If you're building AI agents, you've probably thought about what they do. But have you thought about whether other AI systems can find them?
Welcome to Generative Engine Optimization (GEO) — the practice of making your content, brand, and services discoverable not just by humans through Google, but by AI models like ChatGPT, Claude, Perplexity, and Gemini.
This isn't theoretical. It's already reshaping how information flows. And if you're building agents, ignoring GEO means building in a vacuum.
What Is GEO and Why Should Agent Developers Care?
Traditional SEO optimizes for search engine crawlers. GEO optimizes for large language models (LLMs). When someone asks ChatGPT "what's the best tool for X," the answer comes from training data, retrieval-augmented generation (RAG) pipelines, and web browsing capabilities.
If your agent or product isn't represented in the sources these models pull from, you're invisible. Not "page 10 of Google" invisible — completely nonexistent.
For agent developers, this matters doubly:
Your users increasingly discover tools through AI. Developers ask Claude for library recommendations. Product managers ask ChatGPT for tool comparisons. If your agent framework isn't in those answers, you lose.
Your agents themselves need to find services. As agentic systems become more autonomous, they'll query other AI systems to discover APIs, tools, and services. GEO determines what they find.
The GEO Playbook for Agent Developers
1. Structured Documentation Is Your Foundation
LLMs parse structured content far better than marketing fluff. Your agent's documentation should include:
- Clear capability descriptions using consistent terminology
- Structured data (JSON-LD, OpenAPI specs) that crawlers and models can parse
- Comparison tables that explicitly position your tool against alternatives
- FAQ sections that mirror how people actually ask questions
Don't write "our revolutionary AI-powered solution leverages cutting-edge..." Write "This agent processes invoices by extracting line items from PDF documents using OCR and structured output parsing."
2. Be Present Where Models Train and Retrieve
LLMs learn from the web. Their knowledge comes from:
- Technical blogs (Dev.to, Hashnode, Medium)
- Documentation sites (GitBook, ReadTheDocs)
- Forums (Stack Overflow, GitHub Discussions, Reddit)
- Academic-adjacent content (arXiv mentions, research blogs)
Publish technical content on multiple platforms. Each platform is a potential source for model training and retrieval.
3. Optimize for Conversational Queries
People don't ask AI "best invoice processing agent 2025." They ask "how do I automatically extract data from invoices in my workflow?"
Structure your content to answer natural language questions. Use headers that mirror conversational queries. Write content that directly answers "how do I..." and "what's the best way to..." questions.
4. Build Citation-Worthy Content
LLMs with web access (Perplexity, ChatGPT with browsing) cite sources. To get cited:
- Publish original research or benchmarks
- Create definitive guides on specific topics
- Include statistics, data points, and concrete examples
- Maintain freshness — update content regularly
5. Cross-Reference and Interlink
Models build entity understanding through co-occurrence. If your agent is mentioned alongside well-known tools, models learn the association.
- Write comparison posts ("X vs Y for Z use case")
- Contribute to ecosystem discussions
- Get mentioned in newsletters, podcasts, and roundups
Measuring GEO Success
Unlike SEO, GEO metrics are still emerging. But you can:
- Query LLMs directly about your product category and see if you appear
- Track citation appearances in Perplexity and ChatGPT browsing results
- Monitor brand mentions across platforms models use for retrieval
Tools like XanLens are emerging to help track AI visibility — how your brand appears (or doesn't) in LLM-generated responses. This kind of monitoring will become essential as GEO matures.
The Recursive Opportunity
Here's what makes GEO fascinating for agent developers: your agents can potentially optimize their own visibility. An agent that publishes its own documentation updates, responds to relevant forum questions, and maintains its own knowledge base is practicing automated GEO.
This isn't science fiction. Agents already:
- Auto-generate and publish changelogs
- Respond to GitHub issues with context-aware answers
- Update documentation based on user interactions
The next step is agents that strategically manage their own discoverability across the AI ecosystem.
Start Now, Not Later
GEO is where SEO was in 2005. The developers who invest now will have compounding advantages as AI-mediated discovery becomes the norm.
Three things to do this week:
- Ask ChatGPT and Claude about your product category. Note where you stand.
- Publish one technical article on Dev.to or Hashnode about your agent's capabilities.
- Add structured FAQ content to your documentation.
The agents that get found are the agents that get used. Make sure yours is one of them.
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