The March 2026 Google core update drove far higher ranking volatility than the December 2025 core update. Nearly 80% of top-three results shifted, and almost one in four top-10 pages fell out of the top 100, according to SE Ranking data shared exclusively with Search Engine Land.

The data. Volatility increased across every ranking tier.

• In the top 3, 79.5% of URLs changed positions, up from 66.8% in December. In the top 10, 90.7% shifted, compared to 83.1%.

• Stability dropped sharply. Only 20.5% of top 3 URLs held their exact position, down from 33.1%. In the top 10, that fell to 9.3%, from 16.9%.

• Churn intensified at the top. About 24.1% of pages ranking in the top 10 fell out of the top 100 entirely, versus 14.7% after the December update.

It’s (sort of) complicated. The March 2026 core update began rolling out a day after the March 2026 spam update completed. This complicated attribution, according to SE Ranking:

• Based on historical patterns and the scale of movement, most volatility was likely driven by the core update, with the spam update amplifying disruption.
• That overlap likely skews direct comparisons to December, though March still appeared more volatile.

More core update analysis. Meanwhile, independent analysis by Aleyda Solis, using Sistrix data from March 26 to April 11, found a consistent shift in where visibility concentrates. Rankings appeared to move from intermediary sites toward stronger destination sources. Website types gaining search visibility:

• Official and institutional.
• Specialist and niche.
• Established brands.
• Dominant platforms.

Losses were more common among aggregators, directories, and comparison-driven sites.

Winners and losers. Among the vertical shifts Solis highlighted:

• Dictionary and language reference sites declined, while larger reference platforms and major destinations gained visibility.
• Job aggregators like ZipRecruiter and Glassdoor lost ground, while employer sites and specialized platforms like USAJobs and Amazon.jobs surged.
• Government and institutional domains, including Census.gov and BLS.gov, saw strong gains on fact-driven queries.
• Travel and real estate visibility shifted away from broad discovery platforms toward stronger brands and primary destinations.
• Health results were re-sorted. Broad consumer health sites declined, while clinical, research-driven, and specialist sources gained.
• One exception: YouTube had the largest visibility loss in the dataset.

Why we care. The data suggests Google’s March 2026 core update raised the bar for ranking. Strong brands, owned data, and direct query value won. Intermediaries now look increasingly exposed.

Addy Osmani, a director of engineering at Google Cloud AI, published new guidance on Agentic Engine Optimization (AEO), a model for making content usable by AI agents.

He positioned this AEO (not to be confused with Answer Engine Optimization) as parallel to SEO, built for systems that fetch, parse, and act on content autonomously.

What he’s seeing. AI agents collapse multi-step browsing into a single request. They don’t scroll, click, or engage with UI — they extract what they need instantly. That makes most traditional engagement metrics irrelevant.

The token problem. Osmani highlighted token limits as a core constraint shaping content performance. Large pages can exceed an agent’s context window, causing:

• Truncated information.
• Skipped pages.
• Hallucinated outputs.

His takeaway: token count is now a primary optimization metric.

Content needs to change. Osmani recommended restructuring content for how agents read:

• Put answers early (ideally within the first ~500 tokens).
• Keep pages compact and focused.
• Avoid long preambles and buried insights. (Agents have “limited patience” for this, he noted.)

Markdown over HTML. He also recommended serving clean Markdown alongside traditional pages.

• Markdown reduces noise from navigation, scripts, and layout, making content easier and cheaper for agents to parse.
• This includes making .md versions directly accessible and discoverable.

Discovery and structure. Osmani pointed to emerging patterns for helping agents find and use content:

• llms.txt as a structured index of documentation.
• skill.md files to define capabilities.
• AGENTS.md as a machine-readable entry point for codebases.

These act as shortcuts for agents deciding what to read and use.

Why we care. This adds a new optimization layer alongside SEO. If agents can’t efficiently parse your content — due to token limits, structure, or format — they may skip, truncate, or misinterpret it. That directly affects whether your content is used, cited, or acted on in AI-powered experiences.

Between the lines. To be clear, the type of AEO Osmani discussed in his article is unrelated to Google Search or organic search ranking. Of note, Google’s John Mueller recommended against markdown pages and Google doesn’t use the llms.txt file.

• Osmani’s article highlights how AI systems interact with the web and what “optimized” content may look like in that environment.
• AEO shifts the goal from driving visits to enabling successful outcomes inside AI workflows.

The article. Agentic Engine Optimization (AEO)

Dig deeper:

• Meet llms.txt, a proposed standard for AI website content crawling
• llms.txt isn’t robots.txt: It’s a treasure map for AI
• No, llms.txt is not the ‘new meta keywords’
• Why LLM-only pages aren’t the answer to AI search
• Google’s John Mueller on SEO vs. GEO: Focus on audience behavior