Does the delegation pattern require changes to every downstream service?

The downstream services that hold PHI, PII, or regulated data have to verify the delegation to satisfy the audit requirement. Services outside that scope can continue with the agent's service credential. Most programs scope the delegation rollout to the regulated services first and extend coverage based on audit needs.

How does the delegation interact with OAuth scopes?

The delegation token can be structured as an OAuth token with custom scopes that name the originating user and the task identifier. Programs running OAuth-based service integration can extend the existing pattern with delegation scopes rather than introducing a parallel token format.

What about agents that operate on behalf of no specific user (cron-style background tasks)?

Background agent tasks that no user initiated do not have a natural-person identity to carry. The record series captures the agent identity and the policy version, with an explicit "no originating user" field that distinguishes the task from user-initiated agent runs. The audit treats background agent tasks as a separate category from user-initiated ones.

How does the proxy know which user initiated the agent task?

The agent runtime presents the delegation token at the proxy. The token carries the originating user identifier signed by the corporate IdP. The proxy verifies the signature and extracts the identity. The pattern requires the agent runtime to obtain the delegation token before calling the LLM, which adds one IdP round-trip at the start of each task.

Does the delegation pattern reduce agent autonomy?

The delegation defines the scope the agent operates inside, not the agent's reasoning or planning. The agent can still plan, decompose, and execute. The delegation constrains what the agent's tool calls are authorized to do against the downstream services. Programs that adopt the pattern usually find that the explicit scope improves agent reliability because the failures the agent would have produced outside scope now surface as authorization errors rather than incorrect actions.

The AI Agent Post-Authentication Gap: Why Identity at Login Is Not Identity at the Tool Call

Most enterprise agentic AI architectures I see in production authenticate the user at the start of the session and then run the agent with a service identity that carries no user context on the downstream calls. The user logged in. The agent took it from there. The gap between the login identity and the per-tool-call identity is the post-authentication gap, and it breaks the audit record that the EU AI Act, HIPAA, and the NIST AI agent identity framework expect.

I want to walk through how the gap forms, what it looks like in production traffic, what fails on the audit record, and the architectural pattern that closes the gap without breaking the agent's ability to operate.

How the gap forms

The pattern starts the way most session-based applications start. The user authenticates at the application boundary against the corporate IdP. The application receives a session token. The user issues a high-level instruction: "summarize the last quarter's tickets for the enterprise tier." The application hands the instruction to the agent runtime. The agent runtime calls the LLM with the instruction. The LLM returns a plan that calls four tools in sequence. The agent runtime calls each tool. Each tool runs against the downstream service with a service credential the agent runtime holds.

At step one, the request carries the user identity. By step three (the LLM call), the request carries a service identity for the agent runtime. By step five (the first tool call), the request carries the agent's service identity scoped to the tool. By step seven (the downstream API call), the request carries whatever credential the tool was configured with. The user identity decouples from the operational identity inside two steps, and the audit record on the downstream services shows the agent acting alone.

What this looks like in production traffic

A typical incident I see: the agent ran a workflow over a weekend, called a customer-data API forty-seven times, and exfiltrated data the originating user did not have authorization to see. The audit logs on the customer-data API show forty-seven calls from the agent's service credential. The agent runtime's logs show the user who initiated the session but not the per-call propagation. The LLM provider's logs show the model calls without the natural-person identity.

The audit question (which user authorized this exfiltration) has no clean answer. The answer reconstructs by manual correlation across timestamps and session identifiers in three different stores. The correlation often fails because the agent's session timeout was longer than the user's session, and the user logged out before the agent's last tool call.

What fails on the audit record

The post-authentication gap breaks three specific audit record fields. The natural-person identity is missing from the per-tool-call record. The Article 19 requirement (identification of natural persons involved) fails. The authorization basis for each action is missing because the service credential's authorization is not the same as the user's authorization. The HIPAA Security Rule access management requirement (164.308(a)(4)) fails because the access control did not bind to the user. The policy decision basis is missing because the agent's service credential typically holds blanket authorization rather than the user-specific authorization the policy intended.

The three failures show up as audit findings, not as runtime errors. The system runs. The audit fails six months later.

What closes the gap

The fix is to carry the originating user identity on every downstream call as a delegation. The agent inherits a delegation from the user at the start of the task. The delegation is a structured assertion: "this agent is acting on behalf of user X with scope Y for the duration of task Z." The delegation flows through to each tool call and each downstream API call as a header or a token field. Each downstream system can verify the delegation, evaluate authorization against the user's scope, and emit an audit record that carries both the agent identity and the originating user identity.

The pattern is what the NIST AI agent identity and authorization framework at NCCoE calls out as the central control surface. The comment window on the project closed April 2, 2026. The recommended pattern has the delegation flowing through the entire agent task, with verification at each downstream system.

The HTTP inspection layer at the LLM boundary is one of the points where the delegation gets attached to the record. The layer authenticates the originating user when the session starts, captures the delegation when the agent runs the task, emits the per-LLM-call record with both identities, and propagates the trace_id plus the delegation to the agent runtime for the downstream tool calls.

How the delegation flows in practice

A working delegation flow:

The audit reconstructs the task from any of the records because each one carries the originating user identity, the agent identity, and the task_id that joins the series.

Where the design fails in real implementations

The most common failure is the short-lived delegation token that expires before the agent task completes. Long-running agents (hours, days) outlast the delegation expiry, and the implementation falls back to the agent's service credential at some point in the chain. The fix is renewable delegations: the agent runtime requests a new delegation when the previous one approaches expiry, with the renewal scoped to the same task identifier.

The second common failure is the downstream system that does not verify the delegation. The agent attaches the token but the downstream service does not check it. The downstream service emits a record with the service credential identity, not the user identity. The fix is enforcement at the downstream system: the delegation token has to be the field the authorization decision uses, not a metadata header that the service ignores.

The third common failure is the implicit delegation that the agent runtime treats as universal. The user logged in once and the runtime assumes the user authorized everything the agent might do for the rest of the session. The fix is explicit per-task delegation: the user authorizes specific tasks rather than the agent's general operating authority.

Regulatory framing

EU AI Act Article 12 requires automatic recording of events sufficient to ensure traceability. Article 19 specifies identification of natural persons involved. For agentic AI in high-risk use cases, the natural person is the originating user, and the record has to carry the identity on each step the agent runs.

NIST AI agent identity and authorization (NCCoE project, comment window closed April 2, 2026) frames agent identity and authorization as the primary control surface for agentic AI. The project's recommendations call for the delegation pattern across the agent task.

HIPAA Security Rule 45 CFR 164.312(d) covers person or entity authentication. The agent acting on behalf of a user inherits the user's authentication, but the record has to show the chain. The post-authentication gap breaks the chain.

DeepInspect

DeepInspect closes the LLM-side of the post-authentication gap. The proxy authenticates the agent at the request boundary against the corporate IdP, extracts the delegation token from the agent runtime, captures both the agent identity and the originating user identity on every LLM call, and commits the audit record with both fields on the same series. The trace_id propagates to the agent runtime for downstream tool calls, which lets the lineage flow end-to-end.

For programs running agentic AI in production today, the proxy placement is the field that produces the per-call record with the natural-person identity Article 19 expects. The placement does not depend on each application team wiring the delegation through their tool integrations.

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