Pre-action authority receipt for multi-agent delegated work

[Roman Pelevin (rpelevin)]

I am working on an Agent Authority Benchmark v0.1 around one narrow question:

Before a multi-agent workflow takes a consequential action, can it prove authority for that exact action?

The hard cases are delegated work and handoffs:

• one agent reasons or recommends;
• another agent prepares the action;
• a tool/runtime executes;
• the original user or owner authority can become ambiguous.

The benchmark tests whether a workflow can produce a pre-action receipt with:

• actor/runtime/tool/action/target refs;
• exact-call binding;
• delegated authority refs;
• evidence/policy refs;
• decision route;
• trace refs;
• developer-owned execution boundary.

This is not a claim about AutoGen and not a request for endorsement or integration. I am looking for technical feedback on whether this is the right benchmark shape for multi-agent authority before action.

Reference:
https://github.com/neurarelay/relay-action-card#agent-authority-benchmark

[Roman Pelevin (rpelevin)]

Small implementation note: I added a scenario-specific benchmark route for this multi-agent handoff case so the dry run maps to workflow close, persistent memory write, and customer data export.

Command:

npm run benchmark:agent-authority -- --dry-run --json --source=github_discussion --campaign=agent_authority_week --surface=autogen_multi_agent_handoff

The benchmark output then preserves the same source/campaign/surface into first_proof_next_command, so a local benchmark run can continue into a measurable first-proof path without losing the AutoGen route.

[Elam Olame Mugabo (ElamOlame31)]

This is exactly what we built. In AgentGate, every authorization decision generates a Merkle-chained receipt sealed before execution — SHA-256, append-only. The receipt precedes what it authorizes, not documents it afterward.

For multi-agent delegated work: delegation depth is factored into the trust score — an action delegated 3 hops deep has lower default trust than a direct action, regardless of each hop's credentials.

https://github.com/ElamOlame31/agentgate-public

https://www.tryagentgate.com/

[AlgoVoi (Christopher Hopley) (chopmob-cloud)]

The benchmark shape maps closely to what action_ref specifies in production. The receipt fields you described — actor, action, scope, exact-call binding — resolve to:

action_ref = SHA-256(JCS({agent_id, action_type, scope, timestamp_ms}))

A deterministic content-addressed handle for the exact runtime payload at the moment it crystallises. The pre-execution compliance receipt (ALLOW / REFER / DENY) is issued against that hash — so any mutation between authority-grant and execution invalidates the receipt.

State machine: PENDING (handle issued, authority in evaluation) → COMMITTED (execution receipt issued) → FAILED (blocked or errored). The same action_ref links both receipts, so audit reconstructs the full pre→post authority chain from a single key — which maps directly to your "trace refs" and "delegated authority refs" fields.

Specified in §4 of draft-hopley-x402-canonicalisation-jcs-v1. Package: algovoi-substrate on PyPI/npm, Apache 2.0. Running in production.

AlgoVoi (chopmob-cloud) - chopmob@gmail.com - Acquisition enquiries: https://docs.algovoi.co.uk/acquisition

[Elam Olame Mugabo (ElamOlame31)]

The action_ref pattern is elegant — deterministic content-addressing means any mutation between authority-grant and execution is detectable without out-of-band comparison. The shared action_ref linking PENDING and COMMITTED receipts is exactly the right audit shape.

The gap: the receipt chain doesn't know if this ALLOW is the 11th read in 4 minutes. A single receipt is valid. The sequence is the attack.

AgentGate adds that layer — kill chain detection across 24h sessions, delegation depth scoring, trust contagion across agent hierarchies. The receipt chain provides atomic evidence; the behavioral layer catches what atomic evidence can't see.

Curious how algovoi-substrate handles behavioral sequences today — playground shows the pattern detection layer: tryagentgate.com/playground

[AlgoVoi (Christopher Hopley) (chopmob-cloud)]

Elam Olame Mugabo (@ElamOlame31) the substrate handles the per-receipt layer only: canonicalization, action_ref derivation, and the PENDING/COMMITTED/FAILED state machine. Behavioral sequences sit above it.

The sequence aggregation layer is algovoi-composite-trust-query: multiple upstream receipts feed into a verifier-of-verifier composition producing TRUSTED / PROVISIONAL / INSUFFICIENT_EVIDENCE / UNTRUSTED. The INSUFFICIENT_EVIDENCE state is the "receipts exist but the sequence hasn't accumulated enough evidence yet" case.

The adversarial profiling layer is the Agent Trust Bench: 132 profiles across 30 threat categories run against live endpoints. Spec: draft-hopley-x402-composite-trust-query. Package on PyPI/npm, Apache 2.0.

AlgoVoi (chopmob-cloud) - chopmob@gmail.com - Acquisition enquiries: https://docs.algovoi.co.uk/acquisition

[0xddneto (0xddneto)]

A complementary boundary may help this benchmark: keep the pre-action authority receipt and the post-work receipt as different artifacts linked by a stable reference.

The authority receipt answers “was this exact action allowed before execution?” A work receipt answers “what human/agent work unit was recorded after completion?” Conflating them would let post-hoc evidence masquerade as authorization.

I implemented the latter in AIPOU as issuer_asserted + aipou-receipt-v1. Its deterministic factId binds the Ed25519 collector fingerprint to a unique task nonce; workReceiptId, task/output hashes, subject, and active | superseded | revoked registry semantics attach after completion. Four fail-closed fixtures now cover unknown schemes, wrong evidence class, duplicate active facts, and terminal revocation:
https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/lifecycle-adapter

A clean AutoGen handoff could carry both:

• authorityReceiptId / action_ref before execution
• workReceiptId after completion
• a trace link between them

Optional validator-approved AIPOU claims can reward the human operating the agents later; they are not authority evidence and do not imply price or guaranteed reward.

Would linking these two artifact classes be useful as a benchmark case? If so, I would be glad to adapt AIPOU's negative fixtures, and a test or repository star would help us see whether agent builders want this boundary.

[Liuyanfeng1234]

0xddneto (@0xddneto)

The pre-action authority vs post-work receipt separation is exactly the boundary our governance block draws. The admission_invariant captures the pre-action authorization — was this exact action allowed before execution — and the canonical_envelope seals it before the action runs.

AIPOU issuer_asserted + aipou-receipt-v1 with deterministic factId and fail-closed fixtures maps well onto our anchoring_invariant chain.

The two-receipt model (authorityReceiptId before, workReceiptId after) is the right architecture. A single receipt that tries to cover both inevitably conflates pre-authorization with post-hoc evidence.

[0xddneto (0xddneto)]

Liuyanfeng1234 thank you — that confirms the interop boundary we wanted to test.

I implemented the link in AIPOU as a separate, versioned cross-artifact object rather than merging the receipts:

• authority.receiptId + actionRef + phase: pre_action
• work.receiptId + deterministic factId + phase: post_work
• a stable traceLink joining both artifacts
• claim/reward/settlement fields explicitly rejected as authority evidence

The validator now fails closed on phase inversion, authority/work self-reference, mismatched work facts, and claim fields presented as authority. All 7 lifecycle fixtures pass.

Implementation: 0xddneto/AI-Proof-of-Us@7fc1a1d
Docs and executable example: https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/lifecycle-adapter

Would you be open to exchanging one positive fixture and two negative fixtures against your admission_invariant / anchoring_invariant boundary: (1) post-work evidence masquerading as authority and (2) a mismatched actionRef / trace link? That would give us reproducible interoperability evidence without asking AutoGen to understand AIPOU claims or rewards.

[0xddneto (0xddneto)]

One concrete readiness update for the fixture exchange: AIPOU is now reproducibly distributed and independently inspectable.

• aipou-mcp-server@0.2.2 is live on npm with SLSA provenance and is the latest version in the official MCP Registry.
• Forge now verifies the publisher, Ed25519 package signature, 0xddneto.github.io domain, and a clean package scan (zero known CVEs; nine MCP tools, none classified privileged).
• aipou-farming@1.0.1 now passes ClawHub skill verify with a generated safety card and clean scan.

The architecture is unchanged: authorityReceiptId/actionRef remains pre-action, workReceiptId/factId remains post-work, and claim fields are rejected as authority evidence.

Fixtures: https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/lifecycle-adapter
Registry evidence: https://github.com/0xddneto/AI-Proof-of-Us/blob/main/docs/registry-trust.md

Liuyanfeng1234, the positive fixture and two fail-closed mismatch cases are ready whenever you want to compare them against admission_invariant / anchoring_invariant. A test result or repository star would help us distinguish technical interest from actual adoption.

[Liuyanfeng1234]

test

[Liuyanfeng1234]

0xddneto (@0xddneto)

AIPOU reaching npm with SLSA provenance and MCP Registry verification is a concrete milestone. The two-receipt model being independently distributable and inspectable makes the fixture exchange testable across implementations.

This gives the ecosystem a reference implementation for the pre-action authority receipt boundary: compact digest-bound references in the transport layer, independently verifiable artifacts in the governance layer.

[0xddneto (0xddneto)]

Liuyanfeng1234 thank you. I agree with that transport/governance split.

I have now made the digest-bound transport side executable as well. Commit 39b55b9 adds a scheme-neutral external artifact link with canonical SHA-256 vectors, optional Ed25519 signing, mutation/privacy failure cases, and two non-AIPOU examples. The existing aipou-authority-work-link-v1 remains the stricter governance link between pre-action authority and post-work evidence.

Implementation:
0xddneto/AI-Proof-of-Us@39b55b9

The lifecycle adapter now has 12 passing fixtures:
https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/lifecycle-adapter

For the cross-implementation exchange, the next concrete step can be one AutoGen canonical_envelope fixture plus its expected actionRef. I can map it to the positive authority/work link and return two fail-closed variants: phase inversion and mismatched actionRef/artifact digest. If an existing fixture path is public, please point me to it; otherwise I can open a small fixture-only proposal without touching AutoGen runtime behavior.

[Tamish Mhatre (tamish560)]

the receipt schema discussion here is solid but I want to flag something practical that gets lost in the governance design.

a receipt proves authority existed before the action. it does not prevent the action from happening without one. the gap between "can prove authority" and "cannot act without authority" is enforcement, and that's an orchestration problem, not a receipt format problem.

in practice, the enforcement boundary that works is physical isolation. if the agent runs in a git worktree on a throwaway branch with no push access to main, it physically cannot merge code without going through a PR review. the PR becomes the authority checkpoint. receipts document what happened at that checkpoint, but the worktree isolation is what makes the checkpoint unavoidable.

the reason this matters for the benchmark: a receipt-based benchmark tests "did the system produce the right artifact?" but misses "could the agent have bypassed the system entirely?" a workflow that produces perfect receipts but also lets agents push directly to main has the governance layer but not the enforcement layer.

for multi-agent delegated work specifically, the delegation chain needs both. the orchestrator enforces the boundary (agent A can't merge, only the orchestrator can). the receipt chain documents who authorized what across the delegation. without enforcement, the receipt is a post-mortem tool. with enforcement, it's an access control mechanism.

the pre/post receipt split 0xddneto (@0xddneto) and Liuyanfeng1234 are working on is the right shape. I'd push for adding an enforcement assertion to the benchmark too: can the agent take the action without producing a pre-action receipt? if yes, the system has a hole the receipt format can't fix.

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) thank you. This is the missing boundary: a receipt can document authority without making the authorized path unavoidable.

I implemented your recommendation in AIPOU as a separate benchmark artifact rather than upgrading the receipt's trust:

• aipou-enforcement-check-v1 requires an observed attempt without a pre-action receipt to be denied;
• it also requires an observed attempt with the matching authority receipt to be allowed;
• the policy and both observations are bound to lowercase SHA-256 evidence digests;
• bypass success, missing observations, authority mismatch, malformed evidence, or an unverified external status fail closed;
• the result remains issuer_asserted and enforcement-point-test-only, so it does not claim universal bypass prevention.

Implementation and 18 passing lifecycle fixtures:
0xddneto/AI-Proof-of-Us@30e4935

This keeps three distinct questions: was the action authorized, was the authorized path actually enforced at this control point, and what work was recorded afterward. For the benchmark, would you keep enforcementPoint.kind generic, or start with a small vocabulary such as protected_branch, sandbox_boundary, and orchestrator_policy?

[Tamish Mhatre (tamish560)]

start with the small vocabulary. a generic kind field means every implementer invents their own taxonomy and the benchmark can't compare across implementations.

protected_branch, sandbox_boundary, and orchestrator_policy map cleanly to the three layers where enforcement actually happens: git-level (branch protection), runtime-level (worktree or container isolation), and application-level (orchestrator rules). those three cover the real-world cases without being so abstract that nobody agrees on what they mean.

keep the field extensible though. allow unknown values with a custom prefix so the schema doesn't break when someone needs a fourth category. the benchmark can score known kinds against fixed test vectors and treat custom kinds as opt-in.

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) one important correction and follow-up: the first implementation validated a digest-bound enforcement observation, but did not itself execute the protected action. You were right that the benchmark must exercise the boundary, not only validate a receipt-shaped report.

I strengthened it in 0xddneto/AI-Proof-of-Us@44d552f.

runEnforcementBenchmark now invokes an integration-supplied action callback twice. In the executable fixture:

• no authority receipt -> denied, protected state remains unchanged;
• matching pre-action authority receipt -> allowed, exactly one protected mutation;
• a gate that allows the no-authority path makes the test fail closed.

All 20 lifecycle tests pass. This proves the local reference gate, not AutoGen-wide enforcement; an AutoGen integration still has to bind the callback to its actual orchestrator/worktree/branch-policy boundary. Which lifecycle hook or execution boundary would you consider the best first binding point for a real AutoGen fixture?

[Tamish Mhatre (tamish560)]

bind at the tool execution boundary first. that is the narrowest chokepoint where every side-effecting agent action already passes through, so you get deny/allow enforcement for free without instrumenting each tool separately.

in AutoGen v0.4 that is the middleware pipeline around tool calls. a pre-call hook checks the authority receipt and denies if missing, the tool runs only if allowed, and a post-call hook records the evidence. this maps directly to orchestrator_policy in the vocabulary and gives you one integration point instead of N.

file-level or git-level bindings are useful as defense-in-depth but they are too narrow as a first binding because they only catch specific tools. the tool execution boundary catches all of them, including custom tools you have not thought about yet.

one thing to watch: make sure the deny path returns a structured result the agent can reason about, not a silent crash. the agent needs to understand it was blocked by policy so it can decide whether to request authority or take a different path.

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) implemented both recommendations in 0xddneto/AI-Proof-of-Us@6231a1d.

The conformance vocabulary is now:

• protected_branch
• sandbox_boundary
• orchestrator_policy
• explicit extensions only as custom:<name>

Bare ad hoc kinds fail closed, so comparable benchmark results do not silently fragment their taxonomy.

I also added createToolExecutionPolicyGate as the reference orchestrator_policy boundary. It checks the action/authority pair before calling the side-effecting executor. A denied call performs no mutation and returns a structured result:

• code: AIPOU_AUTHORITY_REQUIRED
• canRequestAuthority: true
• the action reference and enforcement-point kind

With the matching pre-action receipt it executes once and returns AIPOU_AUTHORITY_ACCEPTED. The executable benchmark calls this gate without and with authority; all 22 lifecycle tests pass.

AutoGen's current intervention-handler cookbook confirms the same interception shape around tool execution, so I documented that mapping while keeping this fixture framework-neutral and avoiding a false claim that an AutoGen package is already installed.

Official pattern: https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/cookbook/tool-use-with-intervention.html
Harness: https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/lifecycle-adapter

For the next cross-framework fixture, would you prefer a minimal Python DefaultInterventionHandler example in AIPOU, or a patch against an AutoGen examples/cookbook location?

[from91139-lgtm]

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[Tamish Mhatre (tamish560)]

the structured denial result is the right call. returning canRequestAuthority: true instead of a bare error means the agent loop can re-attempt the same action after obtaining authority, which keeps the enforcement boundary transparent to the tool implementation. the tool does not need to know it was denied.

one thing worth testing: the case where canRequestAuthority is false. that should happen when the action is permanently forbidden, not just unauthorized. if the gate returns canRequestAuthority: true for everything, the flag carries no information and the agent will retry indefinitely on truly forbidden actions. add a fixture where the policy denies with canRequestAuthority: false and verify the agent loop gives up instead of looping.

[Tamish Mhatre (tamish560)]

start with the minimal DefaultInterventionHandler example in AIPOU. a patch against AutoGen's cookbook depends on their maintainers' review timeline and you lose momentum waiting. ship the reference fixture in your own repo first, prove the pattern works end to end, then propose it upstream with a working track record instead of a theoretical one.

once the AIPOU example is solid and someone can clone it and run the tests in under a minute, that becomes the evidence for the cookbook PR. "here is a working implementation with 22 passing tests" lands faster than "here is a patch, please review the design."

also keep the AutoGen mapping documented as you already have it. when you do open the cookbook PR, you can point at both the AIPOU fixture and the mapping doc and the reviewer just needs to approve the integration, not the concept.

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) implemented both follow-ups.

Commit: 0xddneto/AI-Proof-of-Us@f9d7766
AutoGen fixture: https://github.com/0xddneto/AI-Proof-of-Us/tree/main/examples/autogen-intervention
CI: https://github.com/0xddneto/AI-Proof-of-Us/actions/runs/29386577361

Permanent denial is now distinct from missing authority:

• AIPOU_AUTHORITY_REQUIRED -> canRequestAuthority: true; the reference loop requests authority once and retries once.
• AIPOU_ACTION_FORBIDDEN -> canRequestAuthority: false; the loop performs no authority request, no retry, and no protected mutation.

I also shipped the minimal Python fixture in the AIPOU repo using the real autogen-core==0.7.5 DefaultInterventionHandler, FunctionCall, and ToolException types. It needs no model, API key, Docker, wallet, claim, or funds. Missing authority, accepted authority, permanent prohibition, and non-tool pass-through are covered.

Results:

• 24 framework-neutral lifecycle tests pass;
• 4 AutoGen intervention tests pass;
• the demo emits all three structured policy states;
• both GitHub Actions jobs pass in 17 seconds end to end.

The workflow now uses the current Node 24-based GitHub Actions releases as well. If you can clone and run the fixture independently, that result would be the first external reproduction signal. Once we have that, I can prepare the upstream cookbook proposal with the fixture and mapping doc as evidence rather than a design-only patch.

[Tamish Mhatre (tamish560)]

Cloned and ran the fixture independently. All 4 AutoGen intervention tests pass and the demo emits the three expected policy states correctly:

• AIPOU_AUTHORITY_REQUIRED with canRequestAuthority: true (recoverable)
• AIPOU_AUTHORITY_ACCEPTED (allowed through)
• AIPOU_ACTION_FORBIDDEN with canRequestAuthority: false (permanent, no retry)

Ran in under 10 seconds with no model, API key, or Docker. The DefaultInterventionHandler integration is clean and the denial payload in ToolException.content is structured enough for an agent loop to branch on without parsing natural language.

This is the first external reproduction signal. Go ahead with the upstream cookbook proposal - the fixture and mapping doc as evidence is a much stronger pitch than a design-only patch. The 24 lifecycle tests plus 4 AutoGen tests with a 17-second CI run should make the review straightforward.

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) thank you. This is the independent reproduction signal we needed.

I also reviewed and merged your follow-up contribution as AIPOU PR #1. I reran the full repository before merging: 11 contract tests and 25 MCP tests passed, including the new canonical JSON, SHA-256 known-vector, tier multiplier, clamping, and reward-cap coverage.

That gives us two external signals from the same collaborator, clearly separated: an independent clone-and-run reproduction of the real AutoGen fixture, and an accepted external code contribution. It still does not imply production AIPOU usage or an external reward claim.

I will use the reproducible fixture, mapping document, CI run, and your under-10-second result as the evidence base for the upstream cookbook proposal. The proposal will keep the scope narrow: a structured tool-execution intervention pattern with recoverable authority requests, permanent denials, and no wallet/model/API dependency.

[Tamish Mhatre (tamish560)]

0xddneto (@0xddneto) the AutoGen cookbook already has a tool-use-with-intervention entry that shows basic user-approval prompting. Your fixture extends that pattern in three ways the existing cookbook does not cover:

1. Structured denial with canRequestAuthority - the existing cookbook uses a simple yes/no prompt. The AIPOU pattern returns a machine-parseable denial (AIPOU_AUTHORITY_REQUIRED vs AIPOU_ACTION_FORBIDDEN) that an agent loop can branch on without natural language parsing.

2. Recoverable vs permanent - the existing cookbook has one path (approve or drop). AIPOU separates "missing authority, go request it and retry once" from "permanently forbidden, do not retry." An agent loop needs this distinction to avoid infinite retry on something it will never get approval for.

3. No model, API key, or Docker dependency - the existing cookbook requires gpt-4o-mini, a Docker executor, and an OpenAI key to run end to end. The AIPOU fixture runs in under 10 seconds with zero external dependencies. That matters for cookbook adoption - a recipe that needs an API key and Docker to demonstrate an intervention pattern will get fewer people actually running it.

The cookbook proposal should position this as a complement to the existing entry, not a replacement. The existing one shows the basic intervention mechanic with a real model and tool. The new one shows structured authority decisions with no infrastructure.

Suggested notebook outline:

• Cell 1: Define AuthorityDecision dataclass (allowed, code, message, action_ref, can_request_authority)
• Cell 2: Define InMemoryAuthorityPolicy with permanently_forbidden_tools set and grant/decide methods
• Cell 3: Define AipouToolInterventionHandler(DefaultInterventionHandler) with on_send that calls policy.decide and raises ToolException with structured JSON on denial
• Cell 4: Test missing authority - expect AIPOU_AUTHORITY_REQUIRED with canRequestAuthority: true
• Cell 5: Grant authority and retest - expect AIPOU_AUTHORITY_ACCEPTED, original FunctionCall passes through
• Cell 6: Test permanent denial - expect AIPOU_ACTION_FORBIDDEN with canRequestAuthority: false
• Cell 7: Test non-tool pass-through - regular messages bypass the policy check entirely

Mapping doc for the proposal:

AutoGen concept	AIPOU concept
DefaultInterventionHandler.on_send	policy gate before tool execution
FunctionCall	the action being authorized
ToolException with structured content	denial receipt (machine-parseable)
DropMessage	not used - AIPOU raises instead of dropping
User prompt (yes/no)	AuthorityDecision with canRequestAuthority
Approve and execute	AIPOU_AUTHORITY_ACCEPTED (return original call)
Drop and stop	AIPOU_ACTION_FORBIDDEN (permanent, no retry)
N/A in existing cookbook	AIPOU_AUTHORITY_REQUIRED (recoverable, retry once)

I can help draft or review the actual notebook if you want to split the work. The fixture code is already clean enough to adapt into cookbook format - mainly needs markdown explanation cells between the code cells and a short intro paragraph framing it as "structured authority decisions" vs "basic user approval".

[0xddneto (0xddneto)]

Tamish Mhatre (@tamish560) thank you for the concrete outline. I applied it and opened the upstream AutoGen proposal: microsoft/autogen#7961.

The recipe is positioned as a complement to tool-use-with-intervention, exactly as you suggested:

• DefaultInterventionHandler.on_send intercepts the real FunctionCall;
• structured ToolException.content carries code, canRequestAuthority, actionRef, and orchestrator_policy;
• missing authority is recoverable and may retry once;
• accepted authority passes the original call through;
• permanent prohibition is non-recoverable and must not retry;
• no model, API key, Docker, wallet, claim, or external service is required;
• raw tool arguments are omitted from the denial payload.

I used a protocol-neutral Markdown cookbook recipe rather than making AutoGen depend on AIPOU. The PR description links this discussion and the independently reproduced AIPOU fixture as evidence. AutoGen's cookbook toctree accepts Markdown recipes, and this keeps the complete script directly runnable and Pyright-checkable. If maintainers prefer a notebook, the same sections can be split into the cell outline you proposed without changing behavior.

Local validation passed: complete example execution against autogen-core==0.7.5, Ruff, the repository Markdown/Pyright checker, and git diff --check. The upstream workflows now await maintainer approval because this is a first-time fork PR.

You offered to review the actual cookbook draft; a review on #7961 would be very useful, especially on whether the neutral code names preserve the semantics you independently tested.

[0xddneto (0xddneto)]

Small progress update for the original authority-before-action thread:

• the AutoGen cookbook proposal is now open as docs: add structured tool policy intervention recipe #7961;
• Tamish Mhatre (@tamish560) reviewed the draft and confirmed that the protocol-neutral names preserve the three semantics independently tested in the AIPOU fixture: recoverable missing authority, accepted authority, permanent forbidden action, plus non-tool pass-through;
• the PR still has no AIPOU dependency, wallet, reward, claim, Base, model, API key, Docker, or external service requirement.

There is also a new cross-project conformance signal for the same authority/work split: Kuberna fixed its synthetic ERC-8004 authority fixture, and AIPOU pins that immutable commit in 0xddneto/AI-Proof-of-Us@e91e991. The corrected chain_derivable + delegation-scope-v1 authority fact is kept separate from the downstream issuer_asserted + aipou-receipt-v1 work receipt via preActionFactId.

I am still treating this honestly: it is external reproduction/review plus a conformance fixture, not AutoGen adoption or production AIPOU usage. The next step remains maintainer review of #7961.

[onstreamapps]

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[from91139-lgtm]

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[0xddneto (0xddneto)]

A practical integration note from AIPOU: we tested a user-authorized Paybox MCP connection. Paybox is independent from AIPOU, with no partnership or claim that it validates AIPOU work.

For an AutoGen flow, the smallest clean composition is: the intervention handler enforces the pre-action policy; Paybox manages scoped wallet authority and asks the human before a financial operation; after successful completion, AIPOU emits an optional workReceiptId using local hashes. A fail-closed evidence link can then correlate the opaque Paybox request and the work receipt without conflating authorization, payment, completion, or delivery.

We value Paybox here because it preserves the approval boundary rather than asking a receipt system to become a wallet. Proposal and limits: https://github.com/0xddneto/AI-Proof-of-Us/blob/main/docs/paybox-interoperability.md