Empowering AI Agents with MCP and A2A

Why Protocols Matter

As organizations begin experimenting with agents, a familiar challenge reappears: integration. Individual agents may be powerful, but without common protocols, connecting them often requires one-off connectors and brittle APIs that are costly to maintain.

AI agent protocols provide a common language for interaction—defining not just how agents connect to systems, but also how they communicate with one another. They specify the structure and flow of messages, the roles agents assume in conversations, and the rules for when and how they respond.

MCP and A2A tackle this integration problem directly. By standardizing how agents access resources and collaborate with each other, they reduce complexity and make it possible to design workflows that scale reliably across teams and functions.

MCP: Model Context Protocol

MCP (Model Context Protocol) is an open standard that defines how models connect with tools and data. Built on JSON-RPC, it offers a lightweight and consistent way for models to call functions, query databases, or access APIs.

In essence, MCP provides the vertical integration layer between models and resources. By standardizing these connections, it reduces the need for custom integration code and accelerates deployment.

Real-World Example: Simulation-Aware Decision Engine

One of the clearest demonstrations of MCP in action is BKOAI’s Simulation-Aware Decision Engine—a system that connects LLM-based agents to complex engineering simulations through a lightweight client–server framework.

This architecture allows agents to access weather APIs, pricing databases, and plant simulations through standardized MCP servers, linking simulation outputs directly to real-world trading and operational data. The result is a unified decision layer that bridges engineering and business intelligence.

By integrating simulations with live data streams, MCP transforms previously isolated modeling tools into accessible, data-driven systems that support real-time optimization and strategic planning across departments.

A2A: Agent-to-Agent A2A: Agent-to-Agent Protocol

If MCP provides the bridge between models and tools, A2A (Agent-to-Agent Protocol) defines how agents interact with one another. It establishes a common schema for describing an agent—its skills, provider, input and output formats, and authentication—so that agents can reliably discover, delegate, and collaborate.

In practice, A2A creates the horizontal integration layer between agents. By standardizing communication, it allows specialized agents to exchange tasks without custom-built connectors or ad hoc messaging.

Consider a refinery scenario: a scheduling agent can delegate planned maintenance to a compliance agent, which validates the tasks against safety requirements. With A2A in place, this handoff happens in a structured and auditable way, enabling a workflow that scales beyond a single agent’s capability.

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Why They Work Together

MCP and A2A are complementary.

MCP ensures agents can act on real data; A2A ensures they can coordinate with each other. Together, they enable agentic workflows that are both capable and scalable.

Picture a plant operations workflow:

• A monitoring agent (via MCP) detects equipment anomalies.
• It delegates the issue to a compliance agent (via A2A).
• The compliance agent drafts a regulatory report and routes it to supervisors.

This is no longer a demo—it is an end-to-end workflow with direct operational value

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