Pioneering Agent Governance: MCP’s Technical Blueprint for Compatibility and Security
As the demand for intelligent agents diversifies across user groups, governance must address varying priorities. The Model Context Protocol (MCP), bolstered by open-source collaboration and human oversight, provides a foundation for a secure and reliable agent ecosystem.
Intelligent agents (AI Agents) are systems powered by large language models, capable of interacting with the external world through tools and acting on behalf of users. The recent emergence of Manus highlights the market’s anticipation for practical agent applications.
Announced in November 2024, Anthropic’s open-source Model Context Protocol (MCP) offers a technical solution to enhance the efficiency and security of general-purpose agents. MCP streamlines integration through standardized interfaces, boosting data and tool access efficiency. It also fortifies security by isolating models from specific data sources and enhancing command control transparency. This balanced approach prioritizes user experience while ensuring controlled authorization.
While MCP establishes a foundation for agent governance, it doesn’t solve every challenge. For instance, it doesn’t validate the rationale behind tool selection or the accuracy of execution results, nor does it effectively address competition and collaboration within the agent-application ecosystem.
Challenges Faced by General-Purpose Agents in Application
An Agent is a system equipped with memory, planning, perception, tool invocation, and action capabilities, empowered by extensive language models, that interacts with the external environment through tools, acting on behalf of the user. The Agent needs to perceive and understand the user’s intentions, obtain and store information through the memory module, formulate and optimize strategies by leveraging the planning module, invoke the tool module to execute specific tasks, and implement plans through the action module, thereby achieving the goal of autonomously completing tasks.
Manus is more of a general-purpose Agent, unlike workflow-oriented Agent products.
Industry expectations for Agents, especially general-purpose Agents, stem from the collective needs they address. In capital markets, Agents represent the industry’s anticipated closed-loop path for the commercial value of models, shifting AI pricing from token-based computation to effect-based pricing for customized services, resulting in greater profitability. On the user side, businesses expect Agents to execute repetitive, standardized, and clearly defined processes with precision automation, while the public anticipates Agents to bring ‘technological benefits,’ becoming personalized, low-threshold ‘digital stewards’ for everyone.
However, general-purpose Agents face compatibility, security, and competitive challenges in application. In terms of compatibility, models need to collaborate efficiently with different tools and data sources in the call. In terms of security, Agents needto execute tasks clearly and transparently according to user instructions and reasonably allocate security responsibilities under the convergence of multiple parties’ data. In terms of competition, Agents need to solve the competitive and cooperative relationships in the new business ecosystem.
Therefore, the MCP protocol, which enables models to collaborate efficiently with different tools and data sources and reasonably allocate security responsibilities under the convergence of multiple parties’ data, is worth studying in depth compared to the Manus product itself.
Compatibility Concerns
The world of AI is rapidly evolving, with new models and tools emerging constantly. For a general-purpose agent to be truly useful, it needs to be able to seamlessly integrate with a wide variety of resources. This presents a significant challenge, as each tool or data source may have its own unique interface and data format. Without a standardized approach, developers would need to write custom code for each integration, which is time-consuming and inefficient. This lack of compatibility can hinder the widespread adoption of AI agents, as users may be reluctant to invest in a technology that doesn’t easily work with their existing systems. The need for harmonious interaction between diverse components is paramount for the effectiveness and accessibility of AI agents in various applications. Ensuring compatibility requires addressing the heterogeneity of interfaces and data formats, paving the way for broader integration and usability.
Security Risks
AI agents are designed to act on behalf of users, which means they often have access to sensitive data and systems. This raises significant security concerns, as a compromised agent could be used to steal data, disrupt operations, or even cause physical harm. It’s crucial to ensure that agents are designed with security in mind, and that they are subject to rigorous testing and monitoring to prevent vulnerabilities. Additionally, it’s important to establish clear lines of responsibility for security, especially when multiple parties are involved in the development and deployment of an agent. Thorough security protocols, including rigorous testing and clear accountability, are essential to mitigate potential risks associated with AI agents’ access to sensitive data and systems. Protecting against vulnerabilities is critical for maintaining user trust and ensuring the responsible deployment of AI technologies. A robust security framework ensures the integrity and confidentiality of data, safeguarding against malicious activities.
Competitive Landscape
As AI agents become more prevalent, they are likely to disrupt existing business models and create new forms of competition. For example, an agent that can automatically negotiate prices with suppliers could give a company a significant competitive advantage. However, this could also lead to a race to the bottom, as companies compete to offer the lowest prices. It’s important to consider the potential impact of AI agents on the competitive landscape, and to develop strategies for navigating this new environment. This includes addressing issues such as data ownership, intellectual property, and the potential for anti-competitive behavior. The proliferation of AI agents introduces new dynamics into the competitive landscape, necessitating strategies for navigating issues like data ownership, intellectual property, and the potential for anti-competitive practices. Understanding and adapting to these changes are crucial for organizations seeking to leverage AI agents effectively while maintaining fair competition. Addressing these concerns proactively is vital for fostering a healthy and innovative AI ecosystem.
MCP: A Technical Solution for Compatibility and Security in Agent Applications
In November 2024, Anthropic open-sourced the MCP (Model Context Protocol) open protocol, allowing systems to provide context to AI models and can be universalized in different integration scenarios. MCP uses a layered architecture to solve the standardization and security problems in Agent applications. A host application (such as Manus) connects to multiple service programs (MCP Server) through the MCP client at the same time, and each Server performs its own duties, providing standardized access to a data source or application.
First, MCP solves the compatibility problem in Agent data/tool calls through standard consensus. MCP replaces fragmented integration with a unified interface, and AI only needs to understand and abide by the agreement to interact with all tools that meet the specifications, which significantly reduces duplicate integration. Second, MCP has three considerations in terms of security. First, the model and specific data sources are isolated on the data link, 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. The second is to improve the transparency and auditability of the command and control link through communication protocols, and solve the information asymmetry and black box challenges of user-model data interaction. The third is to ensure the controllability of the authorization link by responding according to permissions, and ensure the user’s control over the Agent in the 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 calls. At the user value level, MCP brings stronger collaboration and interaction between intelligent bodies and more tools, and even more intelligent bodies. In the next stage, MCP will focus on developing support for remote connections.
Standardized Interfaces for Enhanced Compatibility
One of the key features of MCP is its use of standardizedinterfaces. This means that AI agents can interact with different tools and data sources without requiring custom code for each integration. Instead, the agent simply needs to understand the MCP protocol, which defines a common set of commands and data formats. This greatly simplifies the integration process and reduces the amount of development work required. It also makes it easier to switch between different tools and data sources, as the agent doesn’t need to be reconfigured each time. The adoption of standardized interfaces streamlines integration, reduces development efforts, and enhances the flexibility of AI agents in interacting with various tools and data sources. This simplifies workflows and lowers barriers to entry for new integrations. The standardized approach unlocks the potential for greater adaptability and efficiency.
The use of standardized interfaces also promotes interoperability between different AI agents. If multiple agents all support the MCP protocol, they can easily communicate and share data with each other. This can lead to the development of more complex and sophisticated AI systems, where multiple agents work together to solve a problem. This interoperability enables the creation of advanced AI systems where multiple agents collaborate, share data, and collectively solve complex problems. The standardized interfaces enable agents from different platforms to seamlessly communicate and work together. This promotes innovation and facilitates the development of sophisticated AI solutions.
Robust Security Mechanisms for Data Protection
Security is a top priority in the design of MCP. The protocol includes several mechanisms to protect data and prevent unauthorized access. One key feature is the isolation of models from specific data sources. This means that the agent doesn’t have direct access to the underlying data, but instead interacts with it through the MCP Server protocol. This adds a layer of indirection that makes it more difficult for an attacker to compromise the data. The protocol’s architecture isolates models from specific data sources, adding a layer of indirection that makes it more difficult for attackers to compromise sensitive data. The focus on security helps to ensure user trust and the reliable operation of AI agents. The enhanced security features protect data and prevent unauthorized access.
MCP also includes mechanisms to improve the transparency and auditability of command and control links. This allows users to see exactly what commands are being sent to the agent, and to verify that the agent is acting in accordance with their instructions. This is important for building trust in AI systems, as it allows users to understand how the agent is making decisions. The protocol enhances transparency and auditability by enabling users to monitor commands sent to agents, thereby fostering trust and understanding of decision-making processes. Providing visibility into the agent’s operations builds confidence and allows users to verify compliance with instructions. This capability is crucial for the responsible deployment of AI agents.
Finally, MCP provides a mechanism for controlling the authorization of agents. This allows users to specify which tools and data sources the agent is allowed to access. This is important for preventing the agent from accessing sensitive data or performing actions that it is not authorized to do. The authorization control mechanism empowers users to specify authorized access to tools and data, preventing unauthorized actions and bolstering data security. This enables fine-grained control over agent capabilities, ensuring adherence to security policies and minimizing risks. Enhanced control is essential for maintaining data privacy and integrity.
MCP: Laying the Groundwork for Agent Governance
MCP provides compatibility and security guarantees for data and tool calls, laying the foundation for Agent governance, but it cannot solve all the challenges faced in governance.
First, in terms of credibility, MCP has not formed a normative standard for the selection of calling 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 Agents, and has become the starting point for Agent governance. With the popularization of Agent and other AI applications, distributed methods are needed to meet the differentiated needs of different users. The focus of governance is not only the security of the model, but also the core requirement of meeting user needs. The MCP protocol has taken the first step in responding to user needs and promoting technological co-governance. It is also on the basis of MCP that Agent achieves efficient division of labor and collaboration of various tools and resources. A week ago, Google open-sourced the Agent2Agent (A2A) protocol for communication between Agents, so that Agents built on different platforms can negotiate tasks and conduct safe collaboration, and promote the development of a multi-intelligent body ecology.
Addressing Trust and Reliability Concerns
While MCP provides a solid foundation for agent governance, it doesn’t address all of the challenges. One key area that needs further attention is the issue of trust and reliability. MCP doesn’t currently include any mechanisms for verifying the accuracy of execution results or for ensuring that agents are selecting appropriate data sources and tools. This means that users may not be able to fully trust the decisions made by an agent, especially in high-stakes situations.
To address this concern, it will be necessary to develop new standards and best practices for agent development and deployment. This could include things like formal verification methods, which can be used to prove that an agent will always behave in a predictable and safe manner. It could also include the use of explainable AI techniques, which can help users understand how an agent is making decisions. Improving trust and reliability involves establishing standards, developing formal verification methods, and incorporating explainable AI techniques to enhance user confidence in agent decisions. Building trust is key to widespread adoption of AI agents, especially in high-stakes situations. The implementation of stringent measures and clear explanations helps to ensure accountability and user understanding.
Navigating the New Competitive Landscape
Another challenge that MCP doesn’t fully address is the impact of agents on the competitive landscape. As agents become more prevalent, they are likely to disrupt existing business models and create new forms of competition. It’s important to consider the potential impact of agents on the competitive landscape, and to develop strategies for navigating this new environment. This includes addressing issues such as data ownership, intellectual property, and the potential for anti-competitive behavior. Navigating the changing competitive landscape requires addressing issues like data ownership, intellectual property rights, and the prevention of anti-competitive behaviors. Understanding the implications of AI agents on existing business models is crucial for adaptation and strategic planning. Maintaining fair competition ensures that the benefits of AI are widely distributed.
One potential approach is to develop new regulatory frameworks that are specifically tailored to AI agents. These frameworks could address issues such as data privacy, algorithmic bias, and the potential for market manipulation. They could also include mechanisms for promoting competition and preventing monopolies. Developing tailored regulatory frameworks for AI agents can address data privacy, algorithmic bias, market manipulation, and the promotion of fair competition. These frameworks can help to ensure responsible innovation while mitigating potential risks associated with AI technologies. Clear regulations are essential for fostering a healthy and ethical AI ecosystem.
The Path Forward: Collaboration and Innovation
The development of MCP is a significant step forward in the field of agent governance. However, it’s important to recognize that this is just the beginning. There are still many challenges to overcome, and it will require a collaborative effort from researchers, developers, policymakers, and users to ensure that AI agents are used safely and responsibly. Addressing the challenges in agent governance demands collaborative efforts from researchers, developers, policymakers, and users to ensure safe and responsible AI agent deployment. A unified approach is essential for overcoming obstacles and harnessing the full potential of AI technology. Engagement from all stakeholders is critical for developing effective and ethical AI solutions.
One promising development is the recent release of Google’s Agent2Agent (A2A) protocol. This protocol enables agents built on different platforms to communicate and collaborate with each other. This could lead to the development of more complex and sophisticated AI systems, where multiple agents work together to solve a problem. It could also help to foster a more competitive and innovative AI ecosystem, as developers are able to build agents that can seamlessly integrate with other agents. The release of Google’s Agent2Agent (A2A) protocol fosters communication and collaboration between agents across platforms, potentially leading to the development of sophisticated AI systems and promoting innovation. Facilitating seamless integration among agents enhances collaboration and promotes the creation of advanced AI solutions. This enhances the overall AI ecosystem by encouraging innovation and competition.
As AI technology continues to evolve, it’s crucial to stay ahead of the curve and to develop new governance mechanisms that can address the challenges of the future. This will require a commitment to collaboration, innovation, and a willingness to adapt to the ever-changing landscape of AI. Continued vigilance, collaboration, and adaptability are essential for developing governance mechanisms that address future challenges in the evolving landscape of AI. Proactive approaches and a commitment to innovation are needed to keep pace with advancements in AI technology. Staying ahead of the curve ensures the responsible and ethical deployment of AI.