Architecting Agentic Operations For Supply Chain – A Practical View Of A2A And MCP

The conversation around AI in supply chain is evolving.

We have moved beyond proofs of concept and isolated copilots. The central question is no longer whether an agent can summarize a planning report or respond to a transportation exception.

The real question is this:

Can AI systems operate across domains, under governance, and at production scale?

That is not a model question. It is an architectural one.

A layered approach built around Agent to Agent communication, A2A, and the Model Context Protocol, MCP, provides a structured way forward. Not as features. As infrastructure.

Coordination vs Capability: The Foundational Separation

At a high level, the pattern is straightforward:

• A2A provides the coordination layer

• MCP provides the capability layer

This separation is more consequential than it first appears.

Without it, agent systems collapse into distributed monoliths characterized by:

• Embedded business logic inside agents

• Hardcoded integrations

• Tight coupling between workflows

• Limited extensibility

With proper separation:

• Orchestration remains distinct

• Execution logic is encapsulated

• Capabilities are modular

• The system can evolve without structural rewrites

This is the difference between experimentation and operational architecture.

A2A: The Coordination Layer

A2A allows agents to discover and communicate with one another through standardized interfaces. Each agent publishes an Agent Card describing:

• Capabilities

• Acceptable request types

• Invocation parameters

Other agents can discover and invoke these capabilities without tight coupling.

For supply chain leaders, the implications are concrete:

• A Transportation Agent calls a Compliance Agent

• A Supplier Risk Agent coordinates with a Financial Exposure Agent

• An Order Promising Agent interacts with a Warehouse Capacity Agent

The objective is not simply inter agent messaging. It is controlled interoperability across domains without embedding vendor specific logic inside every workflow.

This is how specialization scales.

MCP: The Capability Layer

If A2A governs how agents talk, MCP governs how they act.

The Model Context Protocol standardizes how tools, structured data, and predefined prompts are exposed to agents. Rather than embedding all operational logic inside the agent itself, MCP allows capabilities to be modular and discoverable.

In a supply chain context, MCP tools might include:

• get_atp_snapshot

• quote_spot_rate

• screen_restricted_party

• check_wave_capacity

• generate_trade_documents

Adding a new compliance requirement or operational rule does not require rewriting orchestration logic.

It requires deploying a new tool.

This distinction enables:

• Extensibility instead of fragility

• Controlled evolution of capability

• Separation between business intent and operational mechanics

The Layered Architectural Pattern

This model resolves into three defined roles:

Orchestrator Agent

• Translates high level business intent into sequenced tasks

• Maintains visibility into the overall objective

Specialist Agents

• Execute domain specific responsibilities

• Encapsulate transportation, compliance, sourcing, fulfillment, or risk logic

MCP Tool Layer

• Provides granular, reusable operational capabilities

• Exposes APIs, data services, and rule checks in modular form

The separation is deliberate:

• Orchestrators own intent and sequencing

• Specialists own execution logic

• Tools remain modular and reusable

This ensures:

• Business intent remains readable

• Execution remains encapsulated

• Capabilities remain composable

A Practical Scenario

Consider a high value customer order at risk of service failure.

Business objective: Recover service without eroding margin.

The orchestrator agent decomposes the goal into:

• Assess constraints and risk

• Generate recovery options

• Validate feasibility

• Execute and monitor

Through A2A, it coordinates:

• Order Promising Agent

• Transportation Agent

• Compliance Agent

• Warehouse Agent

• Customer Communication Agent

Each specialist invokes MCP tools relevant to its domain, such as:

• Allocation rules

• Spot rate quotes

• Compliance screening

• Capacity checks

• CRM case creation

Now introduce change:

• A new emissions reporting requirement

• A new supplier expedite option

In a layered architecture, these changes require:

• Registering a new tool

• Or introducing a new specialist agent

They do not require redesigning orchestration logic.

That is structural resilience.

Architectural Advantages

A layered A2A and MCP model enables:

Dynamic Discovery

• New agents can join the ecosystem

• Orchestration logic does not require rewrites

Composable Capabilities

• Specialists assemble behavior from modular tools

• Logic is not embedded permanently inside agents

Separation of Intent and Execution

• Business goals remain governable

• Execution details are isolated and replaceable

Adaptability

• New requirements are met through composition

• Structural reengineering is minimized

For enterprises operating globally, these are prerequisites, not enhancements.

Governance Is Not Optional

As agents discover tools and access systems, governance becomes central.

Enterprise grade deployment requires:

• Strong identity and authorization controls

• Tool level access management

• Full decision logging and auditability

• Human approval gates where required

• Deterministic fallback behavior

Autonomy without control increases operational and regulatory risk.

Layered architecture enables governance. It does not replace it.

Coexistence with Deterministic Workflow Engines

This model does not eliminate traditional workflow orchestration platforms.

Those systems remain essential for:

• Reliability

• Scheduling

• Observability

• SLA enforcement

The layered model complements them:

• Workflow engines provide deterministic backbone and operational control

• A2A enables flexible coordination across agents

• MCP standardizes capability exposure

The result is adaptability without sacrificing operational discipline.

The Bottom Line

Supply chain AI will not be determined by who deploys the most capable standalone model.

It will be determined by who builds systems that:

• Coordinate effectively across domains

• Incorporate new capabilities without architectural rewrites

• Maintain control under regulatory pressure

• Avoid recreating monoliths in distributed form

A2A and MCP represent a structured attempt to provide that foundation.