White Paper

Integrating Multi-Agent Systems

The evolution of enterprise architecture: scaling AI through the Model Context Protocol and Agent2Agent.

The Rise of Multi-Agent Systems

For years, the dominant approach to enterprise AI relied on specialised, often monolithic algorithms designed to provide predictive responses based on rigidly structured data. While sufficient for linear business scenarios, these systems often struggle with the complexity and operational rigidity required by modern dynamic processes.

To overcome these limits, architects are shifting focus towards Multi-Agent Systems (MAS). Unlike standalone AI models, MAS function as integrated ecosystems where autonomous agents cooperate, negotiate, and plan strategies to tackle complex problems that require decomposition into distributed sub-tasks.

Integration is Crucial for Effective Multi-Agent Systems

A multi-agent system conceived in this way is more than just an assembly of independent entities: it is an integrated ecosystem in which cooperation, negotiation, communication, and collective learning make it possible to tackle complex and dynamic problems.

The true power of a multi-agent system lies not in the individual intelligence of a single agent, but in the quality of its integrations. Without robust protocols, a system remains a disjointed collection of isolated entities incapable of generating collective intelligence.

The Connective Tissue: Integration Protocols

Two emerging open standards are currently defining the landscape: the Model Context Protocol and Agent2Agent.

Model Context Protocol (MCP)
MCP standardises the connection between AI models and external data (APIs, files, business systems). It utilises a strict Host-Client-Server architecture to decouple the agent's reasoning logic from the engineering of the tools it uses, solving the problem of context management.
Agent2Agent (A2A)
A2A acts as a "lingua franca" for collaboration between autonomous agents. It enables the creation of dynamic teams where agents discover each other via Agent Cards (digital identity files) and delegate tasks based on specific capabilities.

When used together, MCP acts as the lens through which an individual agent interprets its context, while A2A acts as the distributed neural network that allows multiple agents to compare notes and plan jointly.

What is Reply’s AaaT Agent Network?

Reply has developed the AaaT (Agent as a Tool) Agent Network, a modular framework for orchestrating distributed intelligent systems via a central coordination layer.

Coordinator (AaaTServerNetwork): agent discovery + deterministic routing
Agents as services: standalone apps (e.g., FastAPI) with unique IDs; failures are isolated
Async communication: non-blocking task submission for long-running work

To prevent "hallucinations" caused by overloading the context window, the system separates memory types:

Short-term: only the current question + recent responses go into the model context
Long-term: a network function fetches the status of past/ongoing tasks across the network

The Internet of Agents

The evolution of these architectures points towards an Internet of Agents (IoA). Just as the web connected people, the IoA aims to connect heterogeneous agents into a resilient, self-organising infrastructure. In this vision, agents will no longer live in closed ecosystems.

Publish Capabilities
Use rich, standardised descriptions (ontologies) to advertise their skills and domain knowledge.
Form Dynamic Teams
Automatically compose nested teams or temporary coalitions to solve specific sub-problems.
Collaborate via Semantics
IoA uses semantic embeddings and knowledge graphs to match complex requests with the right agent profiles.

This transition aims to transform isolated multi-agent systems into a distributed, resilient infrastructure capable of generating value through large-scale cooperation.

Insights from the Whitepaper

How do MCP and A2A work together in practice?

In Reply’s approach, MCP acts as the individual toolkit that grants a single agent the ability to interact with data and software. A2A serves as the coordination layer, enabling that agent to communicate with, delegate tasks to, or discover other agents. In a fully integrated system, MCP ensures an agent possesses the correct context to execute a task, while A2A allows for the handoff of results to the next specialist in the chain, creating a synergy where contextual understanding becomes collective.

How does the A2A protocol replace static integrations?

A2A moves away from rigid, hard-wired connections towards a dynamic "discovery" model. It utilises Agent Cards—publicly accessible JSON files—that describe an agent’s identity, capabilities, and conditions of use. This allows an orchestrator to search for and select the most suitable collaborator for a given task in real-time, functioning similarly to a marketplace of abilities rather than a fixed pipeline.

What security measures are essential for protecting Multi-Agent Systems?

How can the computational costs of large-scale agent networks be optimised?

Running extensive networks of agents on complex models entails significant costs. Optimisation strategies include the use of hybrid architectures, where lightweight models handle routing and filtering while powerful models are activated only for complex reasoning. Additionally, techniques such as model distillation and semantic caching—storing frequent responses to avoid redundant processing—are recommended to reduce resource consumption.

How does the adoption of MAS impact organisational structures?

The shift to Multi-Agent Systems facilitates a transition from vertical chains of command to networks of distributed intelligence. Decision-making power becomes distributed throughout the operational network, transforming the enterprise into a "widespread cognitive organism". This allows for "co-decision" models where human teams and agents collaborate continuously, rather than relying solely on centralised nodes.

What are the trade-offs between centralised and decentralised agent architectures?

Centralised networks rely on a common knowledge base and offer uniformity and governance, yet they introduce risks of bottlenecks and single points of failure. Conversely, decentralised models offer greater robustness and scalability but entail higher complexity in ensuring convergence and global coherence among the agents. In many enterprise scenarios, a hybrid approach prevails, where critical components are governed centrally while high-frequency processes are distributed.

What makes asynchronous communication critical for scaling Agent Networks?

Initial experiments with synchronous communication revealed significant fragility; latency accumulation and timeouts could cause system-wide deadlocks . By shifting to an asynchronous model—where tasks are submitted without blocking the system for an immediate response—the architecture supports parallelism and domain specialisation. This ensures the network remains robust and capable of handling multiple complex operations simultaneously.

A Fast-Evolving Context

As the market of Multi-Agent Systems evolves at a rapid pace, Reply continues to experiment with and adopt multi-agent systems for customers across various industries. The focus remains on transforming these architectures from laboratory prototypes into production-ready ecosystems, ensuring that organisations can leverage distributed intelligence to build more resilient and adaptive business models.