What is Agentic Search?
Agentic search is the emerging class of AI systems — ChatGPT with browsing/agents, Perplexity Pro, Claude with computer use, Google's Deep Research — that decompose a buyer's high-level question into a plan, run multiple searches, compare sources across steps, and return a synthesized recommendation (often a ranked shortlist) with inline citations. It compresses what used to be a week of B2B evaluation into a single prompt, and shifts the point of decision upstream: by the time the buyer opens a browser tab, the agent has already narrowed the field to 3–5 vendors. Agentic search punishes brands that are only visible on one surface — an agent will cross-check ChatGPT, Perplexity, Google AI Overviews, and cited outlets against each other, and drop anything that doesn't consistently show up. Crackle PR builds for agentic search by driving multi-surface citation consistency: the same brand facts, in the same words, cited across earned media, on-site authority surfaces, and structured data — so an agent doing five checks finds the same answer five times.
“Agentic search is the shortlist before the shortlist. By the time a buyer opens a browser, an agent has already decided who deserves a click — and consistency across surfaces is what earns the slot.”
Related terms
ChatGPT Search
OpenAI's real-time web-search mode inside ChatGPT that returns synthesized answers with inline source citations.
Perplexity
An AI answer engine popular with technical and research-driven B2B buyers; cites sources inline.
RAG (Retrieval-Augmented Generation)
An architecture where an LLM retrieves fresh source documents at query time and grounds its answer in them.
AI Citation
A reference to your brand inside an AI-generated answer, typically with a link back to a source URL.
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