Enterprise AI agents increasingly need to interact with two different kinds of things.
They need tools and data: databases, APIs, search engines, file systems and SaaS applications.
They also increasingly need other agents: research, finance, procurement, compliance and external partner agents.
Those are different interoperability problems.
MCP standardizes how an AI application connects to tools, resources and context. A2A standardizes how independent AI agents discover each other, communicate, delegate tasks and exchange results.
The simplest mental model is:
MCP
Agent ↔ Tool / Data
A2A
Agent ↔ Agent
Real enterprise architectures will often use both.
What is MCP?
Model Context Protocol is an open protocol for connecting AI applications to external context and capabilities.
An MCP server can expose tools, resources and prompts.
Conceptually:
AI Agent
│
MCP Client
│
▼
MCP Server
│
├── Tools
├── Resources
└── Prompts
MCP solves reusable agent-to-system integration.
What problem does MCP solve?
Without a standard protocol, every application builds separate adapters for Salesforce, PostgreSQL, ServiceNow, search and other enterprise systems.
MCP creates a reusable integration boundary:
Enterprise System
↓
MCP Server
↓
Any Compatible AI Client
MCP is primarily capability-oriented.
An MCP server tells the AI application what capabilities exist and how to invoke them.
What is A2A?
Agent2Agent, or A2A, is an open protocol designed for communication between independent AI agents.
A2A enables agents to discover each other, advertise capabilities, exchange messages, initiate tasks, track state and exchange artifacts.
Its fundamental architecture is:
Agent A
│
A2A
│
Agent B
Agent B can have its own reasoning, tools, memory, policies and architecture.
Agent A does not need to know how Agent B works internally.
A2A treats agents as independent systems
A simple supplier API can be represented as a tool.
A sophisticated Supplier Risk Agent that checks several sources, reasons over regulatory history, asks follow-up questions and produces a report behaves more like an autonomous service.
A2A allows the procurement agent to delegate that work without knowing the internal implementation.
MCP vs A2A at a glance
| Dimension | MCP | A2A |
|---|---|---|
| Primary relationship | Agent/application ↔ tool or context | Agent ↔ agent |
| Purpose | Capability and data integration | Agent interoperability and delegation |
| Remote system treated as | Tool/resource provider | Independent agent |
| Discovery | Tools/resources/prompts | Agent Cards and skills |
| Interaction | Capability invocation | Messages and tasks |
| Long-running work | Possible but not central | Native task-oriented model |
| Outputs | Tool/resource results | Messages and artifacts |
| Strong use case | Database/API/tool integration | Multi-agent collaboration |
| Can coexist? | Yes | Yes |
MCP and A2A are complementary abstraction layers.
Agent Cards
A2A introduces the Agent Card: metadata describing an agent's identity, endpoint, capabilities, skills, supported formats and security requirements.
An agent can inspect this card before deciding whether another agent can help with a task.
MCP tool discovery vs A2A agent discovery
MCP asks:
What tools are available?
A2A asks:
What agent is available and what can that agent accomplish?
An MCP tool describes an invocation.
An A2A skill describes a capability the remote agent can fulfill, potentially using many internal tools.
A2A tasks
A2A tasks are stateful units of work.
This supports long-running interactions where the remote agent may be working, ask for input, continue and eventually return a result.
That is richer than a single function call.
Messages and artifacts
Messages represent communication and context.
Artifacts represent deliverables such as reports, documents, images or structured results.
This distinction fits enterprise workflows where a long-running task may produce status messages before final deliverables.
Why not expose every agent as an MCP tool?
Simple agents can be wrapped as tools.
But the abstraction becomes strained when the remote capability needs multi-turn communication, task lifecycle management, asynchronous work, discovery or rich artifacts.
If the remote capability behaves like a function, MCP is natural.
If it behaves like an autonomous collaborator, A2A is natural.
Why not expose every tool as an A2A agent?
Do not turn simple operations such as get_weather, lookup_account or send_email into autonomous agents.
Tools should remain tools unless a distinct agent boundary adds real value.
MCP inside an A2A agent
A strong enterprise pattern is:
A2A
Agent A ─────────────→ Agent B
│
│ MCP
▼
Enterprise Tools
A coordinator delegates a task to a specialist agent over A2A.
The specialist agent uses MCP internally to access its tools and data.
Complete protocol stack
USER
│
▼
ORCHESTRATOR
│
A2A
┌────────┴────────┐
▼ ▼
Finance Agent Legal Agent
│ │
MCP MCP
│ │
Tools/Data Tools/Data
A2A handles agent collaboration.
MCP handles capability access.
APIs and data systems handle execution and information.
MCP and A2A do not replace agent frameworks
Agent frameworks still manage planning, memory, retries, state and internal orchestration.
MCP and A2A solve interoperability.
Security and identity
Protocols do not replace IAM.
MCP requires trusted servers, scoped tool permissions, argument validation, credential protection and audit.
A2A requires agent identity, controlled discovery, validated Agent Cards, scoped delegation, task authorization and provenance.
Interoperable does not mean authorized.
Signed Agent Cards
Cryptographically verifiable metadata can strengthen trust in agent discovery.
But a signed card only proves integrity and identity metadata. It does not prove that an agent should be trusted for a particular business action.
MCP gateways
As enterprises move from a few agents and servers to hundreds, they may need gateway layers for authentication, authorization, routing, rate limits, logging, server inventory and policy.
A2A gateways
Large agent ecosystems may similarly need governed discovery, communication policies, cross-tenant controls, artifact filtering and delegation limits.
Protocols enable communication.
Governance determines whether communication is allowed.
When to use MCP only
Use MCP when one agent mainly needs direct access to tools, databases, search, SaaS applications and APIs.
When to use A2A only
From the caller's perspective, A2A can be sufficient when another autonomous agent encapsulates its own internal implementation and tools.
When to use both
This will likely be the dominant pattern.
Example:
Claims Agent
│
├── MCP → Policy DB
├── MCP → Customer API
└── A2A → Fraud Agent
│
MCP
│
Risk Services
The Claims Agent directly uses deterministic systems through MCP and delegates specialized fraud investigation to another autonomous agent using A2A.
MCP vs A2A decision tree
What are you connecting to?
│
┌──────┴──────┐
▼ ▼
Tool/Data Agent
│ │
MCP A2A
Then ask whether the remote capability has its own reasoning and task lifecycle.
If no, use a tool.
If yes, consider an agent boundary.
Banking example
A Customer Service Agent can use MCP for customer and transaction systems and delegate deeper investigation over A2A to a Fraud Investigation Agent that itself uses MCP for fraud models, case history and risk signals.
Procurement example
A Procurement Agent can use MCP for supplier databases and A2A for an independent risk-analysis agent owned by another team or provider.
Enterprise research example
A Research Coordinator can delegate to Finance, Market and Technical research agents over A2A while each specialist uses MCP internally.
Do not use A2A just because multi-agent sounds advanced
Strong reasons for an independent agent boundary include different ownership, vendor, deployment lifecycle, security domain, expertise or long-running autonomy.
Weak reason: architecture fashion.
Distributed-systems problems still apply
Multi-agent systems inherit timeouts, retries, partial failures, version mismatch, duplicate work and state synchronization.
They also add goal drift, ambiguous delegation, authority transfer and untrusted messages.
A2A does not eliminate distributed-system engineering.
Agent discovery is a security decision
Not every Agent Card should be public.
Organizations may need public, partner-only and internal agent discovery tiers.
Agent Cards should never contain plaintext secrets.
Trusting an Agent Card is not enough
Before high-risk delegation, consider identity verification, provider trust, authorization, risk tier, contracts and audit requirements.
Preserve identity across delegation
A remote agent may need to know the originating user, source agent, task and authorization scope.
Maintain the chain:
Human
→ Agent
→ Remote Agent
→ Tool
→ Resource
Protocol compatibility is not policy compatibility
Two systems can technically communicate and still be prohibited from doing so.
Governance determines whether connectivity is allowed.
Observability across MCP and A2A
Trace:
- user,
- source agent,
- destination agent,
- task,
- MCP server,
- tool,
- arguments,
- authorization,
- artifacts,
- latency,
- cost.
Use correlation IDs across the entire distributed workflow.
Evaluate protocol behavior
For A2A measure discovery success, task completion, delegation accuracy, latency, artifact validity and cross-agent failures.
For MCP measure tool-selection accuracy, tool success, argument validity, latency and authorization failures.
For the full system measure business task completion, quality, cost, latency and risk.
Protocol choice is a KNOB
Tunable variables include:
- direct tool vs specialist agent,
- local vs remote agent,
- MCP vs A2A,
- sync vs async,
- max delegation depth,
- approved agent pool,
- approved MCP-server pool.
These affect latency, cost, reliability, security and quality.
Tool sprawl vs agent specialization
An agent seeing hundreds of tools may struggle to select the right capability.
A coordinator with a few specialist agents can reduce each model's capability surface.
But over-decomposition adds cost and failure modes.
Measure the right granularity.
Boundaries should align with ownership
A2A is especially valuable across teams, companies, clouds, vendors and trust domains.
MCP is especially natural inside one capability domain where controlled access to tools and data is required.
Production security checklist
For MCP:
- authenticate servers and callers,
- scope tools,
- validate arguments,
- isolate credentials,
- log calls,
- require approval for high-risk actions.
For A2A:
- verify agent identity,
- control discovery,
- validate metadata,
- constrain delegation,
- authorize task classes,
- preserve provenance,
- log tasks and artifacts.
For both:
- encrypted transport,
- short-lived credentials,
- inventory,
- Zero Trust,
- behavior monitoring.
Where KONTROLS fits
KONTROLS can govern which agents may collaborate, which tasks may be delegated, how far authority may travel, which MCP servers may be used, which tools may be invoked and which data may be retrieved.
Open protocols should expand interoperability, not authority.
Where KREATE fits
KREATE/KreateBots can build the agents and governed data products that sit on top of these protocol layers.
Where KNOBS fits
Protocol and orchestration choices can become measurable variables: delegation enabled, max agent hops, specialist preference, tool limits, timeouts, fallback and approval thresholds.
Evaluate their impact on quality, cost, latency and risk.
Practical adoption roadmap
- Start with direct tools and MCP.
- Identify genuine independent-agent boundaries.
- Introduce A2A where specialization or ownership justifies it.
- Establish identity and authorization.
- Add governance.
- Add distributed tracing.
- Compare one-agent and multi-agent architectures.
- Optimize.
Common mistakes
- Treating MCP and A2A as competitors.
- Exposing every API as an agent.
- Wrapping every agent as a tool.
- Assuming interoperability means trust.
- Giving agents unrestricted MCP access.
- Publishing sensitive Agent Cards.
- Losing identity across delegation.
- No cross-protocol trace.
- Building multi-agent systems without proving value.
- Designing protocols without governance.
Bottom line
Do not ask:
MCP or A2A?
Ask:
What kind of boundary am I crossing?
If the AI needs a tool, database, API or resource, use MCP.
If it needs to collaborate with an independent autonomous agent, consider A2A.
And when a remote agent itself needs tools:
A2A outside
MCP inside
Protocols provide interoperability.
KONTROLS determines what is allowed.
KNOBS determines what configuration works best.
Frequently asked questions
What is the difference between MCP and A2A?
MCP standardizes agent-to-tool/data integration, while A2A standardizes communication and task delegation between independent AI agents.
Is A2A a replacement for MCP?
No. They solve different layers and are often used together.
Can an A2A agent use MCP internally?
Yes. This is one of the most natural enterprise patterns.
KONTROLS
Open protocols should expand interoperability: not authority. Govern which agents can communicate, which MCP servers/tools they can access, how far authority can be delegated and which actions require approval.