The Tool Desk
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A safe design keeps a human or organization as the authority, binds that authority to an identified agent runtime, grants narrowly scoped and time-limited rights, and pauses for an explicit approval state when an action is consequential. Protocol compatibility makes messages understandable; it does not grant permission.
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What “negotiation” means for AI agents
When two agents negotiate, they are coordinating tasks and constraints: one agent may ask another to search, plan, verify, or perform an operation, while the receiving agent reports capabilities, requests missing information, proposes a result, or signals that authorization is required. The exchange can be multi-step and asynchronous rather than a single prompt and answer.
Negotiation has two separate questions:
- Can the agents communicate? A common protocol defines how they discover each other, describe tasks, send messages, and return results.
- May the requested action happen? Identity, delegated scope, policy, consent, and audit controls decide this at each service boundary.
An agent can be authenticated and still be unauthorized. Authentication establishes which agent or runtime is calling. Authorization evaluates whether that caller may perform this particular action for this particular principal, resource, purpose, and time window. Consent is the human or organizational decision to allow an action; delegation is the technical mechanism that carries a principal’s authority to an agent under defined limits.
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A2A and MCP solve different interoperability problems
| Aspect | A2A | MCP |
|---|---|---|
| Primary scope | Agent-to-agent collaboration | Agent-to-tool, data-source, and service access |
| What it standardizes | Discovery of other agents, task messages, and collaborative work across implementations | Consistent, structured discovery and invocation of external tools, data sources, and services |
| Typical question | “Which agent can handle this subtask, and how do we exchange its status and result?” | “Which tool or data source can this agent call, and with what operation and arguments?” |
| Permission implication | A successful agent-to-agent exchange does not itself authorize downstream effects | Tool availability does not itself prove that the user delegated permission to use the tool |
NIST’s identity and authorization concept paper describes MCP as a protocol that lets AI models and agentic systems discover, access, and interact with external tools, data sources, and services in a consistent, structured manner. NIST treats MCP and agent-to-agent protocols as complementary interoperability layers. In practice, an orchestrator may use A2A to ask a specialist agent for help and that specialist may use MCP to query a database or invoke a service. Every transition still needs a local policy decision.
The principal–agent–sub-agent–tool chain
A useful mental model is a chain of responsibility:
- Principal: the human or organization that grants authority, such as an employee acting for a company.
- Agent runtime: the software instance operating on the principal’s behalf. Its identity must be bound to a specific deployment, account, or workload rather than only to a model name.
- Sub-agent: another agent recruited for a bounded task. It may be operated by the same organization or by a separate one.
- Tool or service: the system that reads data or causes an external effect, such as changing a record or submitting a request.
At each hop, the receiving system should evaluate the caller’s identity, the original principal, the delegated scopes, the requested operation, the resource, and relevant policy. The sub-agent should not receive a broader right than the parent agent possesses. A tool should be able to reject an operation even when an upstream model decided that it sounded reasonable.
NIST’s summary of comments calls delegation “the foundational enabler of agent utility in the enterprise.” That utility creates a tracing obligation: a downstream service must be able to connect the action to the responsible human or institution, the intermediate agents, the scopes granted, and the policy decisions that allowed or denied it.
Authentication, authorization, delegation, and consent
Authentication identifies the caller
Authentication answers “who is calling?” It may bind a request to an agent identity, a workload, and an organization. It does not answer whether that identity may access a particular record or perform a particular side effect.
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Authorization evaluates the requested action
Authorization answers “is this operation allowed here and now?” The decision should consider the operation, resource, purpose, data sensitivity, time limit, and the principal’s policy. Do not let a model’s natural-language explanation substitute for an enforceable check outside the model.
Delegation carries limited authority
Delegation should express exactly what the agent may do and, where possible, attenuate authority as work moves downstream. A parent may authorize a sub-agent to read a specified dataset without allowing it to write, disclose, or invoke unrelated services. Short-lived credentials reduce the damage from leakage and force the system to renew authority deliberately.
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Consent is not implied merely because a user started an automation. For a high-impact operation, the workflow should present the proposed action, its target, material consequences, and the authority being used, then record approval, rejection, or correction. A pre-approved policy can cover low-risk, reversible work, but it should define the boundary rather than leave the boundary to model judgment.
How delegated authority survives multiple hops
Delegation becomes difficult when an orchestrator asks a second agent to ask a third service to act. If the downstream service sees only the immediate caller, it cannot reliably determine who authorized the operation or whether an intermediate agent expanded the request.
NIST’s comments on agent identity and authorization emphasize that authorization context must survive service boundaries and delegation chains. A practical chain carries:
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- the original principal and organization;
- the authenticated identity of every intermediate agent and runtime;
- the exact scopes and any resource, purpose, or amount limits;
- issuance, expiry, and revocation information;
- the policy decisions and human approvals that permitted the current step.
Each service boundary should validate the context instead of trusting a free-form claim passed in a prompt. Credentials should be short-lived, narrowly scoped, and attenuated when a sub-agent receives them. Revocation must be possible before natural expiry, especially when a user leaves an organization, a runtime is compromised, or an automation is found to be acting outside policy.
Where human oversight belongs
Human oversight works best when tied to risk, reversibility, and external impact rather than inserted randomly into every conversation.
Low-risk, reversible work
Within a documented policy, an agent can often classify documents, draft text, or prepare a proposed change without pausing. The system should still log the task, data used, tool calls, and outcome so that a person can inspect or undo it.
High-impact or irreversible work
Actions involving money, legal commitments, privacy-sensitive disclosure, destructive changes, safety, or external publication should pause for approval unless an explicitly governed exception exists. The approval screen should identify the requesting principal, the agents involved, the exact operation, the target, the scope being exercised, and the expected consequence.
The A2A AUTH_REQUIRED state
The A2A specification defines an AUTH_REQUIRED task state. A client can respond by negotiating the missing authorization, correcting the request, rejecting it, or fulfilling it after authorization. The next step may involve a human, another agent, or a service. Treat this as a real workflow transition: record when the state was entered, what evidence was requested, who approved or rejected it, and what operation followed.
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A permission-safe implementation sequence
- Define the principal. Record which human or organization is delegating authority and what accountability applies to that principal.
- Register agent identities. Bind each production agent and runtime to an identity that can be authenticated and independently disabled. Distinguish deployments, environments, and operators.
- Inventory capabilities. Describe what each agent and MCP-connected tool can read, create, change, transmit, or delete. Do not infer capability from a model’s advertised skill.
- Write narrow scopes. Separate read from write, limit resources and purposes, and impose quantity, financial, or time constraints where relevant.
- Issue attenuated, expiring credentials. A sub-agent receives only the subset required for its assigned task. Set an expiry and maintain a revocation path.
- Enforce policy at every boundary. The orchestrator, each A2A peer, and every MCP service should independently validate identity, delegation context, scopes, and current policy.
- Insert approval gates. Route high-impact, destructive, privacy-sensitive, financial, or externally binding operations to an explicit approval state such as
AUTH_REQUIRED. - Record an immutable event trail. Capture identities, task IDs, prompts or instruction references where appropriate, tool calls, data lineage, authorization evidence, approvals, denials, outcomes, and revocations.
- Test failure paths. Verify that expired credentials, revoked grants, scope escalation, unavailable approvers, malformed delegation context, and denied tool calls fail closed and produce useful records.
- Review and reduce authority. Examine logs and policy exceptions, remove unused scopes, rotate credentials, and update agent descriptions as implementations and protocol versions evolve.
Microsoft implementation guidance illustrates this pattern with OAuth 2.0 authorization-code flow and a check that the signed-in user is allowed to access the published agent. The important design lesson is not a particular vendor stack: the signed-in principal and the published agent must be connected by an enforceable authorization decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prompt injection and untrusted tool output
An agent may receive instructions embedded in a web page, document, email, API response, or another agent’s message. Those instructions can attempt to redirect the task, reveal secrets, or persuade the model to bypass a policy. Tool output is data, not an authorization grant.
Defenses belong outside the model’s natural-language reasoning:
- validate requested operations against machine-enforced scopes;
- treat retrieved content and peer messages as untrusted input;
- separate data channels from control and approval channels;
- require fresh authorization evidence for sensitive operations;
- block secret access and external side effects unless a policy engine permits them;
- make the user-visible approval describe the actual operation, not merely the agent’s explanation.
If an agent says that a document instructed it to upload credentials, the policy layer should still deny the upload. A convincing rationale cannot enlarge delegated authority.
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Audit, provenance, and non-repudiation
Observability is part of security. A useful record lets an investigator reconstruct what happened without relying on a model’s recollection. At minimum, retain the authenticated agent and runtime, principal, task and correlation identifiers, A2A messages, MCP tool calls, resources accessed, data lineage, scopes presented, policy decisions, approval events, results, errors, and revocations.
Provenance should connect an output to the instructions, data, tools, and agents that shaped it. Protect logs against alteration, restrict who can read sensitive records, and define retention according to organizational and legal requirements. A log that says only “agent succeeded” cannot establish accountability for a downstream action.
Cross-organization collaboration and federation
A2A can connect agents operated by different organizations, but interoperability does not create mutual trust. Before accepting work from an external agent, an organization needs a way to authenticate the peer, identify its operator, establish which authority it represents, and agree on scopes, data handling, and escalation. Federation policy should specify which credentials and claims are accepted, how revocation is communicated, and who investigates disputed actions.
When a request crosses organizational boundaries, preserve the original principal and the complete delegation chain while applying the receiving organization’s local policy. The recipient may narrow or refuse the request even when the sender’s policy allowed it.
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A2A, MCP, and NIST’s work on agent identity and authorization are evolving. Confirm the protocol version, conformance behavior, authentication options, and implementation status before putting an integration into production. Interoperability tests should cover discovery, malformed messages, unsupported capabilities, authorization failures, retries, duplicate requests, and human escalation.
The NIST AI Agent Standards Initiative announcement of February 17, 2026, describes a goal of enabling agents to operate securely on behalf of users and interoperate across the digital ecosystem. NIST’s AI Risk Management Framework is voluntary, but its functions can organize governance across design, deployment, monitoring, and evaluation. It does not replace an authorization system, identity provider, policy engine, or audit store.
A practical end-to-end example
Suppose a principal asks an orchestration agent to prepare a supplier payment. The orchestrator uses A2A to request verification from a finance agent. The finance agent uses MCP to read approved supplier data and payment limits. Each call carries the principal, agent identities, scopes, expiry, and correlation ID. The finance agent can report that the supplier is verified, but it cannot submit payment if its scope is read-only.
The orchestrator then creates a payment proposal. Because submission is financial and externally binding, the task enters AUTH_REQUIRED. The approval interface shows the amount, recipient, source account, agents involved, and policy evidence. A human approves or rejects. Only after approval does a payment service evaluate the still-valid, narrowly scoped credential and submit the transaction. The audit record links the final action to the principal, both agents, the MCP reads, the approval, and the service response.
If the approval expires, the credential is revoked, or the amount changes, the service should deny the request and require a new decision. The protocol exchange remains useful, but the side effect never depends on the model remembering an earlier instruction.
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