Incomplete terminology leads to incomplete strategy. Here’s a clearer framework for optimizing when AI systems recommend and act.

Search engine optimization (SEO) — be found. Answer engine optimization (AEO) — be the answer. AI engine optimization (AIEO) — be the recommendation. Assistive agent optimization (AAO) — be chosen when no human is in the loop. Four stages where each clearly absorbs the last.

The word that stays constant across the last two is “assistive,” and that’s important because it names the purpose: what the system does for the user. The word that changes is just one: engine becomes agent — a single pivot that tracks the real shift in our industry, from systems that recommend to systems that act.

For me, everything else in the naming debate is a distraction. The SEO industry is fractured across at least six competing terms for what’s functionally the same discipline. Each term has a constituency, each constituency is spending energy defending its label, and while we argue about what to call the work, we’re not doing the work.

So skip a step with me: I’ll explain in the next few paragraphs why AAO is a good solution — then we can all get back to our jobs.

Every competing acronym covers part of the job, none covers all of it

Every AI system that makes recommendations or takes autonomous action — Google, Bing, ChatGPT, Perplexity, Copilot, and any other engine that glides into view — runs on three components: large language models, knowledge graphs, and traditional search. I call this the algorithmic trinity. 

The balance differs by platform (ChatGPT leans LLM-heavy, Google leans on its knowledge graph), but the trinity itself is universal. Even Google team members I’ve spoken with agree on this architecture.

SEO also described the purpose the engine served, which I’ve always liked. So here’s a quick look at the competing acronyms against those three components.

• GEO describes mechanism, not purpose. It covers the LLM layer, includes search by necessity, but misses the knowledge graph entirely. Because “generative” is a technology label, the term expires when the technology evolves. “Generative agent optimization” describes nothing, which tells you the term wasn’t built to scale.
• Entity SEO covers the knowledge graph layer (entities live there), treats search as the delivery mechanism, and tangentially acknowledges LLMs. The term also fails the glossary test, which I now try my best to apply to my own writing. If a non-specialist can’t understand a term on first encounter, it was named for the speaker, not the listener. Every time I use the word “entity” to describe “brand” in conversations with business leaders, I have to explain myself.
• LLM optimization is honest about its scope, but that’s one-third of the job, ignoring the knowledge graph and search entirely.
• AI SEO bolts “AI” onto the old term, which makes it easy access for outsiders, but it doesn’t have long-term legs. Already in 2026, people aren’t searching, they’re researching, and some have agents researching for them.

All of them are incomplete, and I’d argue that incomplete terminology produces incomplete strategy because practitioners naturally optimize for the leg their acronym covers and neglect the others.

Assistive agent optimization (AAO) evolves neatly from answer engine optimization and covers everything we need to build a meaningful, complete strategy: 

• “Assistive” names the purpose across the full algorithmic trinity. 
• “Agent” names the actor that uses all three components to make a decision. 
• “Optimization” is what we do. 

That’s a three-legged stool with all three legs the same length, which, if you’ve ever sat on one, is the only stool that doesn’t wobble.

The glossary test says AAO isn’t perfect, but it’s the closest we’ve got

Generative engine optimization requires the listener to know what a generative engine is, entity SEO requires them to know what an entity means in a technical context, and LLM optimization requires them to know what an LLM is — all three fail the glossary test.

Assistive agent optimization doesn’t pass perfectly either because “assistive” requires half a second to process. But “agent” is mainstream vocabulary now (every tech company on earth is selling us agents), and “optimization” is self-explanatory. Two out of three words land with zero friction, and the third doesn’t need explaining after half a second’s thought.

If you have a better term that covers the full algorithmic trinity — pull and push (see below) — and passes the glossary test more cleanly, I’m open, because the discipline matters more than the term.

More importantly, AAO describes a role (optimize so the assistive agent chooses your brand), not a technology, and roles outlast technologies. The term that names what you do is the one you’ll still be using in five years, regardless of which model architecture or retrieval method is fashionable.

Here’s what changes when you adopt the AAO frame

Your brand identity becomes the foundation, not a nice-to-have. When an agent books a hotel, selects a supplier, or recommends a consultant, it doesn’t scan a list of pages and pick the one with the best title tag. It evaluates what it knows about the brand itself: who this company is, what it does, who it serves, why it would be a reliable solution, and how confident the agent is in those facts. 

That confidence starts at the entity home — the one page you control that anchors everything the algorithmic trinity knows about you — and cascades outward through every corroborating source. If the agent doesn’t understand your brand clearly, it will pick a brand it does confidently understand.

The funnel moves inside the agent. The traditional acquisition funnel (awareness, consideration, decision) used to happen with a bouncing on-and-off-your-website dance, where the search engine was one traffic source that sent people to you. 

Under AAO, the entire funnel happens inside the AI, without the user ever seeing a list of options. The agent becomes aware of you, considers you against alternatives, and decides — all before delivering the result. Your role is no longer to attract visitors to a funnel on your site, it’s to be the answer when the agent runs its own funnel internally.

You might be thinking, “We’re not there yet.” You’re right. We’re not, for most people.

But the funnel is already in the assistive engine (ChatGPT, Perplexity, Google AI Mode), and they bring people to the perfect click — the zero-sum moment in AI where they present one single solution to the user. Most people take the solution they’re offered. The only thing missing is the agent clicking the buy button.

The web index is losing its monopoly as the source of truth. For two decades, the crawled web was effectively the only dataset that mattered: if Google hadn’t indexed it, it didn’t exist. That monopoly is breaking on two fronts. 

• Proprietary datasets are feeding agents directly as search evolves into what I’d call ambient research, where in-app push recommendations surface your brand inside the tools people are already using, without anyone typing a query. 
• Agents and engines already pull from APIs, booking systems, internal databases, and structured feeds that never touch a traditional web index. The web index doesn’t disappear (your website is still the entity home — the anchor), but it’s no longer the sole gatekeeper, and you should already be building your strategy on that basis.

The push layer is back, too. For 20 years, we got lazy: Google and Bing crawled our sites, rendered our JavaScript, figured out what our pages meant even when we made it hard, and we published and waited. That will continue, but you’ll need to account for multiple additions. 

IndexNow (Fabrice Canel has been building this at Bing for years), MCP, and whatever Google eventually ships all do the same thing: they let you push structured information to the systems that act, rather than waiting for those systems to come and find it. It’s the 1990s again — submitting URLs and actively feeding the ecosystem. 

My guess on why Google hasn’t adopted IndexNow isn’t because it’s a bad idea — it’s a brilliant idea — but because it wasn’t Google’s idea, and Google would rather ship a proprietary version. 

The technical generosity we’d been leaning on comes back to bite us, too: JavaScript rendering was a favor Google extended, not a standard the industry can rely on, because most AI agent bots don’t render JavaScript. If your content sits behind client-side rendering, a growing number of agents simply never see it.

(All of this maps to the 10-gate DSCRI-ARGDW pipeline I’ll lay out next in this series.)

Your SEO skills still apply. The target moves from the engine to the agent.

You don’t need to master every intermediate stage before adopting the AAO frame, because AAO contains AIEO contains AEO contains SEO — the skills stack — and only the target changes: be chosen when the agent acts, recommended when the user researches, and mentioned when the user asks.

The compounding advantage I documented in “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it” also applies here. The top performers in our data captured 59.5% of all citability by February, up from 30.9% in December — a 293% increase in concentration over two months. 

People who adopt this frame will be able to reliably build pipeline confidence while everyone else argues about acronyms — and the gap will widen over time.

The discipline has a name, the agents are already acting, the push layer is here, and the lazy days are over.The first two articles were the “what” and the “why.” Next week, the how begins. I’ll open up the 10-gate pipeline I’ve been referencing, DSCRI-ARGDW, which stands between your content and a conversion from an AI engine.