Marketing in a world where agents — not browsers — are the discovery layer.
AI agents are increasingly the interface between users and products — doing research, evaluating options, and sometimes purchasing on behalf of users. This course covers how to make products discoverable and trustworthy to agents: answer engine optimization, structured data, agent-callable product design, prompt-native copywriting, and go-to-market strategy for a world where agents handle discovery. All techniques are platform-agnostic — no vendor lock-in.
Built by Lakshya Kumar
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Audit your product's current agent-readiness: test visibility across 5 LLMs, validate structured data implementation, review your OpenAPI/MCP spec (or absence thereof), and score your content for extractability. Then produce an agent-era GTM plan that addresses the gaps — including answer engine optimization plan, structured data roadmap, content rewrite priorities, and a measurement framework using proxy metrics for agent-driven traffic.
I am learning agentic marketing — how AI agents discover, evaluate, and interact with products, and how to make a product visible and trustworthy to agents through answer engine optimization, structured data, agent-callable product design, and agent-era GTM strategy. Help me understand how agents work and how to optimize for them.
Build an LLM-powered outbound agent: researches a target company, identifies a hook, drafts a personalized email, and slots it into a CRM. Test on 20 real targets; evaluate quality (manual scoring), reply rate, and cost per personalized email.
Build a pipeline that generates SEO content at scale: outline from keyword, draft via LLM, fact-check via retrieval, edit via second LLM, human review at quality threshold. Process 20 articles; measure publishable rate and cost per word.
Build an AI SDR: handles inbound lead qualification (BANT-style), schedules meetings, and hands off qualified leads to a human. Test with 50 simulated inbound chats; measure qualification accuracy and human override rate.
Build an evaluation harness for marketing agents: scenario library (50+ situations), automated scoring (LLM-as-judge for quality, programmatic for correctness), and a comparison report across 3 candidate prompts/models. Use it to make a real model-selection decision.
The machine-readable API description standard that makes products agent-callable.