Building a High-Quality AI Research Stack with Multi-Agent Verification Loops
EXM7777 · x · 2026-08-02
The author shares how to transform an AI agent from a "slop factory" into a narrow expert using a custom workflow. The core research stack includes replacing traditional deep research tools with Exa, using Firecrawl to scrape specific sites for Markdown/PDF analysis, and leveraging /last30days for the latest methodologies.
Architecturally, the system employs a multi-agent verification fleet:
- Subagents are tasked with verifying findings and filtering out low-quality content (slop).
- The system automatically reruns tasks with fresh keyword sets to address missing information.
- The loop is strictly engineered: draft a plan -> run initially -> review outputs -> delete useless data -> redraft around gaps.
- A third-party provider's agent acts as a final quality gate, scoring the results and sending low-scoring outputs back into the loop.
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