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AI agents negotiate through a stack, not through a single magic protocol. The Agent2Agent (A2A) protocol lets agents discover one another, exchange task messages, and collaborate across vendors or frameworks. The Model Context Protocol (MCP) solves a different problem: it gives an agent a structured way to discover and invoke tools, data sources, and services. Identity, authorization, delegation, policy enforcement, logging, and human approval determine whether any requested action is permitted and accountable.
A safe design connects every action to the human or organization that delegated authority, gives each agent only the scopes it needs, carries that authorization context through every hop, and pauses for explicit approval when an action is consequential, destructive, privacy-sensitive, financial, or externally binding.
What “agent negotiation” actually means
When two AI agents negotiate, they are not exchanging unrestricted model thoughts. They are exchanging structured messages about a task: what is being requested, what information is available, which capabilities can be used, what constraints apply, and whether the task is complete. One agent may ask another to find records, compare options, perform a calculation, or carry out a transaction. The receiving agent evaluates the request against its own identity, policy, capabilities, and delegated authority.
The protocol carries communication; it does not by itself grant permission. A2A interoperability can make a request understandable to a remote agent, but the remote service still decides whether the caller is authenticated, whether the requested operation is authorized, and whether a human must approve it. This separation is essential because a well-formed request can still be malicious, outside scope, expired, or based on untrusted data.
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A2A and MCP: complementary, not competing
A2A and MCP sit at different edges of an agent system. Treating them as interchangeable creates security and architecture mistakes.
| Question | A2A | MCP |
|---|---|---|
| Primary purpose | Agent-to-agent discovery, task exchange, and collaboration | Agent-to-tool, data-source, and service discovery and invocation |
| Typical peer | Another autonomous agent operated by the same or a different vendor | A tool server, database connector, API, file system, or other service |
| What it standardizes | How agents advertise capabilities and communicate about tasks | How an agent discovers available tools and structured data or service operations |
| Does it decide authorization? | No. The receiving agent and its policy layer make that decision. | No. The tool server and surrounding authorization controls decide whether an invocation is allowed. |
| Human approval semantics | Defines an AUTH_REQUIRED task state that can trigger approval, correction, rejection, or escalation |
Provides tool access; approval behavior must be supplied by the host application and policy system |
| Best mental model | A communication and collaboration protocol between agents | A controlled interface from an agent to tools, data, and services |
NIST’s identity and authorization work describes MCP as a protocol for AI models and agentic systems to discover, access, and interact with external tools, data sources, and services in a consistent, structured way. NIST treats this tool-access layer and agent-to-agent interoperability as complementary. A production workflow may therefore use A2A for a planning agent to request work from a specialist agent, and MCP for that specialist to query a database or call a business API.
The principal–agent–sub-agent–tool chain
Permission design becomes clearer when the actors are named. The principal is the human or organization that grants authority. The delegating agent receives a bounded mandate. A sub-agent may perform a part of the task, while a tool or service executes the final operation. Each hop must preserve who authorized the work and what limits apply.
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- Principal: a person signs in or an organization establishes a service identity and approves a purpose.
- Agent runtime: the system binds that principal to a particular agent identity, deployment, and session.
- Delegation: the runtime issues narrowly scoped, time-limited rights, such as reading a project calendar but not deleting events.
- Sub-agent request: an A2A message carries the task, the relevant constraints, and evidence of the delegated authority.
- Policy check: the receiving agent verifies the caller, evaluates its own policy, and attenuates the scope if it delegates further.
- Tool invocation: an MCP operation presents the current identity and scope to the tool server, which makes a fresh authorization decision.
- Outcome and evidence: results, denials, approvals, data lineage, and revocations are recorded against the same task and principal.
A protocol message should never be treated as proof that the original user authorized every downstream action. Each service boundary is an enforcement point, not a pass-through.
Authentication, authorization, delegation, and consent are different
Authentication answers “who is calling?”
Authentication verifies an agent, user, workload, or organization. It may use credentials associated with an agent runtime, OAuth 2.0, OpenID Connect, certificates, or another identity mechanism. Successful authentication only establishes a claimed identity; it does not establish what that identity may do.
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Authorization answers “is this operation allowed?”
Authorization evaluates the requested operation, resource, context, and policy. A correctly authenticated agent can still be denied because it lacks a required scope, the resource belongs to another tenant, the request violates a data policy, or the credential has expired or been revoked.
Delegation answers “whose authority is being exercised?”
Delegation links the principal’s authority to an agent and defines its limits. A useful record identifies the principal, the delegating agent, the delegated recipient, purpose, resources, operations, expiry, and any conditions such as geography, transaction amount, or approval requirement.
Consent is a human decision, not a model claim
Consent is the principal’s informed approval for a stated use. A model saying “the user wants this” is not evidence of consent. The approval event should be captured by the application or identity system, with the exact action and scope shown to the person.
How authority survives multiple agent hops
Multi-agent systems are difficult because a request can cross organizational and technical boundaries. NIST’s comment summary calls delegation “the foundational enabler of agent utility in the enterprise” and emphasizes that authorization context must survive service boundaries and delegation chains.
Use an explicit delegation record or token that can be validated at every hop. The downstream agent should be able to determine:
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- which human or institution is the ultimate principal;
- which intermediate agents handled the task;
- which scopes were granted at each step and whether they were narrowed;
- which policy decisions permitted or denied the operation;
- when the authority expires or was revoked; and
- which data and tool results influenced the action.
Scopes should be attenuated, never expanded, as work moves downstream. For example, an account-management agent allowed to “prepare a refund” could delegate only “read order 1842 and calculate refund options” to a sub-agent. It should not pass a general “manage refunds” credential. Short-lived credentials reduce the damage from theft or replay; revocation stops a still-valid-looking delegation when a user withdraws authority or a runtime is compromised.
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Design permissions that an agent cannot quietly broaden
Write permissions as concrete operations on named resources. “Access customer data” is too broad; “read shipping address for order 1842 until 16:00 UTC” is testable. Separate read, propose, approve, and execute rights so that a planning agent cannot silently perform the action it recommends.
- Least privilege: grant only the resources and operations required for the current task.
- Purpose binding: record why access was granted and reject reuse for an unrelated task.
- Time limits: use short expirations for interactive work and renew only after a new policy decision.
- Audience restriction: bind a credential to the intended service, tenant, and agent runtime.
- Amount and rate limits: cap financial value, record volume, API calls, or destructive operations.
- Separation of duties: require a different identity or human for approval of high-impact actions.
- Revocation: provide a way to invalidate credentials and queued tasks, not merely future logins.
Enforce these rules outside the model’s natural-language reasoning. A prompt that says “you may now approve the payment” must not change policy. The authorization service, tool gateway, or transaction system must make the binding decision.
Where human oversight belongs
Oversight should follow risk, reversibility, and external impact rather than a blanket rule that either stops everything or permits everything. Low-risk work can run automatically inside pre-approved bounds. Irreversible or rights-affecting work should stop at a named control point.
| Risk class | Examples | Recommended control |
|---|---|---|
| Low and reversible | Formatting a document, summarizing a private file, checking calendar availability | Pre-authorized scope, automatic execution, routine logging |
| Moderate | Sending an internal draft, changing a noncritical workflow, updating a project record | Policy check, limited scope, visible activity record, optional review |
| High or externally binding | Issuing a payment, deleting records, changing access rights, sending a legal or public statement | Pause for explicit human approval or a separately authorized control agent |
| Prohibited or uncertain | Action outside the principal’s scope, unresolved identity, suspected prompt injection, expired authority | Deny, preserve evidence, and escalate for investigation |
The AUTH_REQUIRED control point
The A2A specification defines an AUTH_REQUIRED task state. Instead of pretending to complete the task, an agent returns that state with the missing authorization context or approval requirement. The client can then negotiate, correct the request, reject it, fulfill it, or contact a human, another agent, or a service.
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- The worker agent identifies that the requested operation exceeds its current scope or meets a high-risk rule.
- It records the exact operation, resource, effect, and reason approval is required.
- The task enters
AUTH_REQUIREDand remains blocked from execution. - The client presents the details to an authorized human or approval service.
- The approver grants a narrowly defined, possibly one-time scope, or rejects the request.
- The decision, identity, timestamp, scope, and resulting action are logged and attached to the task.
Approval should be specific enough that a person understands what will happen. “Allow agent to continue” is weaker than “Allow agent billing-worker-7 to issue a refund of £48.20 for order 1842 once, before 17:00 UTC.”
Prompt injection and untrusted tool output
An agent may receive instructions embedded in a web page, document, email, database field, or tool response. Those instructions are data, not automatically policy. A malicious page could tell an agent to reveal credentials, ignore a spending limit, or call a different tool. If the model treats that text as an authorization decision, the attacker has bypassed the control plane.
- Mark external content and tool output as untrusted in the agent’s context.
- Keep credentials and policy decisions outside prompts where possible.
- Use allowlists for tools, resources, destinations, and data classes.
- Require a fresh policy evaluation before every consequential invocation.
- Detect instruction conflicts and route them to a human or security service.
- Log the untrusted input that preceded a blocked or approved action.
Model reasoning can propose an action; a separate enforcement component must decide whether it is permitted.
Audit, provenance, and observability
Observability is part of security, not merely a debugging convenience. A useful audit trail lets an investigator reconstruct the chain from principal to final effect without trusting an agent’s summary.
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- agent, user, organization, tenant, and runtime identities;
- task identifiers and A2A messages, including state changes;
- MCP tool names, arguments, resources, and returned classifications;
- delegation scopes, policy versions, approval evidence, expiry, and revocation events;
- data lineage showing which records or outputs influenced the decision;
- timestamps, outcomes, errors, and the identity of the enforcement point; and
- tamper-evident storage and retention appropriate to the data and regulation.
Provenance should be carried forward, not reconstructed after the fact. If an agent creates a report from a sub-agent’s result, the report should remain linkable to the original principal, intermediate identities, source records, and tool calls.
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Implementation path for a permission-safe multi-agent system
- Map the business action. List the principal, resources, tools, agents, side effects, reversibility, and regulatory obligations before choosing a protocol.
- Establish identities. Give every agent and runtime a verifiable identity. Bind the signed-in user or organization to the published agent and verify that the user is allowed to access it, as shown in Microsoft’s OAuth 2.0 authorization-code implementation guidance.
- Define scopes and policy. Express operations, resources, limits, expiry, and approval requirements in machine-enforced policy rather than prose.
- Choose the protocol boundary. Use A2A for agent collaboration and MCP for controlled tool, data, and service access. Do not use either protocol as a substitute for authorization.
- Propagate delegation context. Carry the principal, chain of agents, scopes, and policy evidence through every service boundary. Attenuate rights at each delegation.
- Add enforcement points. Check identity and policy before an A2A task is accepted, before an MCP call is executed, and again before an external side effect.
- Implement approval states. Route high-impact operations to
AUTH_REQUIREDor an equivalent workflow with clear approve, reject, correct, and escalate outcomes. - Instrument and test. Verify logs, provenance, revocation, replay resistance, expired credentials, cross-tenant isolation, prompt injection handling, and recovery from a denied or partially completed task.
Common failure modes and fixes
“The agent authenticated, so it can act”
Failure: A valid token is treated as blanket permission. Fix: perform operation- and resource-level authorization at each boundary.
“The first agent’s approval covers every sub-agent”
Failure: A broad credential is forwarded through a delegation chain. Fix: issue attenuated, audience-restricted, short-lived rights and preserve the ultimate principal.
“The model will ask a human when needed”
Failure: Approval depends on an unlogged natural-language decision. Fix: make approval a protocol state such as AUTH_REQUIRED, with an external enforcement gate.
“Tool output is trusted because the tool is internal”
Failure: A compromised record or connector injects instructions. Fix: classify outputs as untrusted data, validate them, and run policy checks independently.
“Logs show what happened, but not why”
Failure: An audit contains only a final success message. Fix: record identities, scopes, policy decisions, approvals, inputs, tool calls, lineage, and revocations as linked events.
What is standardized today, and what remains unsettled
A2A, MCP, and work on agent identity and authorization are evolving. Implementations can differ in supported features, authentication profiles, conformance tests, and error handling. Verify the current specification and implementation status before relying on a feature in production; do not assume that two products claiming protocol support enforce the same policies.
NIST’s AI Agent Standards Initiative, announced on February 17, 2026, states: “The Initiative will ensure that the next generation of AI—agents capable of autonomous actions—is widely adopted with confidence, can function securely on behalf of its users, and can interoperate smoothly across the digital ecosystem.” NIST’s identity and authorization concept paper discusses OAuth 2.0 and extensions, OpenID Connect, policy-based access control, access delegation, logging and transparency, and prompt/data provenance. The NIST AI Risk Management Framework is voluntary, but its functions—govern, map, measure, and manage—provide a useful structure for designing, deploying, monitoring, and evaluating these controls.
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Deployment checklist
- Can every action be traced to a human or organizational principal?
- Are authentication and authorization implemented as separate checks?
- Does each delegation state its purpose, scope, audience, expiry, and revocation path?
- Are A2A messages and MCP calls enforced by policy outside the model?
- Are rights narrowed, rather than expanded, across sub-agent hops?
- Do destructive, financial, privacy-sensitive, or externally binding actions require explicit approval?
- Is
AUTH_REQUIREDor an equivalent blocked state visible, actionable, and logged? - Are prompt injection and untrusted tool output treated as authorization threats?
- Can operators reconstruct identities, data lineage, policy decisions, outcomes, and revocations from tamper-evident logs?
- Have expiry, revocation, replay, cross-tenant, partial-failure, and protocol-version scenarios been tested?
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