Redefining Business Core Assets in the LLM Era: Agent Evolution & Collaboration

Redefining Core Business Assets in the LLM Era

The article discusses how, in the age of large language models (LLM), core business assets should be re‑thought as abstractions of agents, tools, prompts, data supplies, fine‑tuned models, and comprehensive evaluation sets. It proposes that an Agent platform should provide registration, construction, tool integration, prompt debugging, model fine‑tuning, and evaluation capabilities.

Agent Definition

Agents are defined as systems capable of completing a bounded range of human tasks. This definition is broad enough that any system—whether it directly involves AI or not—can be considered an agent within a unified framework.

Agent Collaboration Mechanism

Drawing parallels with the evolution from monolithic services to micro‑services, the article outlines a multi‑agent collaboration model that includes task distribution, cooperation modes, and conflict resolution strategies.

Task allocation mechanisms: centralized assignment, distributed negotiation, role‑based division, dynamic re‑allocation.

Cooperation modes: parallel execution, hierarchical orchestration, expert‑level joint problem solving.

Conflict resolution: priority‑based decisions, voting, arbitration agents, rule‑based handling.

It emphasizes that multi‑agent collaboration inevitably follows the same evolutionary path as micro‑service architectures, moving from a single powerful agent to a network of specialized agents.

MCP Protocol: Advantages and Limitations

The MCP (Model‑Call‑Protocol) has become an industry standard for tool invocation, simplifying prompt engineering and enabling model‑level optimizations. However, the protocol is currently thin; it lacks support for network‑level (public vs. private) permission controls, user‑level authentication, and rapid tool onboarding.

Engineering Considerations for Agent Platforms

Key engineering enhancements include:

Public/inner‑network permission segregation.

User‑level access control to restrict tool visibility per user.

Zero‑code conversion of existing services to MCP servers (e.g., via HSF).

Optimized handling of long tool lists through pre‑filtering based on request relevance, possibly using vector similarity or LLM‑driven matching.

Conclusion

With the emergence of products like Manus, AI has entered a new phase. Integrating existing business processes into an agent‑centric architecture is a critical challenge that demands thoughtful design of assets, protocols, and platform capabilities.