Automated Business Search for AI Agents, on AgentPMT

One Search Across Everything AgentPMT Publishes

Ask an AI assistant whether a platform has a connector for sending texts or parsing a PDF, and you usually get a confident answer stitched together from stale training data and a guessed URL. The user clicks, lands on a 404, and quietly stops trusting the agent. Every wrong link is a support ticket someone has to answer.

AgentPMT Platform Search fixes that at the source. It is a connector on the AgentPMT marketplace that lets your agents search the entire AgentPMT site in a single request: marketplace tools, published workflows, and prebuilt agents, alongside documentation, help articles, research papers, videos, and FAQs. Results come back ranked, with canonical links to pages that actually exist. Agents discover it and call it through AgentPMT's dynamic MCP server, so there is nothing to host and nothing to keep in sync. AgentPMT Platform Search is live now.

Grounded answers instead of scraped ones

Three modes cover most of what an agent needs. A site-wide sweep answers the "is there a tool for this?" question by ranking products, workflows, agents, docs, articles, papers, videos, pages, and FAQs together, so a vague capability request like "make phone calls" resolves to the specific listing that does it. A knowledge-only mode narrows to documentation, help articles, research papers, videos, and FAQs when a response needs to cite official material rather than point at a product. A recency mode returns the newest published content by type, for the times when what shipped this week beats what ranks best.

Underneath, the retrieval layer does the unglamorous work: vector search and embeddings across parsed, indexed site content, with text extraction and parsing already handled so your agent receives clean, citable results instead of raw HTML. Agent-facing output is sanitized by design, carrying titles, summaries, canonical URLs, and relevance scores without leaking vendor identifiers into your responses. Usage is pay-per-use with no required subscription, so a help bot that fields forty questions on a quiet Tuesday costs what forty questions cost.

Where it earns its keep

Support deflection is the obvious first win for automated business search. Point a help agent at your documentation and let a webhook fire it the moment a new ticket lands: it searches official docs and FAQs, drafts a reply with links the customer can actually follow, and escalates only what it genuinely cannot answer. Teams running business process automation on top of a growing catalog get a second win in onboarding, where a scheduled morning run pulls recently published content and posts a digest into the channel where builders already work.

Composition is where this stops behaving like a search box. Inside the Workflow Builder Agent, which walks a builder from idea to published skill across a catalog of well over a hundred connectors, discovery is the step where people stall. They know exactly what they want to automate and cannot name the tool that does it. Platform Search slots into that moment in the build loop and turns "I need something that reads a spreadsheet and texts me" into a shortlist of real listings with working links. Browse the full workflow catalog and the pattern repeats: nearly every multi-step business workflow process opens with finding the right piece.

Runs happen however suits the job. Chain it into an automation flow triggered on a schedule, or by a webhook when something changes on another platform, drive it from Hermes, OpenClaw, Codex, Claude Code, or ChatGPT, or let it run fully autonomously with no local agent setup at all through AgentPMT's dynamic MCP.

Retrieval is the difference between an answer and a guess

Every team shipping ai business solutions has hit the same wall. Language models are fluent about platforms they have only partially memorized, and fluency without retrieval produces citations to pages that were never published. The fix the industry settled on is grounding: give the model a retrieval path into authoritative content and require it to answer from what it finds. That is straightforward when the corpus is one internal wiki, and considerably harder when it spans a marketplace catalog, a docs site, a research library, and a video archive that all update on different schedules. Reconstructing any of that by scraping the open web is slow, brittle, and frequently stale by the time it returns. A business automation platform that indexes its own surfaces and hands them to agents as a first-class connector removes an entire category of failure from content management and publishing work, and from the academic and scientific research lookups that sit next to it.

Wire it into your agent today and let it answer from the source. Find it on the AgentPMT marketplace.