MCP and A2A are complementary, but identity controls must catch up

At a glance

What this is: This is a comparison of MCP and A2A that shows they solve different integration layers for AI systems while creating a shared need for stronger identity and access controls.

Why it matters: IAM teams need to treat agent connections as governed access paths, because protocol interoperability only helps if authentication, authorisation, and monitoring keep pace across tools, data, and collaborating agents.

By the numbers:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.

👉 Read Descope's comparison of MCP and A2A for agentic integration

Context

MCP and A2A are both identity-adjacent protocols, but they solve different parts of the enterprise integration problem. MCP standardises how a model reaches tools and data, while A2A standardises how agents coordinate with each other. For identity teams, the key question is not which protocol wins, but how access, consent, and policy are enforced across both layers.

The governance gap is familiar: once systems can discover tools, delegate tasks, and exchange context dynamically, the control plane must be able to prove who or what is acting, what it can reach, and when that access expires. That matters for NHI, agentic AI, and the human workflows that still approve, monitor, or override them.

Key questions

Q: How should security teams govern MCP and A2A together?

A: Security teams should govern MCP and A2A as one access chain, not two separate features. MCP needs scoped tool access, while A2A needs bounded delegation and clear trust signals. The practical test is whether every hop can be authorised, logged, and reviewed without assuming that capability discovery equals permission.

Q: Why do agentic workflows increase identity risk compared with traditional integrations?

A: Agentic workflows increase identity risk because the actor can change behaviour during execution, delegate work to other agents, and expand the number of systems touched in one task. That makes least privilege harder to define up front and creates more points where scope can drift beyond the original request.

Q: What do security teams get wrong about capability discovery in agent protocols?

A: Teams often mistake capability discovery for trust. In practice, a published agent card or server description only tells you what might be done. It does not prove the requester is authorised, the action is safe, or the delegation path is bounded. Governance must sit above discovery.

Q: Who is accountable when an AI agent misuses access across MCP and A2A?

A: Accountability stays with the organisation that defined the access path, approved the credentials, and allowed delegation. If MCP and A2A are combined, incident review should trace both the technical path and the control ownership so that tool access, agent delegation, and human oversight are all attributable.

Technical breakdown

MCP and the tool access plane

MCP is a vertical integration protocol. It standardises how an LLM discovers, requests, and receives data or tool output from external systems, which reduces custom connector sprawl and gives the model a uniform way to interact with resources. The security implication is that each MCP handshake is an authorisation event, not just a technical exchange. If the resource server, authorisation server, and policy enforcement are not cleanly separated, the model can inherit access it was never meant to hold across unrelated tools.

Practical implication: treat MCP connections as governed access paths and validate token scope, consent, and server separation before exposing production data.

A2A and agent-to-agent delegation

A2A is a horizontal collaboration layer. It lets one agent discover another agent's capabilities, negotiate a task, and exchange progress updates or artifacts without needing direct human mediation for each step. Agent Cards are the trust abstraction here, because they expose capability metadata while hiding implementation details. The risk is that delegation becomes a control boundary in itself. If the initiating agent can fan out tasks without clear policy limits, you get distributed privilege expansion rather than simple coordination.

Practical implication: define which agent-to-agent requests are permitted, logged, and bounded before allowing delegation across business workflows.

Why protocol complementarity increases identity surface area

Using MCP and A2A together creates a chain: a user or agent reaches tools through MCP, then delegates work to other agents through A2A. That is efficient, but it also multiplies the number of identities, trust decisions, and policy checkpoints involved in one task. The control problem is no longer just access to a single API. It is the combined identity surface across models, tools, agents, and operators, which means least privilege has to be enforced at each handoff rather than assumed at the start.

Practical implication: map the full delegation chain and review where access is granted, propagated, and terminated across both protocols.

NHI Mgmt Group analysis

MCP and A2A are not competing standards, they are adjacent trust layers that increase identity complexity. MCP governs model-to-tool access, while A2A governs agent-to-agent collaboration, so the real issue is not protocol choice but access choreography. Once both are in play, the organisation must govern two different trust surfaces with one policy model. Practitioners should evaluate the entire access path, not the protocol in isolation.

Least privilege becomes harder to define when a task can move from one agent to another mid-flow. In a human-led workflow, the actor and the approving context are usually stable. In an agentic workflow, the actor can shift from model to model, and the useful permission set may expand as work is delegated. That makes static entitlement thinking brittle. Practitioners should stop treating agent collaboration as a simple extension of API integration.

Capability discovery is a governance decision, not just a routing feature. A2A agent cards and MCP server registration both expose what a system can do, but disclosure alone does not create trust. It simply makes the control boundary visible. The field should treat discoverability as a policy object, because ungoverned discovery is how hidden reach turns into uncontrolled execution. Practitioners should inventory what is advertised before they decide what can be invoked.

Autonomous behaviour can collapse traditional access assumptions if an agent can choose actions and timing at runtime. Identity does not select or combine tools dynamically mid-session in the way an autonomous agent can. That assumption fails when the actor can decide which tool to call, when to call it, and whether to delegate further without human approval. The implication is that review-based IAM models stop describing the behaviour they are supposed to govern.

Runtime access control for agents will increasingly become a shared discipline across IAM, PAM, and AI governance teams. MCP and A2A both push decision-making closer to execution time, which means permissions, approvals, and observability need to be coordinated across teams that historically worked in separate lanes. The organisations that keep those controls fragmented will struggle to explain who authorised what, when, and under which policy. Practitioners should align identity governance before agent deployment accelerates.

From our research:

• 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• For a broader governance lens, read OWASP Agentic Applications Top 10 for how runtime behaviour changes the control model.

What this signals

Agentic integration will outgrow static entitlement models faster than most IAM programmes can absorb. When protocol layers expand from tool access to agent collaboration, governance has to move from provisioning-time assumptions to runtime verification. That shift is especially visible when 80% of current deployments already report actions beyond intended scope, according to AI Agents: The New Attack Surface report.

The next control question is not whether organisations can connect AI systems, but whether they can explain each delegation step after the fact. For teams building policy, audit, and review processes, the useful baseline is whether every agent action can be tied back to a bounded authorisation decision and a retained audit trail.

For practitioners

• Map the full agent delegation chain Document every hop from user intent to model, tool, server, and downstream agent so you can see where identity context changes and where policy must be re-evaluated.
• Separate authentication from capability disclosure Require a distinct policy decision for what an agent may see, what it may invoke, and what it may delegate, instead of assuming published capability metadata is enough.
• Enforce scoped and short-lived access for agent workflows Issue credentials with the smallest usable scope and expire them quickly so tool access does not outlive the task, especially when the workflow crosses MCP and A2A boundaries.
• Log cross-protocol decisions in one reviewable trail Correlate MCP authorisation events, A2A delegation events, and human approvals in the same audit trail so incident response can reconstruct the complete sequence.

Key takeaways

• MCP and A2A solve different integration problems, but together they expand the identity surface that IAM teams must govern.
• The main risk is not protocol competition, it is unbounded delegation, weak authorisation boundaries, and poor auditability across tool and agent handoffs.
• Practitioners should design access, logging, and approval controls for the full agent workflow, not for each protocol in isolation.

Key terms

• Model Context Protocol: A standard way for an AI model to request context, tools, or data from external systems. In identity terms, it turns tool access into an authorisable transaction, so scope, consent, and token handling matter as much as the data returned.
• Agent-to-Agent Protocol: A communication layer that lets one AI agent discover, delegate to, and collaborate with another agent. It creates a trust boundary between autonomous components, which means policy, logging, and accountability must extend across delegation, not stop at the first agent.
• Agent Card: A metadata object that describes what an agent can do without exposing its internal implementation. It supports interoperability, but it should not be treated as proof of trust or permission, because capability advertisement and authorisation are separate governance decisions.
• Delegation Chain: The sequence of identities, approvals, and handoffs that carries a task from one actor to another. In agentic environments, the chain can cross humans, models, tools, and downstream agents, so every hop needs traceable ownership and bounded privilege.

What's in the full article

Descope's full comparison guide covers the operational detail this post intentionally leaves for the source:

• Step-by-step MCP request flow showing how the protocol handshake, authorisation request, and data provisioning sequence work in practice.
• A2A interaction mechanics, including Agent Cards, delegation flow, progress updates, and artifact generation across collaborating agents.
• Implementation guidance for combining MCP and A2A with OAuth 2.1, PKCE, and scoped credentials in production environments.
• Descope's own examples of where the protocols fit in enterprise workflows and how teams decide between single-agent and multi-agent patterns.

👉 Descope's full guide covers the protocol mechanics, security considerations, and combined deployment patterns in more detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.