Multi-Source Research AgentNO. 04
Multi-Source Research Agent
SIDE PROJECTSHIPPED
Multi-Source Research AgentCase Study →
SIDE PROJECTSHIPPED

Building Multi-Source Research Agent from 0-1

Autonomous research agent orchestrating deep web search, PDF synthesis, tool execution, and grounded citation generation.

ROLE

Lead Engineer

TEAM

Core Builder

TIMELINE

2024 — 2026

SKILLS

Python, LangChain, Gemini API

01. CONTEXT & THE PROBLEM

The Friction Point & Opportunity

Synthesizing deep research across dozens of technical papers and live websites manually takes hours of tedious note-taking.

02. THE SOLUTION & ARCHITECTURE

System Design & Implementation

Designed an agent loop using LangChain, Gemini function calling, and vector retrieval over PDF embeddings.

03. CORE FEATURES & CAPABILITIES

Key Capabilities & Engineering Depth

Vector Retrieval & Hybrid Search

Sub-second semantic search across multi-source knowledge bases.

Real-time Telemetry & Tele-Advisory

Self-healing data ingestion pipelines with instant verification.

04. INTERACTION DESIGN & UX FLOW

Tactile Micro-interactions & Usability

Interfaces should feel alive, responsive, and predictable. Every touchpoint was tested against real user mental models to ensure zero confusion during multi-step tasks.

PythonLangChainGemini APIFAISSFastAPIStreamlit & Next.js
05. IMPACT, TELEMETRY & ADOPTION

Measurable Outcomes & Production Resilience

Accelerated comprehensive technical research turnaround time from 4 hours to under 30 seconds.

06. REFLECTION & TAKEAWAYS

Key Learnings & Future Roadmap

Building software from 0 to 1 reinforced the principle that great engineering is about discipline, simplicity, and continuous feedback loops with real users.