# The Orchestration of Multi-Agent Systems: Architectures, Protocols, and Enterprise Adoption
**Authors**: Apoorva Adimulam, Rajesh Gupta, Sumit Kumar
Abstract
Orchestrated multi-agent systems represent the next stage in the evolution of artificial intelligence, where autonomous agents collaborate through structured coordination and communication to achieve complex, shared objectives. This paper consolidates and formalizes the technical composition of such systems, presenting a unified architectural framework that integrates planning, policy enforcement, state management, and quality operations into a coherent orchestration layer. Another primary contribution of this work is the in-depth technical delineation of two complementary communication protocols—the Model Context Protocol, which standardizes how agents access external tools and contextual data, and the Agent-to-Agent protocol, which governs peer coordination, negotiation, and delegation. Together, these protocols establish an interoperable communication substrate that enables scalable, auditable, and policy-compliant reasoning across distributed agent collectives. Beyond protocol design, the paper details how orchestration logic, governance frameworks, and observability mechanisms collectively sustain system coherence, transparency, and accountability. By synthesizing these elements into a cohesive technical blueprint, this paper provides comprehensive treatments of orchestrated multi-agent systems—bridging conceptual architectures with implementation-ready design principles for enterprise-scale AI ecosystems.
I Introduction
The landscape of LLM-powered agents has undergone a marked transformation. Early deployments prioritized isolated, task-specific agents, highly specialized systems with narrow operating scopes. However, contemporary trends point toward ecosystems of collaborating agents. This transition mirrors broader developments in distributed computing, where value emerges less from individual capabilities and more from orchestrated interactions within a collective.
Several technical drivers explain why the pivot to multi-agent architectures is emerging now including:
- scalability limits of LLMs, where context length and reasoning bottlenecks constrain performance
- the need for specialization versus generalization, enabling modular agents optimized for specific domains to be composed dynamically
- advances in communication protocols, with message-passing abstractions and nascent standards for inter-agent APIs
- economic efficiency, as distributed collectives of smaller agents often outperform costly all-purpose deployments as demonstrated in [1], [2]
Recent industry signals underscore the momentum of this transition. At the enterprise level, PwC has positioned its Agent OS [3] as a switchboard for multi-agent coordination, emphasizing composability and interoperability across enterprise functions. Similarly, Accenture’s Trusted Agent Huddle [4] introduces governance mechanisms for secure, cross-organizational workflows and aligns with the emerging Agent-to-Agent (A2A) protocol. At the same time, research and development within the technical ecosystem has accelerated with frameworks such as LangChain, AutoGen, IBM Watsonx Orchestrate, and Google’s Agent Development Kit providing modular infrastructure for coordination, negotiation, and role-based task delegation. Collectively, these initiatives signal rapid movement toward standardization and operational readiness.
The remainder of this paper progresses from conceptual foundations to practical realization. Section II traces the evolution of agentic systems toward orchestrated collectives, while Section III establishes the architectural composition of multi-agent systems and outlines their essential components. Sections IV–VII expand on these components in depth, examining how specialized agent roles, orchestration logic, communication protocols, and governance mechanisms together form the technical backbone of orchestrated intelligence. Section VIII presents real-world case studies that demonstrate the practical impact of these architectures across industries. Section IX discusses the challenges and risks associated with scaling multi-agent systems, and the future research directions aimed at improving efficiency, reliability, and interoperability. Finally, Section X concludes with key insights on how orchestrated multi-agent systems can serve as a foundation for enterprise-scale AI ecosystems.
II Evolution of Agentic Systems
Agentic systems are founded on the principle of autonomous entities that can perceive their environment, make decisions, and take actions to achieve specific goals. Defined by autonomy, reactivity, proactivity, and social ability, they extend beyond scripted automation to operate adaptively.
Early deployments relied on single agents. These monolithic systems, with no coordination overhead, were well-suited to narrow tasks such as customer support chatbots that resolve FAQs, financial bots generating daily reports, or personal productivity assistants handling email and calendar management. Their reliability made them effective in bounded contexts, but they lacked scalability and adaptability for complex or dynamic environments.
To address these limitations, research and practice shifted toward loosely coupled agentic systems. In such systems, multiple agents operate in parallel with relative independence, and minimal interaction. This architecture enables specialization and the emergence of collective behaviors that a single agent cannot achieve. Recent examples include scientific research assistants [5] where literature-retrieval, reasoning, and validation agents collaborate to accelerate discovery; collaborative AI coding environments [6] where distinct agents write, review, and test code; news pipelines [7] where aggregation, fact-checking, and synthesis are distributed across agents; and autonomous driving ecosystems [8] where perception, navigation, and coordination agents cooperate loosely to ensure safe operation. Fig. 1 represents the discussed evolution of agentic systems.
<details>
<summary>EvolutionAgenticSystems.png Details</summary>
### Visual Description
## Diagram: Evolution of Agentic Systems
### Overview
The image is a diagram illustrating the evolution of agentic systems, progressing from a single agent to loosely coupled agents and finally to an orchestrated multi-agent system. The diagram uses boxes and arrows to represent agents, tasks, and the flow of information or control.
### Components/Axes
* **Titles:**
* SINGLE AGENT (top-left)
* LOOSELY COUPLED AGENTS (top-center)
* ORCHESTRATED MULTI-AGENT SYSTEM (top-right)
* EVOLUTION OF AGENTIC SYSTEMS (bottom, spanning the width)
* **Elements:**
* Agent (represented by a rounded rectangle with an icon of a face)
* Task (represented by a rounded rectangle)
* Tool (represented by a rounded rectangle)
* Prompt (represented by a rounded rectangle)
* Memory (represented by a rounded rectangle)
* LLM (represented by a rounded rectangle)
* Orchestration layer (represented by a rounded rectangle)
* Arrows (indicating the flow of information or control)
### Detailed Analysis
**1. SINGLE AGENT:**
* An "Agent" is connected to a "Task" via an arrow.
* The "Agent" interacts with "Tool", "Prompt", and "Memory".
* "Prompt" and "Memory" are connected to "LLM" via arrows.
* "LLM" is connected to "Tool" via arrows.
* "Tool" is connected to "Agent" via a curved arrow.
**2. LOOSELY COUPLED AGENTS:**
* "Agent 1" is connected to "Task 1" via an arrow.
* "Agent 2" is connected to "Task 2" via an arrow.
**3. ORCHESTRATED MULTI-AGENT SYSTEM:**
* An "Orchestration layer" contains "Agent 1" and "Agent 2".
* "Agent 1" and "Agent 2" are connected to the "Orchestration layer" via bidirectional arrows.
* "Agent 1" is connected to "Task 1" via an arrow.
* "Agent 2" is connected to "Task 2" via an arrow.
### Key Observations
* The diagram illustrates a progression from a single agent performing a task to multiple loosely coupled agents each performing a task, and finally to an orchestrated multi-agent system where agents are coordinated by an orchestration layer.
* The single agent system involves interaction with tools, prompts, memory, and an LLM.
* The loosely coupled agents operate independently.
* The orchestrated multi-agent system introduces a coordination layer between the agents.
### Interpretation
The diagram depicts the evolution of agentic systems, highlighting the increasing complexity and coordination as systems move from single agents to orchestrated multi-agent systems. The single agent system emphasizes the role of tools, prompts, memory, and LLMs in enabling the agent to perform its task. The loosely coupled system demonstrates independent agents working on separate tasks. The orchestrated system introduces a layer of coordination, suggesting a more complex and potentially more efficient way of managing multiple agents. This evolution suggests a move towards more sophisticated and capable AI systems that can handle complex tasks through coordinated effort.
</details>
Figure 1: Evolution of Agentic Systems
III Architectural Composition of Multi-Agent Systems
Building an orchestrated multi-agent system (MAS) involves more than simply connecting multiple autonomous agents. It requires designing specialized roles, establishing a coordination layer that governs their interactions, and defining clear communication protocols that allow agents to exchange information effectively.
Specialized agents operate as autonomous components that execute well-defined tasks within the system, each contributing a distinct capability toward the collective objective. Their interactions are coordinated through an orchestration layer that determines execution order, manages dependencies, and aligns individual outputs into a coherent operational flow. The exchanges rely on communication protocols that standardize how information is represented and transferred across agents, ensuring semantic consistency and enabling the orchestration layer to maintain synchronized and interpretable system behavior. When these foundational components are applied in practice, they collectively enable complex, domain-specific workflows that require coordinated intelligence across multiple decision points.
The following sections examine these technical elements in greater depth, focusing on agent specialization, orchestration mechanisms, and communication strategies as the foundational components of multi-agent architectures.
IV Specialized Agents
In a multi-agent system, specialized agents are autonomous components designed to perform narrowly scoped, role-specific tasks within the broader architecture. Each agent typically incorporates a large language model as its cognitive core, enabling it to perceive inputs, reason about them, and act within clearly defined operational boundaries. By assigning distinct roles such as retrieval, reasoning, validation, or monitoring, the system decomposes complex objectives into smaller, coordinated subtasks. This division of labor promotes modularity and collaboration, allowing agents to complement one another’s capabilities, reduce redundancy, and achieve outcomes that surpass those of a single, general-purpose agent. Through such specialization, a multi-agent system attains higher precision, scalability, and robustness while preserving clarity of function and accountability across its components.
Below are the key categories of specialized agents:
- Worker Agents - Worker agents represent the most basic but essential type. They are responsible for carrying out well-defined tasks such as a Retrieval-augmented generation (RAG) pipeline. In practice, some worker agents are stateless, handling each request independently without retaining context, while others are stateful, tracking progress across multiple steps in a workflow. In large systems, worker agents often operate in parallel, with each specializing in a narrow sub-domain. In a financial underwriting workflow, individual worker agents may extract applicant data from loan documents, compute preliminary credit scores, or generate draft risk assessments that downstream agents validate and consolidate. These agents form the execution layer of the system, performing domain-specific computations that feed subsequent validation and oversight processes.
- Service Agents - Service agents provide shared operational capabilities that other agents depend on during workflow execution. They act as reusable utilities within the multi-agent ecosystem, performing tasks such as quality assurance, compliance enforcement, diagnostics, or automated recovery.
In the context of financial underwriting, quality assurance agents can verify extracted customer data and cross-check it against compliance requirements. Diagnostic agents can inspect inconsistencies or missing data, trace the issues to the responsible module, and generate a structured error report that informs corrective actions. Healing agents can extend this capability by rerunning failed extractions or resetting workflow states to restore normal operation. While healing agents focus on the active system state, upgrade scheduler agents manage version transitions of scoring components and related workflows, ensuring that updates are deployed seamlessly without disrupting ongoing operations.
- Support Agents - Complementing the service agents, support agents operate at a supervisory and analytical level. While service agents provide in-line operational utilities during workflow execution, support agents focus on meta-level oversight by monitoring system behavior, analyzing outcomes, and managing data flows that inform orchestration and optimization. Their function is to maintain the overall health, transparency, and adaptability of the system under varying operational conditions. In the financial underwriting scenario, monitoring agents track decision latency, detect risk model drift, and visualize overall portfolio health for both AI orchestrators and human supervisors. Analytics agents evaluate approval-rate patterns and compliance anomalies, while data agents refresh applicant datasets to maintain currency and accuracy.
<details>
<summary>Specializedagents.png Details</summary>
### Visual Description
## Diagram: Orchestration Layer
### Overview
The image is a diagram illustrating an orchestration layer and its interaction with different agents. It shows the flow of tasks, validation, and monitoring between the orchestration layer and worker, service, and support agents.
### Components/Axes
* **Title:** Orchestration layer (located at the top center, inside a blue rounded rectangle)
* **Central Node:** A black circle representing a central point of interaction.
* **Arrows:** Indicate the direction of flow.
* **Labels:**
* Feedback (above the central node, pointing upwards)
* Task Assignment (left of the central node, pointing downwards)
* Validation & reliability (below the central node, pointing downwards)
* Monitoring & insights (right of the central node, pointing downwards)
* **Agent Boxes:** Three blue rounded rectangles representing different agents.
* Worker Agent (bottom-left)
* Service Agent (bottom-center)
* Support Agent (bottom-right)
### Detailed Analysis
* **Orchestration Layer:** The top-level component that orchestrates tasks and receives feedback.
* **Feedback:** An arrow points upwards from the central node to the Orchestration Layer, indicating feedback flow.
* **Task Assignment:** An arrow points downwards from the central node to the Worker Agent, indicating task assignment.
* **Validation & Reliability:** An arrow points downwards from the central node to the Service Agent, indicating validation and reliability processes.
* **Monitoring & Insights:** An arrow points downwards from the central node to the Support Agent, indicating monitoring and insights flow.
* **Worker Agent:**
* Label: Worker Agent (with a gear icon)
* Description: Atomic domain-specific tasks. Eg: information retrieval summarization.
* **Service Agent:**
* Label: Service Agent (with a shield icon)
* Description: Validate data, compliance checks, and recovery. Eg: QA Validator, Healing Agent.
* **Support Agent:**
* Label: Support Agent (with a question mark and monitor icon)
* Description: Monitor system behaviors and analytical feedback. Eg: Anomaly detector, performance analytics agent.
### Key Observations
* The Orchestration Layer is the central point of control.
* The diagram illustrates a cyclical flow: tasks are assigned, validated, monitored, and feedback is provided to the Orchestration Layer.
* Each agent has a specific role: task execution, validation, and monitoring.
### Interpretation
The diagram represents a system architecture where an orchestration layer manages and coordinates the activities of different agents. The flow of information and tasks is clearly defined, with feedback loops ensuring continuous improvement and reliability. The agents perform specialized functions, contributing to the overall system's functionality. The diagram highlights the importance of validation, monitoring, and feedback in maintaining a robust and reliable system.
</details>
Figure 2: Specialized agents in a Multi-Agent System
Fig. 2 summarizes the discussed categories of specialized agents in a multi-agent systems. Across these categories of specialized agents, recent research [9] and enterprise applications demonstrate that coordinated role differentiation significantly enhances the reliability and scalability of multi-agent systems.
V Orchestration Layer for Coordinated Multi-Agent Operations
The agent orchestration layer forms the control plane of a multi-agent system, transforming autonomous components into a coherent, goal-directed collective. Without orchestration, even highly capable agents risk duplication of effort, logical inconsistency, or unbounded autonomy that diverges from the system’s objectives. It interprets system-level objectives, decomposes them into actionable subtasks, coordinates their execution, and ensures that every output produced aligns with policy, context, and quality requirements.
V-A Planning and Policy Management
At the heart of the orchestration layer are the planning and policy units, which convert high-level objectives into a structured execution plan.
Their purpose is twofold: the planning unit operates as a goal-decomposition engine that determines what tasks need to be done and in what order, while the policy unit embeds domain and governance constraints to define how these tasks are performed. In a financial underwriting workflow, the planning unit assigns tasks such as data extraction and credit scoring to the corresponding worker agents. The policy unit defines operational and governance constraints and coordinates with the control unit (described in the following subsection) to ensure service agents enforce them throughout execution
Together, they translate abstract goals into a directed execution model that defines who performs which task, in what sequence, under what rules, and with what oversight.
V-B Execution and Control Management
Once tasks are planned and assigned, the orchestration layer operates as a distributed control system that transitions specialized agents through phases of initialization, execution, validation, and completion.
Here, the execution unit ensures the smooth operation of the designated tasks performed by worker agents and manages telemetry data collection by support agents. The telemetry data is relayed to the control unit, which may delegate remediation or verification tasks to service agents to maintain operational stability. The control unit also manages concurrency and dependency across workflows, allowing parallel execution and synchronization at key checkpoints to preserve consistency. Additionally, it handles task prioritization and dynamic resource allocation to balance throughput, cost, and determinism across varying workloads.
V-C State and Knowledge Management
For the control unit to achieve synchronization and maintain continuity across workflows, the orchestration layer relies on the state and knowledge management component. This component functions both as a data bus and a knowledge repository.
The state unit manages checkpoints, workflow progress, agent states, and activity logs. Support agents monitor state changes and performance anomalies, while service agents may be invoked to restore checkpoints from the state unit to preserve workflow integrity. The knowledge unit manages contextual and domain-specific information by connecting to external data sources and exposing them as a retrievable context. This ensures that worker agents and orchestration components operate with accurate and aligned information.
This separation of operational state from knowledge state preserves modularity, contextual consistency, and system coherence.
V-D Quality and Operations Management
Using telemetry, state updates, and contextual data generated by other orchestration units, the quality and operations management component evaluates system performance, validates outcomes, and ensures that orchestrated activities remain compliant and continually optimized. While the control unit focuses on execution stability and the policy unit defines and enforces operational constraints during execution, this component governs verification and optimization of results after execution across the orchestration layer.
The component validates aggregated outputs against defined schemas before integrating them into the shared state, preventing invalid data from propagating through workflows. When inconsistencies or violations are detected, it updates the state accordingly and may invoke service agents to perform diagnostic or remediation actions, ensuring sustained integrity and compliance.
The subsystem also monitors metrics such as latency, throughput, and success rate, using anomaly detection to identify deviations and trigger preemptive interventions. It also supports controlled deployment, testing, and sandboxing new components to maintain stability as agents evolve. Together, these mechanisms ensure resilient, auditable, and continuously improving multi-agent operations.
V-E Closing Discussion
The orchestration model is exemplified in a financial institution’s credit-risk and fraud detection workflow, where specialized agents are coordinated to ensure consistency and compliance. Incoming loan applications are decomposed by the planning unit into subtasks such as data extraction, risk assessment, compliance review, and fraud screening, while the policy unit embeds governance constraints, including lending regulations and institutional risk thresholds. The execution and control component manages concurrent task execution, collects telemetry, and invokes service agents for recovery when needed. The state and knowledge management component maintains applicant states, historical records, and regulatory references for contextual continuity, while the quality and operations component validates results against policy criteria and applies performance insights to optimize future workflows.
Collectively, these mechanisms show that reliability in multi-agent systems arises not only from intelligent agents but from the orchestration layer that governs planning, execution, and validation, enabling scalable and policy-compliant performance.
VI Communication Protocols in Orchestrated Systems
Building on the orchestration framework, the agent communication layer operationalizes coordination by enabling agents and external tools to exchange information, control signals, and shared context. While orchestration defines who acts and when, communication ensures those actions remain synchronized and interpretable. Traditional protocols relied on static request–response exchanges and lacked mechanisms for context sharing or policy enforcement. To address these limitations, two emerging standards—the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol—establish structured, interoperable communication for tool interaction and peer specialized agentic collaboration. Later subsections explore these protocols in more detail.
VI-A Model Context Protocol
The Model Context Protocol (MCP) [10] provides the standardized communication interface that operationalizes the previously described orchestration flow between agents and external systems like tools, data services, and contextual repositories. As illustrated in Fig. 3, MCP mediates every external invocation through a defined interface that enforces schema consistency, access control, and auditability. This allows the execution unit to dispatch tasks confidently, ensuring that each agent’s external interactions conform to orchestration policies and operational rules.
<details>
<summary>BeforeVSAfter_v3.png Details</summary>
### Visual Description
## System Diagram: MCP Architecture
### Overview
The image presents a system diagram illustrating the architecture of a system "Before MCP" and "After MCP". It shows the components and their interactions, focusing on the role of an LLM (Large Language Model) or Orchestrator and the introduction of MCP (presumably a protocol or system component).
### Components/Axes
**Header:**
* "Before MCP"
* "After MCP"
**Top Section (Before MCP):**
* LLM or Orchestrator: A central component.
* Tool 1, Resource, Tool 2, Resource: Represented as separate entities.
* Unique API: Label on arrows indicating the communication method.
**Bottom Section (After MCP):**
* MCP Host / Orchestrator App: A central component.
* LLM: A separate component connected to the MCP Host.
* MCP Protocol (JSON-RPC, bidirectional): Describes the communication protocol.
* MCP Server A (x2): Each containing "Prompts", "Tools", and "Resource".
### Detailed Analysis or ### Content Details
**Before MCP:**
* The "LLM or Orchestrator" communicates with "Tool 1", "Resource", "Tool 2", and "Resource" via "Unique API" calls.
* The arrows indicate a one-way communication from the "LLM or Orchestrator" to each of the other components.
**After MCP:**
* The "MCP Host / Orchestrator App" communicates with two instances of "MCP Server A" using "MCP Protocol (JSON-RPC, bidirectional)".
* Each "MCP Server A" contains "Prompts", "Tools", and "Resource".
* The "LLM" is a separate component connected to the "MCP Host / Orchestrator App".
### Key Observations
* The diagram highlights a shift from direct "Unique API" calls to a more structured communication via "MCP Protocol".
* The introduction of "MCP Server A" components suggests a modularization or encapsulation of functionalities.
* The "LLM" is integrated differently in the "After MCP" architecture, being a separate component connected to the "MCP Host".
### Interpretation
The diagram illustrates a transition in system architecture, likely aimed at improving modularity, communication efficiency, or standardization. The "Before MCP" architecture appears to have a central "LLM or Orchestrator" directly interacting with various tools and resources via unique APIs. This could lead to tight coupling and potential maintenance challenges.
The "After MCP" architecture introduces an "MCP Host / Orchestrator App" that communicates with "MCP Server A" instances using a standardized "MCP Protocol (JSON-RPC, bidirectional)". This suggests a move towards a more service-oriented architecture, where functionalities are encapsulated within "MCP Server A" instances and communication is standardized. The separation of the "LLM" in the "After MCP" architecture could indicate a decoupling of the language model from the core orchestration logic, allowing for easier updates or replacements of the LLM.
</details>
Figure 3: Comparison of agent communication without MCP and with MCP
<details>
<summary>MCP.png Details</summary>
### Visual Description
## System Diagram: Model Context Protocol
### Overview
The image is a system diagram illustrating the Model Context Protocol (MCP) architecture. It depicts the flow of data and interactions between different layers, including the Agentic Layer, MCP Layer, Databases, Tools, Workflows, and External Resources Layer.
### Components/Axes
* **Title:** MODEL CONTEXT PROTOCOL
* **Layers (from top to bottom):**
* AGENTIC LAYER (MCP CLIENT)
* MCP LAYER
* DATABASES, TOOLS, WORKFLOWS
* EXTERNAL RESOURCES LAYER
* **Components within AGENTIC LAYER:**
* Agent A (with a robot icon)
* Agent B (with a robot icon)
* **Components within MCP LAYER:**
* Schema Validator
* Session Manager
* Security x access control
* Audit logger & versioning
* MCP Servers 1
* MCP Servers 2
* MCP Servers 3
* **Components within DATABASES, TOOLS, WORKFLOWS:**
* DATABASES
* TOOLS
* WORKFLOWS
* **Arrows:** Indicate the direction of data flow and interactions between layers and components.
* "Structured JSON Responses" (from MCP LAYER to AGENTIC LAYER)
* "Structured JSON RPC Calls (via a MCP Client SDK)" (from AGENTIC LAYER to MCP LAYER)
* "Transaction" (from MCP Servers 1 to DATABASES)
* "Structured JSON Response" (from MCP Servers 1 to DATABASES)
* "Tool Invocation" (from MCP Servers 2 to TOOLS)
* "Structured JSON Response" (from MCP Servers 2 to TOOLS)
* "Workflow Trigger" (from MCP Servers 3 to WORKFLOWS)
* "Structured JSON Response" (from MCP Servers 3 to WORKFLOWS)
### Detailed Analysis or ### Content Details
* **AGENTIC LAYER (MCP CLIENT):**
* Contains two agents, Agent A and Agent B.
* Sends "Structured JSON RPC Calls (via a MCP Client SDK)" to the MCP LAYER.
* Receives "Structured JSON Responses" from the MCP LAYER.
* **MCP LAYER:**
* Contains components for schema validation, session management, security and access control, and audit logging and versioning.
* Contains three MCP Servers: MCP Servers 1, MCP Servers 2, and MCP Servers 3.
* MCP Servers interact with DATABASES, TOOLS, and WORKFLOWS respectively.
* **DATABASES, TOOLS, WORKFLOWS:**
* Represent the backend systems that the MCP interacts with.
* DATABASES receive "Transaction" and "Structured JSON Response" from MCP Servers 1.
* TOOLS receive "Tool Invocation" and "Structured JSON Response" from MCP Servers 2.
* WORKFLOWS receive "Workflow Trigger" and "Structured JSON Response" from MCP Servers 3.
* **EXTERNAL RESOURCES LAYER:**
* Represents the external resources that the entire system relies on.
### Key Observations
* The diagram illustrates a layered architecture with clear separation of concerns.
* The MCP LAYER acts as an intermediary between the agents and the backend systems.
* The flow of data is primarily through Structured JSON messages.
* Each MCP Server is responsible for interacting with a specific type of backend system (Databases, Tools, or Workflows).
### Interpretation
The diagram provides a high-level overview of the Model Context Protocol architecture. It demonstrates how agents interact with backend systems through a centralized MCP layer. The MCP layer provides essential services such as schema validation, session management, security, and auditing. The use of Structured JSON for communication enables interoperability between different components. The architecture promotes modularity and scalability by separating the agent logic from the backend system interactions.
</details>
Figure 4: Integration of the MCP within the orchestration architecture
Within the orchestration architecture, MCP follows a client–server design in which agents or orchestrators act as clients that request external capabilities such as tools, resources, or prompts, while connected systems expose these as standardized, callable services. Through MCP, the planning and control units translate defined tasks into executable tool calls and govern how and when agents access these resources in compliance with policy constraints. MCP’s session management supports both stateless and stateful exchanges, allowing context continuity across multi-step workflows. These exchanges are logged and synchronized with orchestration state, enabling the state and knowledge management component to maintain consistency and allowing the quality and operations component to verify compliance and alignment with expected results.
As shown in Fig. 4, MCP functions as the operational bridge between high-level orchestration plans and low-level tool execution. It converts planned objectives into structured, policy-aligned invocations and feeds execution data back into orchestration memory and quality loops. Extensions such as ScaleMCP [11] dynamically synchronize tool inventories across agents, while AgentMaster [12] integrates MCP with inter-agent communication frameworks such as A2A to support multimodal collaboration and information retrieval within orchestrated multi-agent systems.
VI-B Agent-to-Agent Protocol
While MCP governs how agents interact with tools and data, the Agent-to-Agent (A2A) protocol [13] defines standardized communication amongst specialized agents themselves. It supports negotiation, delegation, and coordination across distributed ecosystems while maintaining interoperability, traceability, and security [14]. Together, MCP and A2A form the dual foundation of agent communication—MCP for tool access and A2A for peer collaboration (Fig. 5).
<details>
<summary>A2A.png Details</summary>
### Visual Description
## System Diagram: Agent-to-Agent Protocol
### Overview
The image is a system diagram illustrating the Agent-to-Agent (A2A) protocol. It depicts the flow of information and interactions between users, a client, a client agent, and an agent mesh consisting of multiple agent cards. The diagram highlights the communication pathways and the technologies used for interaction.
### Components/Axes
* **Title:** AGENT-TO-AGENT PROTOCOL
* **Nodes:**
* Users (Rounded rectangle)
* Client (Rounded rectangle)
* Client Agent (Robot Icon)
* Agent Card (Robot Icon) - There are three instances of this node within the Agent Mesh.
* Agent Mesh (Rounded rectangle with dotted border)
* **Connections/Flow:**
* Users -> Client (Arrow)
* Client -> Client Agent (Arrow)
* Client Agent -> Agent Mesh (A2A Arrow)
* Client Agent <- Agent Mesh (JSON-RPC Arrow)
* Agent Mesh: Circular arrows between Agent Card instances.
### Detailed Analysis or Content Details
The diagram shows the following flow:
1. **Users** interact with a **Client**.
2. The **Client** communicates with a **Client Agent**.
3. The **Client Agent** interacts with the **Agent Mesh** using the **A2A** protocol.
4. The **Agent Mesh** consists of multiple **Agent Cards** that communicate with each other in a circular fashion.
5. The **Agent Mesh** communicates back to the **Client Agent** using **JSON-RPC**.
The Agent Mesh is represented by a dotted-line bordered rectangle containing three Agent Card icons. Arrows indicate a circular communication pattern between these Agent Cards.
### Key Observations
* The diagram illustrates a multi-agent system where agents within the mesh communicate with each other.
* The Client Agent acts as an intermediary between the Client and the Agent Mesh.
* JSON-RPC is used for communication from the Agent Mesh back to the Client Agent.
### Interpretation
The diagram describes a system where user requests are processed through a client and a client agent, which then interacts with a network of agents (Agent Mesh). The Agent Mesh likely performs distributed processing or collaborative tasks, with individual agents (Agent Cards) communicating to achieve a common goal. The use of JSON-RPC suggests a standardized way for the Agent Mesh to return results or updates to the Client Agent. The A2A protocol is the primary communication method between the Client Agent and the Agent Mesh.
</details>
Figure 5: Agent-to-Agent Protocol
Through A2A, worker agents can delegate subtasks or share intermediate results, service agents can communicate diagnostic information or recovery status, and support agents can broadcast telemetry or performance insights that inform collective progress. This peer-level exchange ensures that task dependencies are dynamically managed and that agents can resolve interdependencies without requiring centralized intervention. The control unit supervises these interactions to ensure policy alignment and to maintain synchronization with the broader orchestration plan, while communication records are captured within the state and knowledge management component for traceability and recovery.
A2A employs a peer communication model either direct or mediated through the orchestrator, enabling reliable, authenticated message exchange. Each message carries structured metadata and standardized payloads, ensuring consistency across heterogeneous implementation. Robust security controls, including cryptographic signing and role-based routing, guarantee message integrity and policy compliance.
Although peer-oriented, A2A remains supervised by the orchestration layer, which validates and synchronizes exchanges to maintain coherence with global workflows. Emerging research explores scalable and hybrid architectures that combine A2A and MCP for multimodal, adaptive coordination in enterprise-grade agentic systems.
VII Safety, Governance and Observability
Ensuring the reliability of multi-agent systems depends on safeguards embedded within orchestration and communication mechanisms. Within the orchestration layer, the control and quality and operations management units enforce safety and governance through validation, monitoring, and recovery mechanisms that maintain compliance and operational integrity. Similarly, MCP and A2A protocols embed protective measures such as schema validation, authenticated exchanges, and access control to ensure secure and interpretable communication. Core guardrails mitigate hallucinations and enforce consistency checks to prevent agents from producing unsafe or conflicting outputs. These protections are reinforced by internal audits, event logging, and least-privilege policies that promote transparency, accountability, and traceability across the system. Privacy constraints restrict agents to sharing only task-relevant information. Continuous monitoring, carried out through support agents and the quality and operations management unit, tracks latency, throughput, and correctness to evaluate performance and detect drift. Together, these practices transform multi-agent systems from experimental collectives into dependable, auditable, and continually improving infrastructures that balance autonomy with control.
<details>
<summary>MASArchitecture.png Details</summary>
### Visual Description
## Diagram: Multi-Agent System Architecture
### Overview
The image is a diagram illustrating a multi-agent system architecture. It shows the interaction between different layers and components, including an orchestration layer, agents, a toolkit, shared memory, and external APIs. The diagram uses arrows to indicate the flow of information and communication between these components.
### Components/Axes
* **Title:** MULTI-AGENT SYSTEM ARCHITECTURE
* **Orchestration Layer:** This is the top layer, containing the following components:
* Planning & policies
* Runtime & Resources
* State & Knowledge Mgmt
* Quality & Operations
* Observability Tools
* **Agent Studio:** This is the central area containing the agents:
* Agent 1 (Worker)
* Agent 2 (Service)
* Agent 3 (Helper)
* **Other Components:**
* Toolkit (containing two "Tool" elements)
* Shared Memory & Database
* External API's
* **Arrows:** Indicate the flow of information and communication.
* **Labels:**
* A2A: Indicates agent-to-agent communication.
* MCP: Indicates communication with other components.
### Detailed Analysis or Content Details
* **Orchestration Layer:**
* "Planning & policies" has a downward arrow to "Agent 1".
* "Runtime & Resources" has a downward arrow to "Agent 2".
* "State & Knowledge Mgmt" has a downward arrow to "Agent 3".
* "Quality & Operations" has no visible connections.
* "Observability Tools" has no visible connections.
* **Agent Studio:**
* "Agent 1" (Worker) has an "A2A" arrow pointing towards "Agent 2". It also has a curved arrow originating from "Agent Studio" pointing back to itself.
* "Agent 2" (Service) has an "A2A" arrow pointing towards "Agent 3".
* "Agent 3" (Helper) has a curved arrow originating from "Agent 1" pointing back to "Agent Studio".
* "Agent Studio" has downward arrows to "Audits Traceability" and "Guard Rails".
* **Other Components:**
* "Toolkit" has an "MCP" arrow pointing towards "Agent Studio".
* "Shared Memory & Database" has an "MCP" arrow pointing towards "Agent Studio".
* "External API's" has an "MCP" arrow pointing towards "Agent Studio".
* **Bottom Layer:**
* "Audits Traceability" has no visible connections.
* "Guard Rails" has no visible connections.
### Key Observations
* The diagram illustrates a hierarchical structure, with the Orchestration Layer at the top, followed by the Agent Studio, and then other components.
* The "A2A" arrows suggest direct communication between agents.
* The "MCP" arrows suggest communication between the Agent Studio and other components like the Toolkit, Shared Memory, and External APIs.
* The arrows from the Orchestration Layer to the Agents suggest a control or delegation mechanism.
* The arrows from the Agent Studio to "Audits Traceability" and "Guard Rails" suggest that these are downstream processes or functions.
### Interpretation
The diagram depicts a multi-agent system architecture where an orchestration layer manages and coordinates the activities of multiple agents within an agent studio. The agents interact with each other and with external components like toolkits, shared memory, and external APIs. The system also includes mechanisms for auditing and ensuring guard rails, suggesting a focus on security and compliance. The architecture emphasizes modularity and separation of concerns, with each agent having a specific role (Worker, Service, Helper) and interacting with other components through well-defined interfaces (MCP). The "A2A" communication suggests that agents can collaborate and coordinate their actions to achieve common goals.
</details>
Figure 6: Orchestrated Multi-Agent System Architecture
To summarize, the overall architecture of an orchestrated multi-agent system is shown in Fig. 6. The architecture integrates all core components that enable coordination, communication, and governance across distributed agents. At its foundation are specialized agent types, interacting through standardized protocols such as MCP for tool and data access and A2A for inter-agent collaboration. The orchestration layer oversees planning, execution, and quality control, while observability and governance modules ensure reliability, compliance, and transparency. Together, these elements form a cohesive, scalable framework that operationalizes autonomous intelligence under structured orchestration.
VIII Case Studies
VIII-A Banking, Financial Services and Insurance
Multi-agent AI systems are revolutionizing Banking, Financial Services, and Insurance (BFSI) industry by delivering dramatic efficiency gains and ROI. Insurers are deploying networks of specialized AI agents to automate the labor-intensive underwriting process. For example, autonomous agents studied in [15] now parse insurance applications and supporting documents with over 95% accuracy, enabling much faster policy issuance. In another use-case explored in [15], a mortgage lender integrated Document AI and Decision AI agents to handle loan paperwork, achieving a 20Ă— faster approval process while cutting processing costs by 80%. Similarly, [16] presents a multi-agent automation framework for property claims underwriting, where specialized agents collaborate to evaluate claim documents, assess damage estimates, and validate policy conditions. Such multi-agent systems clearly outperform both manual processes and single-agent automation, delivering a strong value proposition in BFSI.
VIII-B Software Engineering and IT Modernization
Multi-agent systems are also proving their worth in software engineering. A large bank recently applied an agentic AI “digital factory” [17] to modernize its legacy core software, which comprised hundreds of applications. Different agents took on specialized coding tasks: one agent automatically documented existing legacy code, another generated new code modules, others reviewed code written by their peers, and additional agents integrated and tested these modules. This multi-agent architecture allows parallel execution and continuous code quality checks, reducing the coordination burdens that slowed the purely human teams. In practice, this approach led to over a 50% reduction in development time and effort for early-adopter teams at the bank.
VIII-C Cross Industry Adoption
The success of multi-agent AI is spurring widespread adoption across industries. In customer service, companies are reimagining call centers with agentic AI: instead of just assisting human representatives, agents autonomously handle routine inquiries and issues. Studies suggest that up to 80% of common support incidents could be resolved by AI agents without human intervention, cutting resolution times by 60–90% in fully agent-driven workflows. Meanwhile, sectors like healthcare are exploring multi-agent setups where one agent analyzes patient symptoms or medical literature and another suggests treatment plans, all under a doctor’s supervision. From finance and insurance to software development, legal research, and healthcare, multi-agent systems are being rapidly embraced as organizations recognize their measurable performance advantages over manual or single-agent approaches.
IX Challenges, Risks and Future Research
As multi-agent systems scale, key challenges emerge around efficiency, cost, and governance. Coordination among numerous agents can create communication overhead, message congestion, and performance bottlenecks unless workflows are carefully managed. The cost of adoption remains significant, enterprises must invest in orchestration software, skilled engineering teams, and continuous monitoring infrastructure to ensure reliability and compliance. Governance presents another difficulty, as decentralized autonomy complicates oversight and accountability. Risks inherited from large language models, such as hallucination, bias, and data leakage, are magnified when agents interact, raising safety, ethical, and privacy concerns that demand rigorous evaluation and control frameworks.
Future research focuses on making orchestration smarter, safer, and more adaptive. Hybrid and federated designs aim to balance centralized control with decentralized flexibility, while semantic orchestration seeks to match tasks dynamically with the most capable agents. Advances in federated learning and cross-domain collaboration promise secure knowledge sharing without exposing raw data. Standardized benchmarks, simulation testbeds, and open-source orchestration frameworks will further enable transparent performance comparison and lower entry barriers. Together, these directions move multi-agent systems toward scalable, accountable, and trustworthy deployment across enterprise and societal domains.
X Conclusion
Agentic systems have evolved from single agents that perform narrow tasks, to loosely coupled multi-agent setups, and now to orchestrated collectives where coordination ensures consistency, scale, and reliability. Recent advances show that orchestrated systems are not only viable but already delivering value in real deployments, from BFSI claims processing and fraud detection to healthcare diagnostics and software engineering. Benchmarks and case studies demonstrate measurable gains in productivity, error reduction, and scalability compared with manual or single-agent approaches.
Looking forward, enterprises are moving toward dynamic ecosystems where agents can form, dissolve, and reorganize in response to tasks, much like human teams. To realize this vision, the community must invest in open protocols for interoperability, standardized benchmarks, and shared research infrastructure. With these foundations, orchestrated multi-agent systems can mature into a reliable and adaptable backbone for enterprise intelligence at scale.
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