How AI agents will decide which brands get found

Search is evolving in real time. A query that used to return a list of links now shows an AI-generated answer. Sometimes with your competitor’s name in it. 

Then users can ask follow-up questions — comparing options, weighing reviews, narrowing choices — all inside the search experience, before anyone visits your website.

And the AI that delivers that experience is getting better at its job. (It’ll never be worse). It’s researching across sources, cross-referencing what you claim against what community forums and industry articles say about you, and, in some cases, taking action on the user’s behalf. This is agentic search.

Here’s a real example — someone comparing offsite venues in Austin:

The answer included three venues. But the agent evaluated a dozen. The ones that didn’t make the cut were compared on the same and filtered out before the user knew they existed.

That filtering is accelerating. Agentic web traffic grew 1,300% in the first eight months of 2025, and Google’s SAGE research found that AI agents take an average of 4.9 steps per query — searching, comparing, and evaluating across multiple sources before delivering a result.

The sophistication of this behavior varies. Sometimes the agent summarizes. Sometimes it plans a full itinerary. Sometimes it books the table. And at every level, the agent is making decisions about which brands to include, how to represent them, and whether to recommend them.

If you’re responsible for SEO, this is the shift you need to understand. 

Further reading: What Is an AI Agent? (And What AI Agents Mean for Your Brand’s Visibility)

What is agentic search?

Agentic search is AI that retrieves, evaluates, and acts on information on behalf of users. It’s the layer of AI search where the machine doesn’t wait for you to click through results. It researches, compares, and increasingly takes action — booking, purchasing, planning — iterating across multiple sources and steps until it reaches a result.

The difference comes down to what the AI can do with your request. A search engine retrieves what you ask for. A chatbot generates an answer. An agent breaks your goal into steps, uses external tools and live websites to gather information, and adapts when something changes or a source contradicts another. It doesn’t just respond — it works through a problem.

What changes for brands is where the evaluation happens. 

In traditional search, a person visits your site and makes a judgment. 

In AI search, the AI composes an answer that may or may not include you. 

In agentic search, the AI researches you across multiple sources, compares you against competitors, and may take action — all before a human is involved. The further along that progression, the more dimensions of your brand the agent is testing.

As agents take on more complex tasks, they test different dimensions of your brand — whether they can find you, understand you correctly, validate you through independent sources, and trust you enough to act. Each of those dimensions answers a different question about your brand’s readiness for AI search, and different situations test different ones.

To see how this works, it helps to watch what happens as AI agents take on increasingly complex tasks.

Agentic search in practice

We’ll follow one scenario — planning an Austin team offsite — as the agent’s behavior escalates from a simple question to full delegation.

In each situation, the agent is testing different dimensions of your brand — whether it can find you, understand you correctly, validate you through independent sources, and trust you enough to recommend or act on you.

As agent behavior grows more complex, more layers become critical. A simple query tests Discovery. A comparison tests Clarity and Authority. A research brief tests all three, plus Trust. Delegated action makes Trust the decisive threshold.

Understanding these relationships is crucial for building your AI visibility. 

We’ll walk through each situation.

Simple Query

The user asks a question. The agent answers it.

The prompt:

“What are the best off-site venues in Austin for a marketing team of 15?”

The agent pulls from its training data and retrieves sources. It judges which sources are credible. Then it composes a single response with recommendations.

This is what most AI search looks like right now. A Google AI Overview. A ChatGPT answer. The machine evaluates on your behalf. You read the answer and decide what to do next.

What becomes decisive: Brand Discovery.

If the agent isn’t pulling your content into its research, you’re not in the answer. Page-level authority, relevance signals, structured data, and technical health all heavily influence whether the agent even considers you.

If you’re an Austin venue and your site doesn’t clearly describe your event space, capacity, and pricing in a way agents can parse, you’re invisible at this layer.

Comparison Request

The user wants a judgment call. The agent evaluates options.

The prompt:

“Compare these three Austin venues for a 15-person marketing offsite. Which one should I choose based on pricing under $8K, availability in April, team-building activities, and guest reviews?”

Now the agent cross-references multiple source types: your website, review platforms like Google Reviews and Yelp, event planning sites, and third-party recommendation articles. It weighs signals across sources. It ranks options and makes a recommendation.

This is where things get interesting. The agent isn’t just retrieving your content — it’s judging you against competitors using information from sources you may not control.

What becomes decisive: Brand Clarity + Brand Authority (with Discovery as table stakes).

Discovery got you into the comparison. Now, two things influence whether the comparison favors you.

Brand Clarity is whether the agent can build a coherent picture of what you offer. Agents pull from multiple sources to make their comparisons. Your site is one source, but so are reviews, comparison articles, and third-party directories. When these sources agree, agents get a clearer picture and can represent you more accurately. When they disagree, the picture gets muddier.

Brand Authority is whether independent sources validate your claims. Clarity is about how you present yourself. Authority is about what everyone else says. If review platforms, expert articles, and industry directories consistently mention you alongside relevant competitors, you’re treated as a legitimate option. If you’re absent from those conversations, the agent has less reason to include you.

Both matter in this scenario. Clarity without Authority means you’re well-described but unverified. Authority without Clarity means you’re well-known but poorly represented.

Research Brief

The user delegates research. The agent builds a strategy.

The prompt:

“I’m planning a two-day marketing offsite in Austin for 15 people, budget under $8K. Research venue options with breakout rooms and outdoor space, find nearby hotels with group rates, identify three team dinner restaurants (one BBQ, one Tex-Mex, one upscale), and build me a full itinerary with cost estimates.”

This is a multi-step research workflow. The agent browses multiple sites. Cross-references availability, group rates, and menus. Evaluates logistics like proximity between venues and hotels. Makes judgment calls at each step: which venues to shortlist, how to weigh cost against experience, what “best” means given the constraints. It delivers a structured plan.

This kind of multi-step planning is already happening across AI platforms. Deep research features in ChatGPT, Gemini, and Perplexity are one example. The agent takes minutes, not seconds, visiting dozens of sources to build a comprehensive output. 

But planning behavior shows up anytime an AI breaks a complex goal into sub-tasks and works through them: a coding agent mapping an implementation, a project tool sequencing dependencies, or a search agent building the kind of itinerary described above. You review the output, but you didn’t do any of the evaluating.

What becomes decisive: Brand Clarity + Brand Authority + Brand Trust (with Discovery as table stakes).

Clarity and Authority keep you represented correctly and treated as a legitimate option — that work is still running from the previous layer. What we believe tips the recommendation at this level is Brand Trust.

The agent is making a chain of judgment calls. At each step, it decides whether to include you, how to represent you, and whether your claims are credible enough to shape a plan around. 

Google’s SAGE research confirms that agents evaluate across dozens of sources — encountering a mix of first-party and third-party information about your brand. 

Over time, we expect trust signals (reviews, forums, expert endorsements, press coverage) to carry increasing weight in those decisions. The pattern mirrors how humans already evaluate brands, and agents are being trained on human judgment.

Delegated Action

The user delegates execution. The agent follows through.

The prompt:

“Book the offsite. Reserve the venue for April 12-13, block 10 hotel rooms at the group rate, book the BBQ restaurant for 15 on Friday night at 7pm, and send calendar invites to the team.”

The agent goes beyond recommending — it starts executing. Handling the legwork of booking, purchasing, and coordinating, with a human confirming the final step.

Most delegated action right now is a hybrid: The agent does the research, navigates the booking flow, pre-fills the forms, and stages the transaction. You provide the final confirmation. Think of it as a one-tap finish — the agent brings you to the finish line, you tap “Confirm.”

That hybrid is already live in specific contexts:

• Google AI Mode finds real-time availability and links users directly to pre-filled booking pages for restaurants and events. The user still clicks “Confirm” on the partner site.
• ChatGPT agent navigates websites, fills out forms, and stages bookings — with user approval for payment authorization
• Perplexity Buy with Pro enables one-click checkout via PayPal for supported merchants — one of the closest examples to fully autonomous purchasing
• Shopify Agentic Storefronts make millions of merchants’ products discoverable across ChatGPT, Microsoft Copilot, Google AI Mode, and Google Gemini. Users complete purchases via an in-app browser on mobile or are linked to the merchant’s store on desktop — the agent surfaces and stages, the human confirms.

The infrastructure for fully autonomous execution is being built through protocols like Universal Commerce Protocol (UCP) and Model Context Protocol MCP. Visa’s Trusted Agent Protocol and Mastercard’s Agent Pay are building the trust layer. It’s a verification process that confirms an agent is acting on behalf of a real, authorized user. 

The gap between “stages the transaction” and “completes the transaction” is closing. But as of March 2026, most delegated interactions still involve a human in the final step.

Read more: WebMCP: What It Is, Why It Matters, and What to Do Now

What becomes decisive: Brand Trust (with Discovery, Clarity, and Authority as prerequisites).

Everything from the previous scenarios still applies. Discovery gets you found. Clarity gets you represented correctly. Authority earns the consideration. But at this level of complexity, the agent is committing real resources on the user’s behalf — money, time, access, and reputation. The threshold for trust is higher because the consequences of a wrong choice are immediate and tangible.

The pillar doesn’t change from the Research Brief — trust is still decisive. But the stakes of trust do. At the research level, a bad recommendation wastes the user’s time. At the action level, it wastes their money.

Think about what it takes for you to hand your credit card to a concierge you’ve never met. You’d want to know the restaurant has strong reviews, that the hotel is reputable, and that the venue has been independently validated. The agent is that concierge — and it’s running the same calculus, pulling from the same signals. Reviews, sentiment, cross-source corroboration, and track record are what give it enough confidence to act.

The technical infrastructure matters, too — online booking flows, structured data, machine-readable availability. 

If the agent can’t complete the transaction, it may move to the next option it can work with. But that infrastructure is becoming table stakes. What separates the brands that win from the ones that get skipped isn’t whether the agent can book you. It’s whether it will.

What agentic search means for your brand

Most brands are already being evaluated when someone asks an AI a question or runs a comparison. Multi-step research behavior is emerging. Fully delegated action is the frontier. You don’t need to solve for all of these today.

The dimensions that showed up throughout this article — Discovery, Clarity, Authority, and Trust — are the pillars of Brand Visibility. They’re not a sequential checklist. They’re a diagnostic framework: Each pillar answers a different question about your brand’s readiness for AI search, and different teams own the fix for each one.

If you’re wondering where to start, here’s a quick reference.

Discovery gets you found. Clarity gets you understood. Authority gets you considered. Trust gets you chosen.

This might feel new, but the underlying disciplines aren’t. 

SEO (authority, structured content, technical health, entity clarity) is the foundation that runs across every layer. It’s what gets you found and keeps you accurately represented.

Agentic Search Optimization (ASO) extends those foundations into the dimensions where agents evaluate and act on your behalf. It brings brand accuracy, trust signals, and agent readiness into the same discipline — and it requires work that goes beyond the content team. Product marketing, brand, reputation, PR, and customer experience all play a role.

The outcome across all four layers is Brand Visibility — how often and how accurately your brand is found, understood, trusted, and acted on, whether the one doing the finding is a person or an agent.

Brand Visibility isn’t binary. You might be discoverable but invisible at the comparison level because your entity data is inconsistent. You might have strong authority but lose at delegated action because your booking flow isn’t agent-accessible. The pillars give you a way to diagnose where you’re strong, where you’re exposed, and where to invest next.

When all of these pieces come together — Discovery, Clarity, Authority, and Trust — that’s when agentic search becomes a competitive advantage instead of a risk.

FAQ

Do I need to change my entire SEO strategy? 

No. Authority, structured content, entity clarity, and technical health become more important in agentic search, not less. These are the signals AI agents use to decide which brands to retrieve, compare, and recommend. 

What changes is the emphasis: You’re optimizing for machine evaluators alongside human ones. Marketers are starting to call this expanded discipline Agentic Search Optimization (ASO) — it builds on the SEO foundations you already have and extends them into areas like brand accuracy across third-party sources and agent readiness for AI-mediated transactions.

How do I know if agents are already evaluating my brand? 

Check your server logs for AI-specific user agents — GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, and Google-Extended are the most common. These crawlers indicate that AI platforms are accessing your content for potential use in AI-generated answers. 

Semrush’s Log File Analyzer lets you see exactly which bots are crawling your site, how often, and which pages they’re hitting. In the example above, GPTBot and OAI-SearchBot are both active — a signal that OpenAI is accessing this site’s content. Filtering by bot type gives you a clear picture of your AI agent traffic alongside traditional crawlers like Googlebot.

What’s the biggest risk of agentic search for brands? 

Being filtered out before a human ever sees you. In agentic search, AI agents evaluate your brand on behalf of users — comparing your pricing, reviews, and positioning against competitors using information from sources you may not control. 

If your information is inconsistent, outdated, or missing from the sources agents check, you can be excluded from recommendations without the user ever knowing you existed. The evaluation happens before the human arrives.

What’s the difference between agentic search and AI search? 

AI search is the broader category — the entire ecosystem where AI shapes how people and machines find, compare, and decide. It includes everything from AI-powered ranking algorithms to AI-generated answers in Google AI Overviews and ChatGPT. 

Agentic search is a subset of AI search where the AI goes further: It retrieves information, evaluates options, and increasingly takes action on behalf of users — booking, purchasing, planning. All agentic search is AI search. Not all AI search is agentic.