Two open protocols are becoming important in enterprise agent architecture:
MCP - Model Context Protocol
and
A2A - Agent2Agent Protocol
They solve related problems, but they are not competitors.
The simplest distinction is:
A useful mental model is:
A production system may use one, the other, both, or neither.
MCP vs A2A at a glance
| Dimension | MCP | A2A |
|---|---|---|
| Primary purpose | Connect agent to tools/data/capabilities | Connect agents to agents |
| Relationship | client/agent to server/tool | agent/client to remote agent |
| Main abstraction | tools and capabilities | agents, skills, messages, tasks, artifacts |
| Typical request | Search these records | Research this company and return a report |
| Best for | tool integration and data access | delegation and cross-agent collaboration |
What is MCP?
MCP provides a standardized interface between AI applications and external capabilities such as enterprise APIs, search systems, databases, files, developer tools, SaaS products, and specialized functions.
Instead of creating custom integration logic for every backend, an agent can connect through a common protocol boundary.
MCP is increasingly an integration layer that separates agent reasoning from capability implementation.
What is A2A?
A2A is designed for independent agents to discover one another, exchange messages, delegate tasks, and return results.
Consider specialized agents such as Research, Finance, Compliance, Procurement, and Customer agents.
Without a common protocol, every pair may need custom integration.
A2A creates a shared contract for cross-agent communication.
Capability invocation vs delegation
The cleanest distinction is intent.
MCP-style interaction
This is capability invocation. The agent still owns the broader task.
A2A-style interaction
This is delegation. The receiving agent may plan, call tools, inspect several systems, reason, and return an artifact.
A useful principle is:
Tool vs agent
A deterministic operation such as calculate_tax(income, filing_status, deductions) behaves naturally as a tool.
A Tax Research Agent that interprets facts, researches authority, plans work, and produces a memorandum behaves like an autonomous worker.
Do not turn every API into an agent. Do not reduce every sophisticated agent to one giant tool.
MCP architecture
A simplified pattern is:
The MCP server becomes a controlled boundary where the enterprise can govern exposed capabilities, arguments, identity, data returned, and logging.
A2A architecture
A simplified A2A pattern is:
Each remote agent can use its own model, prompts, tools, memory, policy, infrastructure, or vendor.
A2A standardizes the boundary between them.
Agent Cards
A2A agents can advertise identity, provider, skills, interfaces, and security requirements through an Agent Card.
A caller can inspect those capabilities before deciding whether the remote agent is appropriate.
Discovery
MCP discovery asks:
A2A discovery asks:
This mirrors capability discovery vs agent discovery.
Tasks in both protocols
Both protocols can support work that lasts beyond one immediate request, but the architecture differs.
The important question is not whether the work is called a task. Ask:
Enterprise example
A research supervisor may delegate via A2A to SEC Research, Financial Analysis, Competitor Research, and Risk agents.
Each specialist may then use MCP to access market data, SEC data, document search, and internal databases.
This demonstrates why the protocols are complementary.
Vertical and horizontal interoperability
A useful model is:
The agent reaches downward into tools, data, and infrastructure.
Agents communicate across functional, vendor, or organizational boundaries.
When to use MCP only
Use MCP without A2A when one agent owns the workflow and needs controlled access to several systems.
Example:
Do not create more agents unless delegation provides measurable value.
When to use A2A
A2A becomes more valuable when:
- different teams own independent agents,
- agents run on different frameworks,
- remote capabilities involve planning and reasoning,
- tasks are long-running,
- agents span organizations or vendors.
When to use both
A common pattern is:
This cleanly separates delegation from execution.
MCP vs ordinary APIs
Traditional APIs remain excellent for deterministic application integration.
| Need | Starting point |
|---|---|
| Known application operation | REST/gRPC/API |
| Agent needs reusable tool access | MCP |
| Agent delegates to another autonomous agent | A2A |
| Agent needs tools plus remote agents | MCP + A2A |
Protocols should reduce complexity, not add it.
MCP vs function calling
Function calling is generally a model/API mechanism for selecting structured operations.
MCP creates an interoperable protocol boundary around capabilities.
A model may select a tool while the runtime uses MCP to reach the implementation.
A2A vs framework-native handoffs
Framework handoffs are useful when all agents live inside one application stack.
A2A addresses the broader interoperability problem of communicating with independently implemented remote agents.
Neither protocol replaces the agent harness
MCP and A2A do not decide how the agent plans, which model it uses, what memory it retains, when it stops, which agent it delegates to, or whether a result is correct.
Those responsibilities belong to the agent harness.
Protocols provide connectivity. The harness provides behavior.
Security
Using MCP or A2A does not automatically make an integration secure.
For MCP, enterprises still need identity, authentication, authorization, tool allowlists, argument validation, least privilege, data filtering, rate limits, and audit logs.
For A2A, also consider remote-agent identity, trusted Agent Cards, delegated authority, task-level authorization, data sharing, impersonation, and malicious outputs.
Delegation is not permission transfer
A remote agent should receive only the permissions needed for the delegated task.
Use scoped authority rather than copying broad supervisor credentials.
Risk-classify MCP tools
Use progressively stronger controls:
Trust-classify A2A agents
Remote agents may be internal trusted, internal experimental, partner-managed, or external/unverified.
Apply Agent Card verification, approved-agent registries, skill allowlists, data-sharing policy, task-value limits, and output validation.
Cross-protocol observability
A business task may span:
Use a shared correlation ID so the complete business trajectory is traceable.
Measure architecture by outcome
Do not measure success by number of MCP servers or connected agents.
Measure task success, tool correctness, delegation quality, reliability, latency, cost, security, recovery, and portability.
Common mistakes
Treating MCP and A2A as competitors.
Turning every capability into an agent.
Representing complex agents only as tools.
Building multi-agent systems unnecessarily.
Assuming protocols provide governance.
Passing too much context between agents.
Hiding authoritative business state inside protocol messages.
Enterprise reference architecture
A scalable pattern is:
with cross-cutting:
How to choose
Is the remote capability deterministic and narrowly callable? Use an API or MCP.
Does the remote capability independently plan and execute a goal? Consider A2A.
Are all agents inside one runtime? Framework-native orchestration may be enough.
Do agents span frameworks, teams, vendors, or companies? A2A becomes more valuable.
Does each agent need enterprise tools? Use MCP inside those agents.
Practical rollout
Phase 1
Standardize a few high-value tools with MCP.
Phase 2
Build one reliable agent and validate permissions, tracing, and evaluation.
Phase 3
Identify genuine specialist boundaries.
Phase 4
Introduce A2A only where agent-to-agent interoperability adds value.
Phase 5
Add cross-agent observability.
Phase 6
Evaluate quality, latency, reliability, and cost.
MCP and A2A as architectural knobs
Compare:
Architecture A: one agent plus six MCP tools.
Architecture B: supervisor plus three A2A specialists, each with MCP tools.
Measure task success, latency, token cost, tool errors, delegation errors, human escalation, and operational complexity.
Do not assume the more agentic topology is better.
KREATE, KONTROLS, and KNOBS
KREATE
Build agents, data products, workflows, MCP integrations, and multi-agent applications.
KONTROLS
Govern which tools are available, which agents may communicate, action limits, permissions, approvals, and data policy.
KNOBS
Expose tool set, remote-agent choice, delegation depth, max hops, model tier, timeout, retry policy, and human-review threshold as measurable variables.
Bottom line
MCP and A2A solve different parts of the interoperability problem.
MCP connects agents to capabilities.
A2A connects autonomous agents to other autonomous agents.
Use MCP for tools, data, and reusable enterprise capabilities. Use A2A for discovering and delegating to independent agents. Use both when a multi-agent system needs both forms of interoperability.