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Agentic Research System

ResearchLoop AI

An agent-based research workflow that turns a simple brief into source collection, synthesis, prioritization, and a clean weekly report for founders, operators, or strategy teams.

Date

April 2026

Type

Research Automation

Client

Strategy Team

LangGraph OpenAI Notion API Email Automation Postgres
ResearchLoop AI preview
Agent

Project Overview

ResearchLoop AI was created for teams that need recurring market scans, product comparisons, or trend updates without manually repeating the same research process every week. The system breaks a request into steps, assigns jobs across multiple agents, and compiles the final output into a readable brief.

The user experience focuses on transparency: sources are grouped, assumptions are visible, and the final report separates findings from recommendations so decision-makers can move quickly without losing trust in the process.

Technologies Used

  • LangGraph for multi-step agent flow
  • OpenAI for synthesis and drafting
  • Notion API for report delivery
  • Database logging for traceability

Key Features

Brief-to-Plan Parsing

Converts open-ended requests into clear research steps, themes, and output structure.

Source Collection

Gathers and groups references by topic before producing a summary or recommendation.

Weekly Briefs

Packages the final output into a predictable format for leadership updates or team syncs.

Traceable Output

Preserves sources, reasoning checkpoints, and open questions for human review.

Outcome

This concept positions AI as a reliable strategic research layer rather than a one-off chat tool with no process memory.

Planning an agentic product or workflow?

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