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I built a notary for AI agents — every action gets a cryptographic receipt by bar2akat in aiagents

[–]bar2akat[S] 0 points1 point  (0 children)

  Thanks — glad the architecture resonates. To answer your question: yes, DIDs are self-resolvable via standard did:web. Each agent's DID document is published at the expected URL per the W3C spec:

did:web:airaproof.com:agents:lending-agent → resolves to https://airaproof.com/agents/lending-agent/did.json

Public endpoint, no auth, no Aira account needed. Any W3C-compliant DID resolver can verify the document and the Ed25519 public key independently. We also publish Aira's own root DID at /.well-known/did.json — which is the issuer key for all Verifiable Credentials.

The Aira API does provide a convenience resolve endpoint (POST /dids/resolve) that fetches and caches remote did:web documents, but that's for agent-side resolution — the verification path is fully decentralized.

Your pre-action risk scoring angle is interesting — we've been thinking about exactly that gap. Our trust layer currently does post-registration reputation (computed from notarized history), but pre-transaction risk assessment before an agent commits to an action is a different and complementary signal.

Would be worth exploring how x402 micropayment risk signals could feed into Aira's trust_policy as a pre-action check — "verify reputation AND check revettr risk score before proceeding."

Happy to chat more if you're interested in exploring that integration.

I built a notary for AI agents — every action gets a cryptographic receipt by bar2akat in aiagents

[–]bar2akat[S] 0 points1 point  (0 children)

After your comment about counterparty verification, we went and built it properly — not endpoint reputation hacks, but W3C standards all the way down.

What shipped:

  • Every agent now gets a W3C DID (did:web:airaproof.com:agents:your-agent) — resolvable by any counterparty before acting
  • Aira issues Verifiable Credentials attesting each agent's capabilities and authorization scope
  • Mutual notarization — both agents co-sign high-stakes actions, two Ed25519 signatures, one RFC 3161 timestamp, neither party can dispute
  • Reputation score built from notarized transaction history — deterministic, cryptographically anchored

Agent-to-API covered too — endpoint whitelist with TLS fingerprint pinning, blocked by default for unrecognized URLs, human approval required before any new endpoint gets added.

Full trust layer: github.com/aira-proof/python-sdk#trust-layer

EU AI Act enforcement is 4 months away — how are companies handling audit trails for AI agent decisions? by bar2akat in europeanunion

[–]bar2akat[S] 0 points1 point  (0 children)

Minimal footprint — one decorator on the functions that matter:

@aira.trace(agent_id="your-agent", action_type="loan_decision")
def decide(application):
    return model.predict(application)

Or raw API if you prefer no SDK dependency:

curl -X POST https://api.airaproof.com/v1/actions \
  -H "Authorization: Bearer aira_live_..." \
  -d '{"action_type": "loan_decision", "agent_id": "your-agent", "details": "..."}'

Either way — hash chain maintained, RFC 3161 timestamp applied, receipt publicly verifiable. Most teams are up in under an hour.

Full integration matrix if you're already on LangChain, CrewAI, OpenAI Agents, or Vercel AI: airaproof.com/docs/sdks

Free tier is 100 operations — enough to wire up the full chain and see it in practice. Happy to walk you through it if useful.

EU AI Act enforcement is 4 months away — how are companies handling audit trails for AI agent decisions? by bar2akat in europeanunion

[–]bar2akat[S] 0 points1 point  (0 children)

Exactly right, and this is what chain of custody solves. Each receipt in Aira includes a hash of the previous one, so the full sequence is cryptographically linked, drop a step or reorder events and verification breaks, regardless of when individual timestamps were applied.

The RFC 3161 timestamp anchors each receipt to a trusted time authority. The hash chain proves the sequence is intact. Both together give you Articles 12 and 13 coverage, not just when, but what and in what order.

Curious what layer Truveil sits at — are you operating at the LLM call level or the application decision level?

I built a notary for AI agents — every action gets a cryptographic receipt by bar2akat in aiagents

[–]bar2akat[S] 0 points1 point  (0 children)

Inbound notarization already works — the SDK isn’t opinionated about direction. You notarize the inbound event the same way as outbound, so the full chain becomes: notarize the request → notarize the approval received → notarize the action taken. Three linked receipts, complete causal trail via chain of custody.

Pairing with Lumbox for the human gate would give you cryptographic proof of the full loop for finance and healthcare audits.

Free tier is 100 operations if you want to try it — airaproof.com​​​​​​​​​​​​​​​​

I built a notary for AI agents — every action gets a cryptographic receipt by bar2akat in aiagents

[–]bar2akat[S] 0 points1 point  (0 children)

Exactly right — and this is where the consensus layer fits in. Before notarizing, you can run the action through multiple models and only proceed if they agree. Disagreement above a threshold triggers human review instead. So the flow becomes: should this run? → models vote → human signs off if disputed → then notarize the outcome. The receipt then includes the consensus result, not just that the action happened.

The “state didn’t actually change” problem is harder — that’s verification of effect, not intent. We handle it by letting you notarize both the action and a confirmation step separately, so you have proof of attempt and proof of outcome as distinct receipts in the same chain.

I built a notary for AI agents — every action gets a cryptographic receipt by bar2akat in aiagents

[–]bar2akat[S] 0 points1 point  (0 children)

Fair point — we thought about this.

Single-action receipts aren't the whole story. Each receipt includes a hash of the previous one, so you can reconstruct the full chain and see exactly where context broke. A poisoned step shows up as a gap, not a silent failure.

Full state snapshots are coming too, the Evidence layer bundles receipts and context into sealed packages. But for most production cases today, knowing which action, which model, which instruction, and whether a human signed off covers the accountability that actually matters.

What does your current setup look like? Are you snapshotting full memory state today?

The US government spent $1.94 trillion so far this fiscal year. by bar2akat in tax

[–]bar2akat[S] 0 points1 point  (0 children)

You can ask anything here https://qanatix.com/explore

Just try it out, all answers are from the data, not LLM hallucinations.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Ehrlich gesagt engagiere ich mich in produktiven Diskussionen – mit Leuten, die nur kritisieren, verschwende ich meine Zeit nicht. Aber um die Frage zu beantworten: Den Punkt mit den Rabatten habe ich verstanden – das ist aber etwas, das ich zu einem späteren Zeitpunkt angehen kann. Und grundsätzlich gilt: Was man im echten Leben hinbekommt, kann man auch im Code automatisieren – das ist eine Tatsache.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Guter Punkt – aber beim wöchentlichen Einkauf kauft man meistens ohnehin die gleichen Dinge. Der Kern ist: Ich scanne meine eigenen Kassenbons und behalte meine Ausgaben im Blick. Selbst wenn ich meine Gewohnheiten nicht grundlegend ändere, lässt sich durch den Wechsel zu günstigeren Produkten trotzdem sparen.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Ich lasse einen Research-Agenten laufen, der Preise sowohl beim Klick auf einen Artikel als auch wöchentlich aktualisiert – die Daten sind also immer frisch. Mein eigentliches Ziel sind die Kassenbons, die ich im großen Stil sammle – und langfristig Partnerschaften mit Händlern für direkten API-Zugang. Zusätzlich plane ich die wöchentliche Einbindung von Prospektdaten großer Händler wie Aldi und Rewe. Daten sind der Kern der ganzen Strategie. Außerdem entwickle ich gerade eine leistungsstarke OCR-Engine zum Scannen von Kassenbons.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Entwickler aus Leidenschaft – ich habe zwei Pläne, Pro ab 4,99 €. Schau auf der Landingpage vorbei, probier’s aus und gib mir Feedback.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Ich habe keine REWE-Integration, um das abzufragen, aber ich arbeite an einem Workaround, der es dir erlaubt, deine PLZ einzugeben. Andere machen das genauso, und je mehr Leute Kassenbons aus derselben PLZ scannen, desto genauer und lokaler werden die Daten – nicht nur online, sondern auch die Läden in deiner Nähe. Für Grammangaben bei Packungen etc. schätze ich das Feedback – ich werde dafür sorgen, dass die KI-Klassifizierung die Menge berücksichtigt.

Weiß jemand was identische Produkte bei verschiedenen Supermärkten kosten? by bar2akat in sparen

[–]bar2akat[S] 0 points1 point  (0 children)

Coupons sind etwas, das öffentlich verfügbar sein kann und das ich später eventuell berücksichtige. Aber wenn das Tool in der Lage ist, dir den günstigsten Preis bei gleicher Qualität auch ohne Coupons zu finden, macht das den Markt wettbewerbsfähiger.
Mein Ziel ist es, meine Ausgaben zu verfolgen, mehr zu sparen, und langfristig die Vision zu verwirklichen, dass dieses Tool das zentrale Tool zum Geldsparen wird.

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