Europe 2026
Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi
Louis-François Bouchard , Paul Iusztin , Samridhi
Overview
This workshop introduces a system for automating the creation of technical articles, moving beyond generic AI-generated content. It details the architecture and implementation of a deep research agent and a writing agent, designed to work together to produce high-quality, human-like content. The system aims to augment human writers by handling the research and initial drafting, allowing for more efficient and scalable content production.
Who should watch
- AI engineers and builders interested in creating automated content generation systems.
- Product Managers and technical writers looking to improve content production workflows.
- Developers seeking to understand the integration of research agents, writing agents, and evaluation systems.
- Anyone interested in building complex, end-to-end AI systems that combine multiple components.
Key takeaways
- AI-generated content often suffers from "AI slop," hallucinations, and outdated information, necessitating a more robust approach to research and writing.
- The system is divided into two main agents: a deep research agent for exploratory information gathering and a writing agent for structured content creation.
- The research agent utilizes tools like web scraping (Firecrawl, Apify), search engines (Gemini with grounding), and video analysis (Gemini) to gather comprehensive information.
- The writing workflow employs a structured approach with guidelines, writing profiles (structure, terminology, character), and few-shot examples to control generation and ensure a human-like tone.
- An evaluator-optimizer loop, involving a reviewer and editor, refines the generated content through multiple iterations to improve quality and adherence to guidelines.
- Observability tools like Opic are crucial for debugging and monitoring complex agentic workflows by capturing traces of LLM and tool calls.
- AI evaluations are essential for quantifying performance, enabling regression testing, and ensuring continuous improvement of AI systems.
- The system leverages MCP (Meta Communication Protocol) for tool and agent communication, with Fast MCP simplifying agent creation.
Notable quotes
*The most important part is to actually use your product or a potential user should use your product to give you feedback as early on as possible.*
*High-quality technical content is expensive to produce. We do that every day and and we need a lot of people and it needs a lot of time and review and it's just really time-consuming, really expensive.*
*Skills are a very clean way of, you know, doing that and it is also very shareable cuz, you know, we internally write a lot of skills for our team and like share it with each other.*
Unofficial community note. Prefer the recording for nuance.