Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi

Avoiding Generic AI Slop

Standard LLM outputs suffer from hallucinations, outdated information, and overused phrases like 'intricacies' and 'rapidly evolving' that provide shallow, meaningless value.

Automation Solution

The team built a deep research and writing agent system to automate technical course and article creation, replacing the expensive manual process of research plus human writing.

Meta-Learning Approach

They used this same agentic system to generate a comprehensive course teaching students exactly how to build the deep research system itself.

The Autonomy Slider

AI engineering exists on a spectrum from simple prompting to complex agentic systems, where increased autonomy reduces control and raises costs per task.

Workflows Defined

Workflows execute predetermined steps in fixed sequences—like a ticket handler that always classifies, routes, drafts, and validate—offering reliability without dynamic environmental reaction.

Agents Defined

True agents autonomously plan actions, dynamically select tools, and react to environmental changes, making them necessary only when tasks require branching logic like a CRM marketing content generator.

Context Rot Threshold

LLM performance degrades significantly around 200,000 tokens due to the 'lost in the middle' training problem, occurring well before theoretical context window limits.

Delegation Pattern

Manage context budget by delegating specialized tasks to tools or sub-agents with isolated system prompts and validation logic rather than overloading a single agent's memory.

Multi-Agent Triggers

Deploy multi-agent systems only when facing specific constraints like 20+ tools, strict security or compliance requirements, or insufficient single-agent context windows.

Core Capabilities

Effective deep research agents combine planning, autonomous web navigation, reliable source citation, and feedback loops to perform thorough research without human micromanagement.

Hybrid Implementation

Production-grade AI products integrate workflows, single agents, and multi-agent systems rather than using one pattern exclusively, with deep research serving as a prime example of this unified approach.