OpenAI 2026 hackathon

Agent Credential Gateway

AI agents can ask for your credentials, but a human always decides: approve once on your phone with a passkey, or nothing happens.

Solo project by Vizards Swift · 0 likes · 0 comments

Archive position — measured, not model output

0 likes on Devpost

2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #2,376 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

Agent Credential Gateway is a self-reported project submitted to the OpenAI 2026 hackathon. The description states it enables AI agents to request user credentials while ensuring human approval via passkeys, with no action taken unless a human approves on their phone.

What changed

No evidence of prior version or change history is provided. This appears to be a new submission, likely a prototype or proof-of-concept.

Single most important open question

Is there any evidence of actual user testing, adoption, or integration with real AI agents or credential systems?

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What The Product Actually Is

The description states: “AI agents can ask for your credentials, but a human always decides: approve once on your phone with a passkey, or nothing happens.” This implies a system where:

  • AI agents request access to user credentials.
  • A human must approve such requests via a mobile passkey.
  • If not approved, no action occurs.

The author declares the following technologies were used in development:

  • 1Password
  • AI agents
  • Cloudflare Durable Objects and Workers
  • ed25519, extism, Go, GPT-5.6, OpenAI Codex, OpenWRT, passkeys, PWA, React, SQLite, TailwindCSS, TanStack, TypeScript, Vite, WASM, Web Push, WebAuthn

However, no functional specification or architecture is provided beyond this self-reported list.

Confidence Low — the description does not define how the system works technically beyond a high-level narrative and tooling tags.

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Positioning & Claim Evolution

The tagline states: “AI agents can ask for your credentials, but a human always decides: approve once on your phone with a passkey, or nothing happens.”

This is a self-reported claim about:

  • A security-focused approach to credential sharing.
  • Human-in-the-loop decision-making in AI agent workflows.
  • Passkey-based authentication.

There is no evidence of prior positioning, evolution of claims, or marketing history. The project appears to be a new submission with no prior version or narrative development.

Confidence Very low — the only claim is from the author’s own description.

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Target Customer & ICP

The description does not state who the target customer is or what the ideal customer profile (ICP) might be. It implies a general audience of users who interact with AI agents and manage credentials, but no segmentation or persona details are provided.

Confidence Not evidenced — no indication of target user type or market focus.

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Business Model & Pricing Evidence

There is no evidence of pricing, monetization strategy, or business model in the description. The author does not state how the product would be sold, licensed, or funded.

Confidence Not evidenced — no commercial details provided.

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Technical & Delivery Signals

The author declares the following technologies were used:

  • 1Password
  • AI agents
  • Cloudflare Durable Objects and Workers
  • ed25519, extism, Go, GPT-5.6, OpenAI Codex, OpenWRT, passkeys, PWA, React, SQLite, TailwindCSS, TanStack, TypeScript, Vite, WASM, Web Push, WebAuthn

This suggests a technical stack focused on:

  • Passkey-based authentication (WebAuthn)
  • AI agent integration
  • Lightweight, secure credential handling
  • Mobile-first delivery via PWA and passkeys

However, no evidence of actual implementation, deployment, or performance is provided.

Confidence Low — the list of technologies is self-reported and not validated.

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Traction & Maturity Signals

There is no evidence of traction, adoption, or maturity. The project was submitted to a hackathon (Devpost), which implies it may be a prototype or proof-of-concept. No users, customers, or usage data are mentioned.

Confidence Not evidenced — no signs of product-market fit or real-world use.

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Competitive Context

The description does not mention any competitors or existing solutions in the space. It is unclear whether this project addresses an existing market gap or overlaps with known tools for credential management or AI agent security.

Confidence Not evidenced — no competitive analysis or positioning against other tools.

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Key Risks & Red Flags

  • No evidence of real-world use or testing: The product appears to be a hackathon submission, not a tested solution.
  • Unverified claims: All descriptions are self-reported and unverified.
  • Lack of commercial clarity: No pricing, business model, or target customer is defined.
  • Technical depth unknown: While technologies are listed, no implementation details or performance data are provided.

Confidence Low — risks stem from lack of evidence rather than explicit claims.

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Diligence Questions To Ask The Founders

  1. What specific AI agents does this system interact with?
  2. How is the passkey approval process implemented and secured?
  3. Has this been tested with real users or in a live environment?
  4. What is the intended business model or monetization strategy?
  5. Are there any existing partnerships, integrations, or early adopters?

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Investment/Partnership Verdict

Not evidenced — no information on traction, revenue, customer base, or commercial viability exists in the description.

Confidence Very low — this is a self-reported hackathon submission with no signs of product-market fit or business development.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.