MCP Architecture

Introduction

The Model Context Protocol (MCP) is emerging as an essential interoperability standard in the AI agent ecosystem. Developed by Anthropic and adopted by a growing community, MCP defines an open communication protocol between language models and external systems: databases, APIs, files, business services. Where each agent framework historically imposed its own exchange format and conventions, MCP introduces a universal abstraction layer. An agent using LangChain can now communicate with a tool exposed via MCP without knowing its internal implementation, just as a web browser communicates with a server via HTTP without caring about the server-side programming language.

This standardization comes at a pivotal moment in the evolution of AI systems. Autonomous agents — capable of planning, executing actions, and adapting to new situations — are multiplying in professional environments. But this proliferation comes with a structural problem: integrating heterogeneous agents from different frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel) produces fragmented contexts, incompatible exchange formats, and duplicated integration efforts. Each team develops its own connectors, message formats, and context-passing mechanisms. The result is a fragmented technical landscape where interoperability is the exception rather than the rule.

The central problem this project addresses is context fragmentation in multi-agent architectures. When multiple agents collaborate on a complex task, each maintains its own state, interaction history, and working data. Without a standardized sharing mechanism, transitioning from one agent to another requires costly conversions, information loss, and duplicated model calls. A document assistant that must first search for information, then synthesize it, then format the result, potentially calls three different agents with three distinct contexts, with no guarantee that the first agent's context is correctly transmitted to the second. The resulting inconsistencies degrade service quality and complicate debugging.

Our vision is a modular multi-agent architecture based on MCP as the central communication protocol. In this architecture, each agent is an independent component that exposes its capabilities via the MCP protocol. A central orchestrator manages request routing, response aggregation, and overall system consistency. Agents do not communicate directly with each other: all exchanges pass through the orchestrator, which ensures traceability, context persistence, and resilience in case of agent failure. This approach strictly decouples each agent's business logic from communication mechanisms, allowing agents to be replaced, updated, or added without impacting the rest of the system.

The central MCP orchestrator is the heart of the architecture. Implemented in TypeScript to leverage its strong typing and asynchronous ecosystem, it exposes a GraphQL API that serves as the single entry point for clients. Each request is analyzed, decomposed into subtasks if necessary, and routed to the competent agents. The orchestrator maintains a global session state, stores intermediate contexts in a distributed Redis cache, and ensures exchange consistency through a contextual transaction mechanism. Redis plays a crucial role here: it serves as a context cache, a message bus for asynchronous exchanges between agents, and a session store for recovery after incidents.

Specialized agents constitute the functional layer of the architecture. Each agent exposes a set of capabilities — or tools — via the MCP protocol. These capabilities are described in a standardized way: name, input parameters, output format, usage contract. A document search agent exposes capabilities such as search, getDocument, and extractEntities. A synthesis agent exposes summarize, reformulate, and translate. A formatting agent exposes toPDF, toMarkdown, and toJSON. This standardization allows the orchestrator to dynamically discover available capabilities and build complex workflows through composition, without prior manual configuration.

Integration with existing agent frameworks is a major focus of this project. LangChain, AutoGen, and CrewAI are the three dominant frameworks for building agents, each with its philosophy and strengths. LangChain excels with its ecosystem of chains and ready-to-use tools. Microsoft's AutoGen focuses on multi-agent conversation with specialized roles and feedback loops. CrewAI offers a team-based orchestration model with agents having roles, responsibilities, and hierarchies. Our approach does not seek to replace these frameworks but to make them cooperate: each framework can expose its agents via MCP, and a single orchestrator can coordinate LangChain, AutoGen, and CrewAI agents within one unified workflow.

Performance of MCP architectures is evaluated along three axes: latency, scalability, and reliability. Latency measures the end-to-end time of a request, from reception by the orchestrator to delivery of the final response. In a multi-agent system, each hop between agents adds latency: MCP negotiation, context serialization/deserialization, model inference, response. Optimization involves the Redis cache which avoids rebuilding context at each step, parallelization of calls to independent agents, and response streaming via GraphQL Subscriptions. Scalability is ensured by Kubernetes deployment: each agent can be replicated horizontally according to load, with auto-scaling based on Redis queue depth. Reliability relies on failure recovery mechanisms, configurable timeouts per capability, and circuit breakers that isolate a failing agent without impacting the rest of the system.

Kubernetes deployment is the target infrastructure for the reference architecture. Each agent is packaged in an independent Docker container, with its own dependencies and configuration. Deployments are managed via configurable Helm Charts, with profiles per environment (development, test, production). The central orchestrator is deployed as a full service, with multiple replicas for high availability. Redis is deployed as a cluster with disk persistence and replication for resilience. The GraphQL API is exposed via an Nginx ingress with TLS termination, per-client rate limiting, and JWT authentication. Prometheus and Grafana provide system-wide monitoring: latency per capability, error rate per agent, Redis queue saturation, pod memory consumption.

Use Cases

Concrete use cases cover varied professional scenarios. Intelligent document assistants represent the first field of application: a search agent queries a vector database, an analysis agent extracts key concepts, a synthesis agent produces a structured summary, and a delivery agent formats the response according to the output channel (email, PDF report, Slack message). Business process automation constitutes a second use case: a classification agent routes incoming requests, a validation agent checks attachments and metadata, an execution agent triggers actions in the CRM and ticketing system via dedicated MCP connectors. In both cases, the MCP architecture guarantees that each agent has the necessary context, transitions are seamless, and the whole remains modifiable without regression.

The benefits of this architecture are tangible at several levels. Interoperability is the fundamental benefit: agents developed with different frameworks, by different teams, in different languages, can collaborate within the same system without ad hoc integration. Extensibility follows directly from modularity: adding a new agent simply means implementing its MCP interface and registering it with the orchestrator, without modifying any existing agents. Exchange standardization simplifies maintenance, reduces integration errors, and allows replacing one agent with another as long as the MCP contract is respected. Finally, traceability is ensured: each exchange between agents is logged in Redis with a session identifier, enabling debugging, auditing, and interaction replay.

Perspectives

The evolution prospects of the MCP protocol are promising and are actively monitored in this project. The protocol itself is evolving rapidly: the specification is enriched with new capability types, advanced discovery mechanisms, and quality-of-service contracts. Adoption by major model and framework providers is accelerating: OpenAI, Google, and Microsoft have announced their intention to support MCP in their tools. In the longer term, MCP could become the de facto standard for AI agent interoperability, playing for multi-agent systems the role that HTTP played for the web. This research project contributes to this evolution by documenting implementation patterns, identifying current protocol limitations, and proposing improvements for demanding professional use cases.