrate new technologies into the agent ecosystem.
Addressing Compatibility through Standard Consensus
MCP addresses the compatibility challenge by providing a standardized protocol for agents to access and manipulate data from different sources. This protocol defines a common set of operations for reading, writing, and updating data, as well as a common format for representing data. The standardized protocol ensures that agents can interact with different data sources in a consistent manner, regardless of their underlying implementation.
By adhering to this protocol, agents can interact with different data sources without having to worry about the details of their specific formats or interfaces. This simplifies the development process and reduces the risk of compatibility issues. Developers can focus on building the logic of the agent, rather than spending time on integrating with different data sources.
Security Considerations in MCP
MCP incorporates several security considerations to protect data and prevent unauthorized access. These include:
Data Isolation: The MCP architecture isolates the agent from the underlying data sources, preventing it from directly accessing sensitive information. The agent can only access data through the MCP servers, which act as intermediaries and enforce access control policies. This reduces the risk of data breaches and unauthorized access.
Command Control Transparency: The communication protocol used by MCP provides transparency and auditability, allowing users to track and verify the actions performed by the agent. The protocol includes mechanisms for logging and auditing all interactions between the agent and the data sources, allowing users to track the agent’s activities and verify that it is acting in accordance with their instructions.
Permission-Based Authorization: MCP enforces strict access control policies, ensuring that the agent only has access to the data and tools that it is authorized to use. The MCP servers enforce access control policies based on the user’s permissions, ensuring that the agent can only access the data and tools that it is authorized to use. This reduces the risk of unauthorized access and data breaches.
Balancing Interoperability and Security
MCP strikes a balance between interoperability and security by providing a standardized interface for accessing data and tools while also implementing security measures to protect data and prevent unauthorized access. This balance is essential for ensuring that agents can be used in a safe and responsible manner. The standardized interface allows agents to interact with different data sources in a consistent manner, while the security measures protect sensitive information and prevent unauthorized access.
Beyond MCP: The Future of Agent Governance
While MCP represents a significant step forward, it is not a complete solution to the challenges of agent governance. Several areas require further attention, including:
Trustworthiness and Data Validation
MCP does not currently provide mechanisms for verifying the accuracy and reliability of data sources, nor does it provide a way to evaluate the quality of results produced by agents. This is an area that requires further development, as users need to be able to trust the information and actions provided by agents. Mechanisms for data validation and quality assessment are needed to ensure that agents are providing accurate and reliable information. This could involve using techniques such as data provenance tracking, data lineage analysis, and data quality metrics.
Navigating New Commercial Landscapes
The rise of agents is creating new commercial relationships and business models, which can be difficult to navigate. MCP does not address these issues, and further consideration is needed to ensure that the agent ecosystem is fair and competitive. Issues such as data ownership, intellectual property rights, and liability for agent actions need to be addressed to ensure that the agent ecosystem is fair and sustainable.
The Ongoing Evolution of Agent Governance
MCP represents a crucial starting point for agent governance, providing a technical foundation for addressing compatibility and security concerns. However, ongoing efforts are needed to address the remaining challenges and ensure that agents are used in a responsible and beneficial way. As the field evolves, continued collaboration between researchers, developers, and policymakers will be essential to shape the future of agent governance. This collaboration should focus on developing ethical guidelines, regulatory frameworks, and technical standards to ensure that agents are used in a way that benefits society as a whole. The future of agent governance requires a multi-faceted approach that considers technical, ethical, and societal implications.