Ahrefs published a comprehensive guide on June 15 defining agentic marketing as a distinct operational category and revealing Agent A, the company’s own AI marketing agent, according to the Ahrefs blog. The guide, authored by marketing researcher Mateusz Makosiewicz and reviewed by VP of Marketing Ryan Law, draws a clear line between prompt-based AI assistance and agent-driven execution where systems take a goal, plan the workflow, run tools autonomously, validate output, and iterate without human intervention at each step.
What Agent A Does
Agent A has unrestricted access to every Ahrefs endpoint, including internal endpoints not available through the public API or MCP interface, per the guide. The platform runs on Postgres for state management, Flask for UIs, and an OpenRouter proxy supporting 300+ models. It connects natively to Slack, HubSpot, GitHub, Notion, Linear, Mailchimp, Resend, SendGrid, Stripe, Gong, WordPress, Airtable, Apify, and Semrush.
Ahrefs tested the platform through a week-long internal hackathon. The content team built 16 functional marketing applications using Agent A without writing any code manually. The guide uses this as evidence that agentic marketing has crossed the threshold from experimental to operational.
The Market Context
Search volume for “agentic AI” in the US grew from 1,450 monthly searches in May 2023 to 122,175 in May 2026, an 84x increase over three years, according to Ahrefs’ own Keywords Explorer data. Approximately 6,000 YouTube videos mentioning agentic AI were published in the three months prior to the guide’s publication, generating roughly 83 million impressions.
LLM inference costs have dropped 15 to 20x since early 2024, per the guide, which positions cost reduction as the enabler that moved agent-driven marketing from theoretical to practical. The guide cites specific workflow examples: an agent preparing data, graphs, and recommendations for content entirely autonomously, with the human reviewing a finished package rather than managing a checklist.
The Agent Lens
The guide categorizes agent types as open-source (citing OpenClaw and Hermes) versus closed-source, and describes skill library concepts where marketing teams encode their actual working processes as reusable agent capabilities. This mirrors the pattern emerging in developer tooling, where agent skills are treated as composable, shareable units of work rather than one-off prompts.
For marketing teams evaluating agentic tools, the Ahrefs guide is the first published operational playbook from a major SaaS company that treats agent-driven marketing as a production workflow rather than a demo.