What is agent discovery optimization?
Agent discovery optimization is the practice of making a product or service discoverable and assessable by an AI agent during a defined task.
The phrase can refer to a browsing agent finding product facts, a directory exposing a connector, or a system selecting an available action. Those are different discovery environments. A useful definition names the environment before recommending changes.
The library distinguishes what an assistant can learn from a website from connecting to a service. This entry concerns the umbrella term, not a universal installation recipe.
The earliest dated record we verified
Gabriela Marco’s Exista.io working paper carries a February 2026 dateline and explicitly proposes Agent Discovery Optimization. Its Zenodo deposit records publication on 22 February 2026; the author’s ResearchGate upload is dated 6 March 2026. These are distinct dated records. The complete deposited original supplies the title page and §4, PDF page 7.
The paper offers a conceptual framework and a proposed composite score. It is evidence that the term was used, not proof of a universal agent-selection mechanism or validated performance of the score. We do not repeat its market forecasts or adopt its technical recommendations as operator requirements.
Practitioners make origin claims. Those claims do not settle priority without dated supporting artifacts. The earliest record located for this definition is therefore a bounded research finding, rather than a sole-inventor verdict.
Current vendor usage varies
Omnia’s knowledge-base entry uses the phrase for helping agents find, evaluate and choose a brand while researching or acting for a user. The opened article supplies no publication date; we record it as undated, checked 30 October 2026, rather than manufacture one. [Omnia, Agent discovery optimization].
A Maven event listing featuring Agent Discoverability co-founder Elliot Garreffa also uses the phrase in his biography, focused on product selection when an agent needs to act. Its advertised session date is 11 November 2026, which was still in the future when checked. It is a listing, not evidence that the session occurred or that its claims were demonstrated. [Agent Discoverability & Level 250, listing].
These sources share an interest in being found by agents but do not establish interchangeable metrics. A directory-specific discoverability assessment should not be represented as coverage of every browsing assistant.
What would an observation measure?
Define a task, an eligible candidate set and an observable outcome. Finding a product page, identifying a compatible connector, selecting an action and completing that action are separate events. Preserve failures at each stage instead of counting every technical availability check as a completed task.
State the discovery channel. An installed connector may be available to a particular account while absent from a public directory. A browsable product page may supply facts without exposing any action. Authentication and user authorization can properly limit what the agent may do after discovering a service.
For a repeatable assessment, keep task wording, account conditions, observation date and the expected eligible alternatives. A success rate needs a denominator of attempted eligible tasks. A checklist score needs its chosen weights and rationale. Neither automatically estimates how real buyers distribute their tasks.
What can a publisher improve responsibly?
Publish accurate product identity, capabilities, exclusions and stable documentation. Make important information accessible in the formats actually supported by the intended discovery channel. If a service offers actions, describe prerequisites and permissions accurately rather than implying that an agent can complete them unaided.
Use current documentation for a particular protocol or directory. A conceptual marketing paper cannot establish that every website needs an agent endpoint or a special root file. A static informational site and an action service have different responsibilities.
Test the actual task after a change. Discovery alone does not demonstrate preference, successful use or a commercial result. Keep each observed stage named so a technical repair does not become an unsupported growth claim.
Omnia’s definition concerns agents “when they autonomously research, compare, and take actions”. That expands the proposed objective beyond appearing in a displayed answer, while leaving the actual mechanism and outcome to be measured. [Omnia, Agent Discovery Optimization, opening definition; verified 30 October 2026].
You need task-level evidence to assess autonomous discovery.
What our own data shows
We have no measurement on this point: autonomous task completion or connector-directory selection. The Absence Index observes answers to category questions. We publish naming and citation outcomes under that narrower description.
Our crawler log also records requests, not discovery success. Its initial snapshot contained 83 observations after removing our own check, with 0 declared AI user-agent matches; Wilson 95% interval 0% to 4.4%. The operational crawler log explains the short window, matching limits and later privacy correction. This is no estimate of how often an agent can find or use a product.
Sources
- Marco, G. (2026, February 22, Zenodo publication date). Beyond AEO: The AI Visibility Stack and the era of agent discovery optimization. Exista.io working paper; February document dateline, title page and §4 (PDF p. 7). Zenodo. https://doi.org/10.5281/zenodo.18728630 . Complete deposited original. Separate author upload on ResearchGate dated 6 March 2026. Verified 30 October 2026.
- Omnia. (n.d.). Agent discovery optimization (ADO). Knowledge base. No publication date exposed in the opened article; checked 30 October 2026. https://www.useomnia.com/knowledge-base/agent-discovery-optimization Verified 30 October 2026.
- Agent Discoverability & Level 250. (2026, November 11, advertised event date). Agent-led growth: How ChatGPT and Claude pick products. Maven event listing, speaker biography. Listing opened 30 October 2026, before the advertised event. Publication date unestablished. https://maven.com/p/a5f933 Verified 30 October 2026.
- Broadcastwell. (2026). Absence Index, release 2026-09 [Data set]. Zenodo. Release JSON, answer JSONL and README. https://doi.org/10.5281/zenodo.22907695 Verified 30 October 2026.
- Broadcastwell. (2026, October 30). AI crawler operational-log initial snapshot [Data set]. Collection window 05:35:44.478 to 05:38:00.771840 UTC; own check excluded. Snapshot CSV; published method and instrument definitions. Verified 30 October 2026.
Cite this page
Broadcastwell research team. (2026, October 30). What is agent discovery optimization?. Broadcastwell. https://docs.broadcastwell.com/research/glossary/agent-discovery-optimization/ Published 30 October 2026; updated 30 October 2026.
@online{broadcastwell2026research41,
author = {{Broadcastwell research team}},
title = {What is agent discovery optimization?},
year = {2026},
date = {2026-10-30},
url = {https://docs.broadcastwell.com/research/glossary/agent-discovery-optimization/},
urldate = {2026-10-30},
note = {Published 2026-10-30; updated 2026-10-30}
}
Prepared by the Broadcastwell research team. Lead: Maya Thompson, AI architect and specialist.
Sources checked 30 October 2026. Independent check: 30 October 2026, Broadcastwell verification desk. This page has not been peer reviewed.
Last updated: 30 October 2026. Next review: 30 January 2027.