A2A
Horizontal plane
Agent2Agent Protocol
Agent ↔ agent collaboration
- Core unit
- Task
- Discovery
- Agent Card
“Ask another agent to do something.”
Protocol comparison · Prepared June 5, 2026. Sources checked against official A2A, MCP, and OpenWOP documentation.
A2A vs MCP vs OpenWOP
A specification-level comparison of three complementary agentic AI protocols: inter-agent collaboration, durable workflow orchestration, and tool/context integration.
MCP
Vertical plane
Model Context Protocol
Agent ↕ tools, data & context
- Core unit
- Tool / resource / prompt call
- Discovery
- tools/list · resources/list
“Give the model safe access to external capabilities.”
OpenWOP
Substrate
Open Workflow Orchestration Protocol
Durable workflow control
- Core unit
- Run
- Discovery
- Host capability document
“Run and observe a durable workflow.”
Agent Ainternals opaque
A2A · tasks & messages
Agent Binternals opaque
MCP
MCP
Tools · data · prompts
Tools · data · prompts
OpenWOP
durable runs · event log · human checkpoints · replay · signed webhooks
Executive Summary
A2A, or Agent2Agent, is an open standard for communication and interoperability between independent, potentially opaque AI agent systems. The official A2A specification says its primary goals include capability discovery, modality negotiation, collaborative task management, and secure information exchange without requiring agents to expose internal state, memory, or tools.
MCP, or Model Context Protocol, standardizes how AI applications connect to external tools, resources, prompts, and contextual systems. Its official architecture documentation defines an MCP Host, MCP Client, and MCP Server model, with a JSON-RPC data layer and transports such as stdio and Streamable HTTP.
OpenWOP, or Open Workflow Orchestration Protocol, is an open, vendor-neutral wire protocol for durable multi-agent workflow orchestration. Its documentation describes a system where clients start workflow runs, stream run events, pause for human checkpoints, replay history, use signed webhooks, and preserve portability across compliant hosts.
Most important architectural takeaway: these are not direct substitutes. A2A is the horizontal agent-to-agent layer. MCP is the vertical agent-to-tool/context layer. OpenWOP is the durable workflow-control layer that can orchestrate agents, tools, humans, events, and replayable state.
| Question | A2A | MCP | OpenWOP |
|---|---|---|---|
| Primary purpose | Agent-to-agent collaboration | Connect AI apps to tools, data, prompts, and context | Durable multi-agent workflow orchestration |
| Core unit | Task | Tool/resource/prompt interaction over an MCP session | Run |
| Main discovery object | Agent Card | Initialization capabilities plus primitive lists such as tools/list, resources/list, and prompts/list | Host capability document and workflow catalog |
| Best mental model | “Ask another agent to do something.” | “Give the model/app safe, structured access to external capabilities.” | “Run and observe a durable workflow.” |
| Primary boundary | Between independent agents | Between AI host application and context/tool servers | Between workflow client and workflow host |
| Internal execution visibility | Intentionally opaque | Visible at tool/resource/prompt-call level, not full workflow orchestration | First-class: events, traces, replay, artifacts, checkpoints |
Protocol Purpose and Scope
A2A: inter-agent collaboration
A2A solves the problem of independent agents discovering each other, exchanging messages, delegating work, and reporting task progress across framework, vendor, and language boundaries.
Its target users are agent platforms, agent marketplaces, enterprise automation systems, and specialist agents that need to collaborate without exposing internals.
MCP: tool and context integration
MCP solves the problem of connecting AI applications to external context providers and action surfaces through a shared protocol for tools, resources, prompts, notifications, sampling, elicitation, and logging.
Its target users are AI app developers, tool providers, IDEs, data platforms, SaaS integrations, local automations, and agent frameworks that need a standardized integration surface.
OpenWOP: durable workflow control
OpenWOP solves the problem of portable AI workflow execution: how to start, stream, interrupt, resume, replay, observe, and validate long-running multi-agent workflows.
Its target users are workflow authors, host implementers, pack authors, debuggers, evaluators, and enterprise platforms that need durable orchestration semantics.
Assumptions about the surrounding ecosystem
| Protocol | Assumptions |
|---|---|
| A2A | Multiple autonomous or semi-autonomous agents exist behind endpoints; callers should not need to know the remote agent's memory, plans, tools, or implementation. |
| MCP | AI applications need access to changing external context and actions; tool/resource providers should expose capabilities through a common client-server protocol rather than bespoke integrations. |
| OpenWOP | AI applications increasingly need durable, replayable, observable, multi-step workflows; workflow hosts should expose a common wire contract even if runtimes differ. |
Architectural Models
A2A architecture
A2A uses a remote-agent model. An A2A client discovers an A2A server through an Agent Card, sends a message, receives either a direct message or a task, and then follows task status, artifacts, streaming events, or push notifications.
CallerA2A Client / Caller
Send Message
Remote agentA2A Server / Remote Agent
Creates or updates Task
OutcomeTask lifecycle
- status updates
- artifacts
- messages
MCP architecture
MCP uses a host-client-server model. The host is the AI application. The MCP client is the connection manager inside that host. The MCP server exposes context or capabilities such as tools, resources, and prompts. The MCP data layer uses JSON-RPC, while the transport layer supports stdio for local process communication and Streamable HTTP for remote server communication.
HostMCP HostAI application · IDE · agent runtime
one or more MCP Client connections
ServerMCP Server(s)
expose tools, resources, prompts, notifications
BackendsExternal systems
- APIs
- databases
- filesystems
- SaaS apps
- services
OpenWOP architecture
OpenWOP uses a workflow-host model. A client starts a run against a compliant host. The host executes a declarative workflow, emits events, may suspend for human input or approval, streams state over SSE, sends signed webhooks, and can support replay or fork behavior.
CallerClient / SDK / Agent
POST /v1/runs
HostOpenWOP Host
Executes WorkflowDefinition
RunRun → Event Log
- SSE
- Webhooks
- OpenTelemetry
Interrupt / pause / resume / replay
ReachesExecution surface
- Humans
- tools
- agents
- workers
- subflows
- artifacts
| Architecture concern | A2A | MCP | OpenWOP |
|---|---|---|---|
| Core entities | Client, server/remote agent, Agent Card, Message, Task, Artifact, Part | Host, client, server, tools, resources, prompts, notifications, sampling, elicitation | Client, host, workflow, run, event log, node, artifact, interrupt, pack |
| Communication pattern | Message/task exchange with streaming and push options | JSON-RPC over stdio or Streamable HTTP | REST run control, SSE event streams, signed webhooks, optional gRPC profile |
| Discovery | Well-known Agent Card | Initialization capability negotiation plus primitive list calls | Well-known host capability document plus workflow catalog |
| Lifecycle | Task state machine | Session initialization, capability negotiation, primitive discovery, calls, notifications | Run lifecycle, event log, pause/resume, replay/fork |
| State management | Remote agent owns task state; execution internals are opaque | Stateful connection/session; server exposes current tools/resources/prompts dynamically | Host owns durable run state and exposes events/snapshots |
| Trust boundary | Between independent agents | Between host application and MCP servers, including local or remote tool providers | Between workflow client and execution host; also between workflow nodes/tools/humans |
Specification Comparison
| Dimension | A2A | MCP | OpenWOP | Architectural implication |
|---|---|---|---|---|
| Agent/service discovery | Agent Card advertises identity, skills, endpoints, security, capabilities, input/output modes. | Capability negotiation at initialization; primitive discovery through list methods such as tools/list. | Capability document advertises protocol version, transports, limits, profiles, envelopes, node support, and host behavior. | A2A discovers agents; MCP discovers capabilities exposed by context/tool servers; OpenWOP discovers workflow hosts. |
| Identity and authentication | Agent identity and security schemes appear in the Agent Card; supports standard web-security patterns. | Transport-specific authentication, especially for Streamable HTTP; stdio often relies on local process trust. | Host/caller auth with operation scopes and production profiles; workflow execution identity may travel through run metadata and agent surfaces. | A2A has the most explicit remote-agent identity layer; MCP auth depends heavily on deployment mode; OpenWOP focuses on run-operation authorization. |
| Authorization and permissions | Agent-defined authorization; callers must respect security schemes and scopes exposed by the agent. | Server/tool permissions generally mediated by the host/client and transport authentication; tool authorization is often provider-specific. | Standardized operation scope vocabulary such as run creation, read, approval response, artifact read. | OpenWOP is strongest where workflow operation-level authorization matters. |
| Message format | Canonical protocol model with JSON representations and bindings including JSON-RPC, gRPC, HTTP+JSON. | JSON-RPC 2.0 request/response/notification messages. | HTTP JSON schemas, events, envelopes, workflow definitions, artifacts, and run snapshots. | MCP is the simplest RPC substrate; A2A and OpenWOP carry richer task/workflow semantics. |
| Transport layer | JSON-RPC, gRPC, HTTP+JSON, streaming, push notifications. | Stdio and Streamable HTTP with optional SSE behavior. | REST, SSE, signed webhooks, gRPC profile. | MCP covers local and remote tool servers; A2A and OpenWOP are more naturally network service protocols. |
| Task delegation | Core purpose: delegate to another agent as a task. | Not task delegation; it is tool/context invocation by a host application. | Delegation happens inside workflow runs through nodes, workers, agents, subflows, or tools. | Use A2A for peer delegation; use MCP for capability invocation; use OpenWOP for orchestrated delegation. |
| Tool invocation | Possible inside the remote agent but not standardized as the primary abstraction. | Central abstraction through tools/list and tools/call. | Can model tool calls as workflow nodes and can compose with MCP tool calls. | MCP is the most direct tool integration protocol. |
| Human-in-the-loop | Task states such as input-required and auth-required can request user participation. | Elicitation lets servers request additional user information through the client/host. | First-class interrupts, approvals, clarifications, external events, resume schemas, and run suspension. | OpenWOP is strongest for auditable approval gates; MCP elicitation is narrower and tool-session-oriented. |
| Error handling | Binding-native errors plus protocol-specific task states and error semantics. | JSON-RPC error responses plus tool-call result semantics. | Run errors, lifecycle errors, capability-gated refusals, terminal events, and debug artifacts. | OpenWOP is strongest for workflow-runtime failures; MCP is strongest for RPC/tool-call errors. |
| Observability | Task status, streaming, push notifications, enterprise tracing guidance. | Logging primitive and host/server observability, but not full workflow tracing by itself. | OpenTelemetry naming, W3C trace context, run events, cost attribution, event logs, debug bundles. | OpenWOP has the most explicit production observability model. |
| Auditability | Caller can audit task-level outputs and status, not necessarily internal steps. | Tool calls can be logged, but audit semantics are largely host/platform responsibilities. | Event log, replay, artifacts, trace IDs, and signed events support audit-heavy systems. | OpenWOP is best for regulated workflow audit trails. |
| Extensibility | Agent Card extensions and protocol extension mechanisms. | Primitive capabilities, server feature extensions, dynamic listChanged notifications. | Host extensions, profiles, node packs, workflow-chain packs, pack registry. | All three are extensible, but in different layers. |
| Versioning | A2A-Version header/parameter and semantic compatibility expectations. | Specification versions plus initialization capability negotiation. | Versioned endpoint surface and compatibility/conformance profiles. | A2A and OpenWOP are more explicit at the public wire-contract level; MCP negotiates capabilities per session. |
| Deployment model | Remote agent service. | Local process or remote server connected to an AI host. | Durable workflow host. | MCP can be embedded/local; OpenWOP usually requires a durable host; A2A requires addressable agents. |
Similarities
All three protocols attempt to reduce bespoke integration work in agentic AI systems. They each provide a standardized contract around a boundary that would otherwise become custom glue code.
| Shared pattern | A2A | MCP | OpenWOP |
|---|---|---|---|
| Capability declaration | Agent Card | Initialization capabilities and primitive lists | Host capabilities and workflow metadata |
| Asynchronous or long-running work support | Tasks, streaming, push notifications | Progress notifications and long-running tool operations, depending on server/client support | Runs, event logs, SSE, webhooks, pause/resume |
| Structured artifacts/results | Artifacts and Parts | Tool/resource content arrays and structured JSON-RPC responses | Artifacts, events, envelopes |
| Human involvement | Input-required/auth-required task states | Elicitation from server to user via host | Interrupts and approval/clarification gates |
| Security boundary awareness | Agent trust boundary | Host/server/tool trust boundary | Run/host/tool/human trust boundaries |
| Extensibility | Extensions and metadata | Capabilities, primitives, notifications, server-specific tools/resources/prompts | Profiles, packs, extensions |
Differences
Horizontal vs vertical vs orchestration layer
| Layer | Protocol | What it standardizes |
|---|---|---|
| Horizontal agent layer | A2A | How independent agents discover, message, delegate, and return results to each other. |
| Vertical tool/context layer | MCP | How an AI host connects to external tools, data sources, prompts, and contextual capabilities. |
| Workflow-control layer | OpenWOP | How durable multi-step workflows are started, streamed, interrupted, resumed, replayed, observed, and validated. |
Different answers to “what is opaque?”
A2A intentionally treats the remote agent as an opaque collaborator. The caller can see declared skills, task states, and artifacts, but not the remote agent’s private planning, memory, tool chain, or execution graph.
MCP is neither an agent transparency protocol nor a workflow protocol. It exposes capabilities from servers to hosts. A host can see and invoke tools/resources/prompts, but MCP does not define an end-to-end workflow graph or independent agent collaboration model.
OpenWOP removes that opacity inside a workflow host. It standardizes run state, event streams, artifacts, interrupts, replay, node packs, and observability so the workflow can be inspected and validated.
Different units of composition
| Protocol | Composition unit | Typical example |
|---|---|---|
| A2A | Skill advertised by an agent and invoked as a task | “Ask the procurement agent to compare vendors.” |
| MCP | Tool, resource, prompt, sampling, elicitation, logging | “Query CRM, read the contract, call the pricing API, use a prompt template.” |
| OpenWOP | Workflow definition, node, pack, run, interrupt, artifact | “Run a vendor-evaluation workflow, pause for approval, replay if policy changes.” |
Strengths and Weaknesses
| Protocol | Strengths | Weaknesses / open questions |
|---|---|---|
| A2A | Strong abstraction for independent agent collaboration. Agent Card creates a clear discovery and capability surface. Supports long-running and human-in-the-loop task interaction without exposing internals. Good fit for cross-vendor agent ecosystems and skill marketplaces. | Not designed to standardize internal workflow graphs, replay, or node-level traces. Tool invocation is hidden behind agent behavior unless paired with MCP or another tool protocol. Task states are intentionally coarse compared with a workflow engine. |
| MCP | Best protocol for connecting models/apps to tools, data, prompts, and APIs. Simple JSON-RPC interaction model with dynamic capability discovery. Works locally via stdio and remotely via Streamable HTTP. Widely adopted mental model: “USB-C for AI tool/context integration.” | Does not define agent-to-agent task delegation. Does not define durable workflow orchestration, replay, or full run audit semantics. Security posture depends heavily on host trust, server trust, local process isolation, tool metadata validation, and transport authorization. |
| OpenWOP | Strongest durable execution, replay, event-log, and observability model. Rich human checkpoint and interrupt semantics. Conformance-oriented approach for workflow-host portability. Composes naturally with both A2A and MCP. | Heavier implementation burden than A2A or MCP. Not a replacement for an agent-to-agent messaging protocol. Not a replacement for MCP’s direct tool/context integration surface. Requires durable host infrastructure to realize its full value. |
Use Case Fit
| Scenario | Best fit | Why |
|---|---|---|
| Agent-to-agent collaboration | A2A | Designed specifically for independent agents exchanging messages and tasks. |
| Tool/API/data-source integration | MCP | Tools, resources, and prompts are MCP’s core primitives. |
| Durable multi-step AI workflow | OpenWOP | Run lifecycle, event log, interrupts, replay, and observability are central. |
| Human approval flows | OpenWOP | First-class interrupts and resume semantics beat coarse task states or tool elicitation. |
| Simple remote specialist agent invocation | A2A | Lower-level workflow details do not need to be exposed. |
| Local filesystem/database/SaaS tool access from an AI app | MCP | MCP servers expose these as tools/resources over stdio or HTTP. |
| Workflow replay, fork, time-travel debug | OpenWOP | A2A and MCP do not define this as a core lifecycle. |
| Agent marketplace or skill catalog | A2A | Agent Cards and skills are the natural marketplace metadata unit. |
| Enterprise agent platform | All three | A2A for external agents, MCP for tools/context, OpenWOP for durable orchestration. |
| Secure tool execution inside audited workflow | MCP+OpenWOP | MCP invokes the tool; OpenWOP governs run-level policy, event log, human approval, and traceability. |
| Cross-vendor interoperability | Layer-specific | A2A for agents, MCP for tools/context servers, OpenWOP for workflow hosts. |
Integration Possibilities
The strongest architecture treats the three protocols as composable layers rather than alternatives.
External agent ecosystem
A2A · discover agents, delegate tasks, exchange artifacts
A2A-facing agent or gateway
OpenWOP · start durable run, stream events, pause/resume, replay
Workflow host / orchestration plane
MCP · call tools, read resources, use prompts, elicit input
External systems
- APIs
- databases
- files
- SaaS apps
- local services
Pattern 1: OpenWOP host exposed as an A2A agent
An OpenWOP host can publish an A2A Agent Card. Each workflow can be advertised as an A2A skill. An incoming A2A message creates an OpenWOP run. Run events are projected into A2A task status updates. Run artifacts become A2A artifacts.
| A2A concept | OpenWOP projection |
|---|---|
| Agent Card | Host metadata plus workflow catalog |
| AgentSkill | WorkflowDefinition |
| Message | Run input |
| Task | Run |
| Task status update | Run event or run snapshot |
| Artifact | Workflow artifact |
| Input required | OpenWOP interrupt, approval, or clarification |
Pattern 2: OpenWOP workflow calls MCP tools
Inside a workflow run, worker nodes can call MCP servers for tool execution or resource retrieval. This keeps MCP at the tool/context boundary while OpenWOP governs run-level durability, state, observability, retry behavior, and approvals.
| OpenWOP workflow need | MCP role |
|---|---|
| Read external system state | Expose resource or tool call |
| Execute a business operation | Expose action as an MCP tool |
| Use a reusable instruction template | Expose prompt primitive |
| Ask user for missing tool input | Use MCP elicitation, optionally wrapped in OpenWOP interrupt |
Pattern 3: A2A agent backed by MCP and OpenWOP
A specialist agent can expose a simple A2A interface while internally using MCP to call actual systems and OpenWOP to coordinate the work. This is likely the cleanest enterprise pattern because it separates the public contract from internal execution and integration details.
A2Acaller
A2Aspecialist agent
OpenWOPworkflow run
MCPCRM server
MCPdatabase server
MCPfilesystem or document server
OpenWOPapproval interrupt
A2Aartifact result
Adapter responsibilities
| Adapter concern | Mapping / design choice |
|---|---|
| Discovery | Expose OpenWOP workflows as A2A skills; expose MCP server primitives to workflow node catalogs. |
| Invocation | Map A2A SendMessage to OpenWOP POST /v1/runs; map OpenWOP tool nodes to MCP tools/call. |
| Identity | Propagate caller identity from A2A through OpenWOP run metadata and into MCP tool authorization where appropriate. |
| State | Project OpenWOP run states into A2A task states; preserve extra detail in metadata. |
| Human-in-the-loop | Map OpenWOP interrupts to A2A input-required states and/or MCP elicitation depending on where the user interaction belongs. |
| Observability | Preserve trace IDs across A2A, MCP, and OpenWOP calls. |
| Security | Do not let MCP tool metadata implicitly escalate privileges; enforce host/workflow policy before tool invocation. |
Final Recommendations
Choose A2A when...
- You need independent agents to discover and call each other.
- You want an agent or skill marketplace.
- The caller should not inspect internal tools, workflows, memory, or reasoning.
- The core problem is remote task delegation and artifact return.
Choose MCP when...
- You need to connect an AI app or agent runtime to external tools, data, prompts, APIs, files, or SaaS systems.
- You want dynamic discovery of tools/resources/prompts.
- You need local process integrations via stdio or remote integrations via Streamable HTTP.
- Your main challenge is context/tool integration rather than agent collaboration or durable workflow control.
Choose OpenWOP when...
- You need durable, replayable, observable AI workflows.
- You need human approvals or clarifications as first-class workflow checkpoints.
- You need event logs, run snapshots, fork/replay, traces, and production debugging.
- You are implementing or evaluating a workflow host, not merely an agent endpoint or tool server.
Use all three when...
Use all three for an enterprise-grade agentic platform:
A2Apublic agent collaboration boundary
MCPtool, data, prompt, and context integration boundary
OpenWOPinternal durable workflow and observability boundary
Decision checklist
| Question | Likely answer |
|---|---|
| Do we need one agent to call another independent agent? | A2A |
| Do we need the agent to call external tools or read external data? | MCP |
| Do we need durable, replayable, observable workflow execution? | OpenWOP |
| Do we need human approval gates with deterministic resume? | OpenWOP |
| Do we need a simple integration between an AI app and a database/API/filesystem? | MCP |
| Do we want remote agents to remain implementation-opaque? | A2A |
| Do we want to expose workflows as agent skills? | A2A+OpenWOP |
| Do we want audited tool execution inside durable workflows? | MCP+OpenWOP |
| Do we want a complete enterprise agent platform? | A2A+MCP+OpenWOP |
Bottom line: A2A, MCP, and OpenWOP are best viewed as a layered protocol stack. A2A lets agents collaborate. MCP lets agents and AI applications use tools and context. OpenWOP makes multi-step agentic work durable, observable, interruptible, replayable, and portable.
Sources
The analysis distinguishes specification-grounded claims from architectural interpretation. These are the primary source documents consulted.
- Agent2Agent (A2A) Protocol Specification — official A2A v1.0.0 specification.
- Model Context Protocol Architecture Overview — official MCP architecture, primitives, transports, and lifecycle overview.
- Model Context Protocol GitHub repository — official MCP specification and schema repository.
- OpenWOP — Workflow Orchestration Protocol — OpenWOP overview and specification landing page.
- OpenWOP Spec v1 — A2A Integration — OpenWOP’s documented A2A composition and state-projection guidance.