Feeding Live Market Intent to AI Agents via MCP and APIs: A GTM Architecture Guide
sibraan_ · reddit · 2026-09-09
A developer shares how to build a real-time data layer for autonomous GTM agents. He identifies context degradation as the biggest failure point when using frameworks like Claude Code, Cursor, or LangChain—static CSVs or CRM data lead to generic personalization. The solution is decoupling scraping from agent reasoning: use infrastructure like Scale Intelligence to ingest signals from 75+ data sources and resolve public activity to corporate domains, then expose an intent graph via API or MCP server. Agents call tools like getactivesignals(accountdomain, timewindow) or getbuyingreadiness(icpsegment) to receive structured JSON (company profile, pain points, buying readiness score). This keeps agents lightweight, avoids scraper maintenance, and grounds actions in live market events.
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