Multi-agent SEO: putting four models on one job

One model doing everything is simple to build and mediocre at everything. The moment I split the work across specialised agents, quality jumped. Seowyn is the result: a platform where a team of agents audits a site, researches competitors, and hands back a ninety day growth plan with ready-to-use deliverables.

Why more than one model

Different models are genuinely good at different things, and pretending otherwise leaves quality on the table.

• One model is a strong, structured researcher.
• Another writes with a warmer, more human voice.
• A third is cheap and fast, perfect for high-volume classification.
• A fourth is excellent at strict, schema-bound output.

Routing each step to the model that fits it is the whole game.

The orchestration layer

A coordinator breaks a job into tasks and dispatches each to the agent best suited for it. The heavy, long-running work runs on background workers with Celery and Redis, so the interface stays responsive while a full audit runs for minutes.

ROUTES = {    "keyword_research": "reasoning-heavy",    "draft_article":    "warm-writer",    "classify_intent":  "fast-cheap",    "structured_audit": "schema-strict",}def route(task):    return ROUTES.get(task.kind, "default")

Each agent returns structured output, the coordinator stitches the pieces together, and the user watches real-time progress instead of a spinner.

Guardrails matter more than cleverness

With several models in play, the failure modes multiply. So every agent's output is validated against a schema before it moves downstream. If an agent returns something malformed, it is retried or routed elsewhere, and the run keeps going.

A multi-agent system is only as trustworthy as its weakest validation step. Spend your time there.

The takeaway

Multi-agent is not about stacking models for show. It is about matching each unit of work to the tool that does it best, then being strict about what each one is allowed to hand off. Do that, and a pipeline of ordinary steps produces a result no single model could.