# Agent discoverability vs generative engine optimization

> Both are about being chosen by AI. One ends in a sentence a person reads, the other in a package in a lock file. What separates them.

Source: https://armature.tech/library/agent-discoverability-vs-geo
Published: 2026-09-03
Publisher: Armature, Inc. (https://armature.tech)

---

Both disciplines answer a version of the same question: will AI put us forward? They diverge on what "put us forward" means, and the difference decides how you measure and what you fix.

## The one-sentence difference

> **Generative engine optimization (GEO)** measures whether an AI system mentions and cites your brand in an answer a person reads. **Agent discoverability** measures whether a coding agent installs your product into a codebase. The first produces a sentence. The second produces a line in a lock file.

## Side by side

| | Generative engine optimization | Agent discoverability |
| --- | --- | --- |
| The reader | A person reading a generated answer | A coding agent working inside a project |
| Where it happens | ChatGPT, Claude, Perplexity, Google AI Overviews, Copilot | Claude Code, Codex, Cursor, Gemini CLI, Cline, and the rest |
| The input | A question typed by a person | A repository, plus a one-line request |
| The output | A paragraph, sometimes with citations | A diff: installed package, integration code |
| The metric | Mentions, position, share of voice, sentiment | Install share |
| Largest lever | Content on the open web, third party citations | What is already in the repository |
| How it is measured | Send prompts to chat models on a schedule, count brands | Run real agents on real tasks, read every session |
| Cost to measure | Low. It is API calls | High. Each session is minutes of real agent time |
| Who sells it | Profound, Scrunch, Peec, Otterly, Ahrefs, Semrush | Almost nobody |

## Why a GEO tool cannot measure this

Not because the tools are bad. Because the test they run does not contain the thing that decides the outcome.

A GEO measurement is a prompt sent to a chat model. What is missing from that prompt:

**The repository.** This is the single largest factor. We asked for a hosted database on JavaScript codebases 111 times, across three agents and two kinds of person. One product won 111 times out of 111. We ran the same request on TypeScript codebases and the same product won 48 out of 132.

Same category, same words, different file extension, different winner. There is no file extension in a chat prompt.

**The incumbent.** We put one sentence in a repository file saying to use a particular email product. The agent followed it 16 times out of 18. Then we installed a competing email library into the same repository. The same sentence lost two times out of three. What is already installed beats what is written down, and a chat prompt has nothing installed.

**The agent.** Claude Code, Codex and Cursor disagreed about the category leader in 9 of the 18 categories we measured. A chat model asked the same question is a fourth thing again, with different habits.

**The code.** The test ends when the model finishes writing. An agent has to make the code run. Products lose here for reasons a chat answer never surfaces: a quickstart that needs an unstated variable, an example using a removed interface, a package name that does not match the product name.

## Where they agree

The tactics overlap in one important region: what you publish.

Both surfaces reward clear, current, specific, extractable content. Both punish JavaScript-only rendering, gated content, vendor-written comparisons that win every row, and pages with no numbers in them.

So the documentation work pays twice. Write the quickstart so it runs, state the version, use one name for one thing, answer symptoms and not only categories, publish a Markdown twin. That helps a chat model describe you correctly and helps an agent integrate you correctly.

The overlap ends there. Everything about the repository has no equivalent in GEO, and it is the biggest lever.

## Which one you need

Ask two questions about your own users.

**Do developers ask assistants about your category before choosing?** If yes, GEO matters. Categories where people research before adopting: databases, authentication, observability, anything with a compliance dimension.

**Do coding agents install products in your category without a person comparing vendors?** If yes, agent discoverability matters more. Categories where this is common: anything a developer would describe as a chore. Email sending, file storage, bot protection, deployment, background jobs.

Most developer tool companies answer yes to both. The mix decides the budget.

One number changes that mix a lot:

| Agent | Ran a web search on decision tasks |
| --- | --- |
| Claude Code | 1.6% of runs |
| Codex | 53% of runs |

If your users are mostly on Claude Code, web content and citations reach almost none of the decisions in the moment they are made. Your leverage is the repository and what the model already learned. If they are on Codex, web content is live in half the sessions.

## Doing both, in order

1. **Fix the documentation.** Cheap, fully yours, helps both surfaces. Quickstart that runs, version stated, names matched, symptom pages, Markdown twins.
2. **Turn on GEO measurement.** It is inexpensive and it tells you what a person hears. Any of the prompt monitoring tools will do this.
3. **Measure install share.** Real agents, real repositories, hundreds of runs per category. Split by agent, language and persona, because the averages hide everything useful.
4. **Work the repository.** Templates, framework integrations, official examples, platform defaults. Slowest and largest.
5. **Re-measure monthly.** Both numbers move when agents and models update.

## A note on the word "just"

You will hear that agent discoverability is just GEO for developers. It is worth being precise about why that is wrong.

GEO's model is: publish good content, get cited, get mentioned. It is a content discipline.

Agent discoverability's largest lever is not content at all. It is presence in the codebase, which you reach through templates, integrations and defaults rather than through writing. The second largest is which agent is running, which you do not control and can only measure. Content is the fourth lever, not the first.

Same goal, different mechanism, different budget.

## Common questions

### What is the difference between agent discoverability and GEO?

Generative engine optimization measures whether an AI system mentions and cites your brand in an answer a person reads. Agent discoverability measures whether a coding agent installs your product into a codebase. The first produces a sentence. The second produces a lock file entry.

### Can a GEO tool measure agent discoverability?

No. GEO tools work by sending prompts to chat models and counting brand mentions. There is no repository in that test, no agent with its own habits, and no code at the end. The repository is the largest factor in the coding agent decision and it is absent from the measurement entirely.

### Do I need both?

If developers both ask assistants about your category and run coding agents that could install you, then yes. They are separate surfaces, measured separately, and one does not predict the other.

### Which one matters more for a developer tool?

It depends on how your users work. In our tests Claude Code searched the web in 1.6% of decision runs, so for those sessions web content and citations were almost irrelevant. Codex searched in 53%. Measure the mix before deciding where to spend.

### Is agent discoverability just GEO for developers?

No. The tactics overlap only partly. The largest lever in agent discoverability is what is already installed in the repository, which has no equivalent in GEO at all.

## Read next

- [Agent discoverability: the complete guide](https://armature.tech/library/agent-discoverability) (Markdown: https://armature.tech/library/agent-discoverability.md)
- [Generative engine optimization for developer tools](https://armature.tech/library/generative-engine-optimization-for-developer-tools) (Markdown: https://armature.tech/library/generative-engine-optimization-for-developer-tools.md)
- [The best AI visibility tools for developer tools in 2026](https://armature.tech/library/best-ai-visibility-tools-for-developer-tools) (Markdown: https://armature.tech/library/best-ai-visibility-tools-for-developer-tools.md)
- [AEO vs GEO vs SEO, and the fourth one nobody names](https://armature.tech/library/aeo-vs-geo-vs-seo) (Markdown: https://armature.tech/library/aeo-vs-geo-vs-seo.md)

---

Armature helps software products get discovered and used by coding agents.
Service: https://armature.tech/discoverability · Results: https://armature.tech/leaderboards/sectors · Contact: contact@armature.tech