Independent Submission W. Han
Internet-Draft Individual
Intended status: Informational 21 April 2026
Expires: 23 October 2026
AI Manifest: Embedded Workflow Instructions for AI Agents
draft-han-ai-manifest-00
Abstract
This document specifies the AI Manifest protocol, a JSON-based format
for websites to declare step-by-step user interface (UI) workflow
instructions readable by autonomous AI agents. By embedding the
manifest, website operators allow AI agents using browser-automation
tools to execute multi-step transactions directly via Cascading Style
Sheets (CSS) selectors, without repeated analysis of the full
Document Object Model (DOM). The specification defines three
interoperable embedding methods, a SHA-256 canonical hash
verification procedure via a central trust registry, and security
mitigations against prompt injection attacks.
Empirical results from a reference implementation demonstrate an
81.9% reduction in input tokens consumed by the AI agent and an
increase in task success rate from 20% to 100% on a representative
multi-step transaction, compared with conventional DOM-analysis
approaches.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 23 October 2026.
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Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 3
3. Protocol Overview . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Embedding Methods . . . . . . . . . . . . . . . . . . . . 4
3.1.1. Method A: Well-Known URI . . . . . . . . . . . . . . 4
3.1.2. Method B: Hidden DOM Element . . . . . . . . . . . . 4
3.1.3. Method C: HTTP Response Header . . . . . . . . . . . 4
3.2. Manifest Schema . . . . . . . . . . . . . . . . . . . . . 4
3.3. Agent Detection Algorithm . . . . . . . . . . . . . . . . 5
3.4. Canonical Hash and Trust Verification . . . . . . . . . . 5
3.5. Execution . . . . . . . . . . . . . . . . . . . . . . . . 6
4. Central Trust Registry . . . . . . . . . . . . . . . . . . . 6
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
5.1. Well-Known URI Registration . . . . . . . . . . . . . . . 6
5.2. AI Manifest Actions Registry (initial) . . . . . . . . . 7
6. Security Considerations . . . . . . . . . . . . . . . . . . . 7
6.1. Prompt Injection Risk . . . . . . . . . . . . . . . . . . 7
6.2. Integrity of the Manifest . . . . . . . . . . . . . . . . 7
6.3. Transport Security . . . . . . . . . . . . . . . . . . . 8
7. Privacy Considerations . . . . . . . . . . . . . . . . . . . 8
8. Implementation Status . . . . . . . . . . . . . . . . . . . . 8
9. Intellectual Property Rights Disclosure . . . . . . . . . . . 8
10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 8
11. Normative References . . . . . . . . . . . . . . . . . . . . 9
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction
Large Language Model (LLM)-based AI agents increasingly interact with
web services via browser-automation protocols such as the Model
Context Protocol (MCP), Playwright, Puppeteer, and Selenium
WebDriver. Current agents typically parse entire DOM trees or
screenshots on every page to infer UI structure, producing three
well-known problems:
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1. Substantial token consumption due to repeated analysis of large
DOMs on every session.
2. High failure rates on complex multi-step transactional UIs such
as enterprise resource planning (ERP) systems, academic
manuscript submission portals, and government e-services.
3. Absence of a standardized mechanism for a website operator to
declare an AI-agent-friendly ("AI-Ready") operational surface.
Related prior work includes robots.txt, llms.txt, agents.txt, and ai-
plugin.json. These address crawling permissions, LLM-friendly
documentation, agent capability declarations, and API-level
integration respectively. None provides step-by-step UI workflow
instructions for multi-page transactional flows.
AI Manifest fills this gap by specifying a JSON format that
enumerates ordered UI operations keyed to CSS selectors. An AI agent
detects, parses, and verifies the manifest before executing the
listed steps, and avoids further DOM-based inference for those steps.
2. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
AI Manifest A structured data object, expressed in JSON, that a
website operator embeds into a web page or serves at a well-known
URI, containing at minimum a task identifier, an ordered steps
array, and for each step an action field and a CSS selector.
AI Agent A software process, typically driven by an LLM, that
accesses a web page via a browser-automation tool and executes
actions on behalf of a human user.
Central Trust Registry A network service that stores SHA-256 hash
values of manifests pre-registered by publishers, and responds to
real-time trust lookups from AI agents with a status of white-
listed, black-listed, or unknown.
Canonical Form The representation of an AI Manifest obtained by
lexicographically sorting all JSON object keys at every nesting
level and serializing the result using the JSON encoding defined
in [RFC8259] with UTF-8.
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3. Protocol Overview
3.1. Embedding Methods
A website MAY provide an AI Manifest via one or more of the following
methods:
3.1.1. Method A: Well-Known URI
The server SHOULD make the manifest retrievable at the following
well-known URI [RFC8615]:
/.well-known/ai-manifest.json
In addition, the HTML document SHOULD declare the entry point via an
HTML meta element:
3.1.2. Method B: Hidden DOM Element
The server MAY embed the manifest into the HTML response as a hidden
element with the display:none style and aria-hidden="true" attribute:
3.1.3. Method C: HTTP Response Header
The server MAY declare the manifest location and hash in a response
header:
X-AI-Manifest: url=/.well-known/ai-manifest.json;
hash=sha256:
Method C is RECOMMENDED in conjunction with Method A, so that an AI
agent can discover the manifest URL and validate the hash in a single
request-response round trip before fetching the body.
3.2. Manifest Schema
An AI Manifest is a JSON object [RFC8259] with the following top-
level fields:
version (string, REQUIRED) Specification version. This document
defines version "1.0".
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publisher (string, REQUIRED) Canonical domain name of the publishing
website.
manifestId (string, REQUIRED) Stable identifier scoped to the
publisher, used for registry lookup.
registry_url (string, REQUIRED) HTTPS URL of the trust registry that
the publisher has pre-registered this manifest with.
task (object, REQUIRED) Contains a task-level id and an ordered
steps array. Each element of steps is an object with at least
step (integer), action (string), and selector (string). The
action value MUST be one of the registered actions (see
Section 5).
3.3. Agent Detection Algorithm
Upon loading a page, an AI agent implementing this specification
SHOULD perform the following detection sequence before any full-DOM
inference pass:
1. Inspect the HTTP response headers for X-AI-Manifest (Method C).
2. If absent, retrieve /.well-known/ai-manifest.json (Method A) or
resolve the URI declared by the meta element.
3. If still absent, search the DOM for an element with id="ai-
manifest" and read its data-manifest attribute (Method B).
4. If none is found, fall back to conventional DOM-based inference.
3.4. Canonical Hash and Trust Verification
Prior to execution, the AI agent MUST compute a SHA-256 hash over the
canonical form (see Section 2) of the manifest and send a trust
lookup request to the URI in the registry_url field. The request
MUST use HTTPS [RFC2818] and MUST carry the tuple {publisher,
manifestId, hash} as a JSON body.
The registry response is a JSON object containing a status field with
one of the following values:
* "white" — the manifest is trusted; the agent MAY proceed to
execution.
* "black" — the manifest is explicitly distrusted; the agent MUST
abort and SHOULD alert the human user.
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* "unknown" — the manifest is not registered; the agent SHOULD warn
the user and MAY fall back to DOM-based inference.
Implementations MAY cache a non-expired registry response keyed by
the manifest hash, to avoid repeated network round trips for an
identical manifest.
3.5. Execution
When trust is confirmed, the agent executes the steps array in
declared order, mapping each step's action and selector to a browser-
automation primitive (e.g. "click", "fill", "select", "upload"). For
the duration of a manifest-driven execution the agent SHOULD NOT
perform additional LLM-based inference over the page DOM.
4. Central Trust Registry
A Central Trust Registry accepts manifest registrations from
publishers and answers real-time hash lookups from AI agents. A
conforming registry SHOULD:
* Store the SHA-256 hash of the canonical form of each registered
manifest, together with the publisher and manifestId fields.
* Perform static analysis on the submitted steps array and reject or
black-list manifests whose selectors or actions match a published
pattern of prompt-injection risk (for example, selectors targeting
iframe elements for cross-origin form submission, or actions
outside the registered action set).
* Expose a community-driven mechanism for reporting and black-
listing malicious manifests.
This document does not mandate a specific registry operator.
Multiple interoperable registries MAY exist, and each manifest
declares which registry is authoritative for it via registry_url.
5. IANA Considerations
5.1. Well-Known URI Registration
This document requests IANA to register the following entry in the
"Well-Known URIs" registry established by [RFC8615]:
URI Suffix: ai-manifest.json
Change Controller: Independent Submission Stream editor
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Reference: This document
Status: provisional
Related Information: None
5.2. AI Manifest Actions Registry (initial)
This document requests IANA to create a new registry named "AI
Manifest Actions", with the following initial registrations.
Registration policy: Specification Required [RFC8126].
click Invoke a click event on the selected element.
fill Type a value into a text input element.
select Choose an option from a drop-down list element.
upload Attach a file to a file input element.
wait Pause for a condition or duration.
navigate Change the current URL.
assert Verify that a condition holds before proceeding.
6. Security Considerations
6.1. Prompt Injection Risk
A malicious website could embed an AI Manifest whose steps array
leads an AI agent to perform actions harmful to the user (for
example, submitting a form to a third party with user-supplied
credentials). The Central Trust Registry mechanism (Section 4) is
the primary mitigation. Agents MUST NOT execute a manifest whose
registry lookup returns "black" and SHOULD warn the user before
executing an "unknown" manifest.
6.2. Integrity of the Manifest
The SHA-256 hash is computed over the canonical form of the manifest
so that semantically equivalent encodings produce identical digests.
Implementations MUST NOT rely on a hash computed over non-canonical
bytes.
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6.3. Transport Security
All communication with the registry MUST use HTTPS with server
authentication per [RFC2818]. Registry operators SHOULD sign their
responses with a public key published out of band so that an AI agent
can verify the integrity of a cached response.
7. Privacy Considerations
Registry lookups necessarily expose to the registry operator the fact
that a particular AI agent has visited a particular publisher's
manifest. Registry operators SHOULD minimize the retention of client
identifiers associated with lookup requests. Agents MAY employ
private, time-limited caching of registry responses to reduce the
frequency of such lookups.
8. Implementation Status
Note to RFC Editor: This section is intended to be removed prior to
publication as an RFC.
A reference implementation, including an example publisher server, a
reference registry, two AI agent variants (DOM-analysis baseline and
manifest-aware), and an automated benchmark harness, is available at
https://github.com/11pyo/AINavManifest under the MIT License.
In the reference benchmark — a two-step ERP order-entry transaction
repeated 30 times with input tokens counted via the tiktoken
cl100k_base encoding — the manifest-aware agent consumed an average
of 341 input tokens per task with a 100% task success rate (30 of 30
runs), while the DOM-analysis baseline consumed an average of 1887.6
input tokens with a 20% success rate (6 of 30 runs). Raw results
accompany the reference implementation.
9. Intellectual Property Rights Disclosure
The technology described in this document is the subject of Korean
Patent Application No. 10-2026-0071716, filed on 2026-04-21 by the
author. The applicant commits to offer any essential claims under
Fair, Reasonable, and Non-Discriminatory (FRAND) terms to
implementers of this specification, as declared in the project
repository.
10. Acknowledgments
The author thanks the Anthropic Claude Code, Model Context Protocol,
and OpenAI function-calling communities for the empirical
observations that motivated this work.
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11. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
.
[RFC2818] Rescorla, E., "HTTP Over TLS", RFC 2818,
DOI 10.17487/RFC2818, May 2000,
.
[RFC8126] Cotton, M., Leiba, B., and T. Narten, "Guidelines for
Writing an IANA Considerations Section in RFCs", BCP 26,
RFC 8126, DOI 10.17487/RFC8126, June 2017,
.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, .
[RFC8259] Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
Interchange Format", STD 90, RFC 8259,
DOI 10.17487/RFC8259, December 2017,
.
[RFC8615] Nottingham, M., "Well-Known Uniform Resource Identifiers
(URIs)", RFC 8615, DOI 10.17487/RFC8615, May 2019,
.
Author's Address
Won-pyo Han
Individual
Korea, Republic of
Email: pk102h@naver.com
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