Agentic SEO With Agent Teams: Prompt, Results, Token Bill

I spawned a team of AI researchers to audit my blog's SEO. One report later, I created a landing page that hit rank 1 and 6,000 daily views. It also burned 5x the tokens of a normal Claude Code session. Here's the full agentic SEO setup—and whether it's worth it.

What Agent Teams Actually Are

Claude Code can spawn sub-agents—focused workers that handle a task and report back. Agent Teams take this further. Instead of one agent doing everything sequentially, you create a team of specialists that work in parallel, each with their own context window.

The key difference: sub-agents report to a parent. Agent team members can share findings with each other, challenge each other's analysis, and coordinate independently. For agentic SEO, this means one researcher analyzes your content quality while another digs into your GSC data while a third checks search intent alignment—all at the same time.

The team lead collects everything and compiles one final report. You get a comprehensive AI SEO audit in minutes that would take hours manually.

If you haven't seen my agentic SEO stack breakdown—that covers the foundation. This article goes deeper into Agent Teams specifically.

The Setup

Agent Teams are experimental. You need to enable them first:

export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1

You also need Claude Code running with full permissions so the team members can access your files, APIs, and database without asking for confirmation every operation.

Make sure your environment variables include access to Google Search Console and your CMS (Supabase, WordPress, whatever you're running). Every teammate inherits your project's CLAUDE.md and MCP server configurations automatically. I covered how to set up Claude Code as your full content architecture in a previous article.

The Prompt

Here's the agentic SEO prompt structure that works. Adapt the specifics to your stack:

Create a team of SEO researchers to audit my blog. The team structure:

Team Members:

1. Content Quality Analyst — Review all published articles in my Supabase 
   database. Check for: thin content, missing meta descriptions, titles not 
   matching search intent, internal linking gaps, missing FAQ sections. 
   Flag every issue with severity (critical/medium/low).

2. GSC Performance Researcher — Pull Google Search Console data. 
   Identify: pages with high impressions but low CTR (bleeding results), 
   queries where I rank 5-15 (striking distance keywords), queries where 
   I appear but have no dedicated page (content gaps), declining pages 
   that need updating.

3. Search Intent Analyst — For every high-impression query from GSC, 
   verify that my content matches what Google actually wants to show. 
   Check: is Google showing guides when I wrote news articles? Am I 
   targeting informational intent when the query is commercial? Flag 
   every intent mismatch.

4. Competitor Gap Researcher — For my top 10 performing queries, 
   check what's ranking above me. Identify: topics they cover that I 
   don't, structural advantages (tables, FAQs, diagrams), content depth 
   differences.

Team Lead: SEO Research Director — Collect all findings from team 
members. Compile into one prioritized report with:
- Critical issues (fix today)
- Quick wins (fix this week)  
- Content gaps (new pages to create)
- Strategic opportunities (long-term)

For each item, include the specific data point, the recommended action, 
and the expected impact.

The logic is straightforward: each AI SEO specialist does their job in parallel, the team lead synthesizes. You don't micromanage the team—you define the roles and the output format.

What the Report Looks Like

The team lead delivers a structured report. Not vague suggestions—specific, data-backed action items:

Critical (fix today):

• Article X: 4,800 impressions for "keyword Y" but content is a news article. Google wants a setup guide. Rewrite as guide or create dedicated landing page.
• Meta title on page Z doesn't contain primary keyword in first 30 characters. Rewrite title.

Quick Wins (this week):

• 3 pages ranking position 7-12 for high-volume queries. Add FAQ sections, improve internal linking.
• 2 articles missing meta descriptions entirely. Write and push to Supabase.

Content Gaps (new pages):

• Query cluster around "keyword A + keyword B" — 2,400 combined impressions, no dedicated page. Create landing page covering all related intents.

Strategic:

• Competitor X has comparison tables on their top pages. My equivalent pages don't. Add comparison tables to top 5 articles.

That's the output. One report. Every issue mapped to data. This is what separates agentic SEO from manual SEO—the agent doesn't just find problems, it maps each problem to a specific action.

What Happened Next

The report flagged something I hadn't seen: my blog was showing up for a cluster of keywords where the search intent didn't match my existing content. Google was literally telling me "I would rank you for this if you just created the right page."

So I created the landing page. Covered all the keyword variants and search intents the team identified. Structured it properly—FAQ, comparison table, clear sections matching each intent.

Next day: rank 1. 6,000 views. On one page.

That's not the agent being magic. That's AI SEO reading data I had access to but wasn't analyzing systematically. The parallel team structure meant nothing was missed—content quality, GSC data, intent matching, and competitor analysis all happened simultaneously.

After the report, I spent the rest of the day in regular Claude Code sessions fixing every issue the team flagged. New meta titles, intent-matched rewrites, internal linking fixes. Each fix took minutes because the report told me exactly what was wrong and what to do.

The Token Bill: Let's Be Honest

Here's what nobody tells you about Agent Teams: each teammate is a separate Claude instance with its own context window. Four researchers plus a team lead means five parallel Claude sessions consuming tokens simultaneously.

I ran this five times to stress test the system. The token usage was roughly 5x a normal Claude Code session. On a Pro plan, this eats into your usage limits fast. On the API, you're looking at meaningful cost per run.

Is it worth it? Depends on what you're doing.

Worth the tokens:

• Monthly or bi-weekly comprehensive agentic SEO audit. One deep run, then execute the findings for two weeks in regular sessions.
• Before a major content push. Run the team once, get the full picture, then build.
• When you're stuck. If your growth has plateaued and you can't see why, the parallel analysis catches things you miss.

Not worth the tokens:

• Daily usage. Run this every day and you'll burn through your plan allocation in a week.
• Simple optimization tasks. If you just need meta titles rewritten, a regular Claude Code session is 5x cheaper.
• Small blogs with fewer than 20 pages. Not enough data for parallel analysis to add value over a single session.

The sweet spot: run Agent Teams once for the big-picture AI SEO audit. Then use regular Claude Code sessions daily to execute on the findings. That's the cost-efficient workflow.

Agent Teams vs Sub-Agents vs Single Session

Quick decision framework:

Sub-agents are focused workers that report back to you. Agent Teams are specialists that debate with each other. If you need a meta title rewritten, use a single session. If you need your entire agentic SEO strategy analyzed from four angles simultaneously, use Agent Teams.

The Workflow: Putting It Together

Here's the practical rhythm I recommend:

Monthly (Agent Teams — high token cost, high value): Run the full AI SEO research team audit. Get the comprehensive report. Identify all critical issues, content gaps, and strategic opportunities.

Weekly (Regular Claude Code — normal token cost): Execute on the report's findings. Create new pages for content gaps. Fix intent mismatches. Update meta titles and descriptions. Optimize internal linking.

Daily (Regular Claude Code — minimal token cost): Quick GSC check for new opportunities. Write and publish new content. Monitor recent changes.

This gives you the depth of Agent Teams without the token cost of running it constantly. The monthly audit sets the direction. Daily execution compounds the results.

The Reality Check

Three things to know before you start.

Agent Teams are experimental. The feature is in research preview. Session resumption, coordination, and shutdown behavior have known limitations. Don't rely on it for production workflows without testing. It works—but it's not polished yet. I wrote about the full Agent Teams setup and parallel workflows separately.

Your knowledge files matter more than the team structure. If your CLAUDE.md, database schema docs, and project context are weak, the team produces generic output. The agent team is only as good as the context you give it.

The 6k views result isn't guaranteed. I got that result because the data was there—Google was already showing me for those queries. The agent team helped me see what I was missing. If your blog has no existing impressions, Agent Teams won't create demand that doesn't exist. It optimizes what you have.

The Verdict

Claude Code Agent Teams for agentic SEO is a power tool, not a daily driver.

Run it monthly for the comprehensive AI SEO audit. Execute daily in regular Claude Code. That's the cost-efficient split. One Agent Teams run costs 5x the tokens but generates a report that drives weeks of optimized work.

The prompt is simple: define specialists, define the team lead, define the output format. The value is in parallel analysis that catches what sequential thinking misses. My rank 1 page with 6,000 daily views came from a data point I had access to for weeks—but the Agent Team was the first thing that actually found it.

Start with regular Claude Code for daily SEO. Graduate to Agent Teams when you need the full picture. Just watch your token bill.