New Live Session: Designing Multi-Agent Deep Search Systems

2024-06-18 · Source: To Data & Beyond · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, quick

Summary

A 1.5–2 hour technical workshop titled "Designing Multi-Agent Deep Search Systems" will be held on Saturday, May 16, 2026, from 17:00 to 18:30 EEST. This session focuses on architecting deep search agents that extend beyond basic retrieval. It will cover designing agentic workflows for planning searches, utilizing various tools, collecting and validating evidence from multiple sources, handling contradictions, reasoning about temporal data, merging findings into structured outputs, and iterative improvement. The workshop emphasizes core components, design decisions, tradeoffs, and patterns for building reliable deep search systems, without delving deeply into implementation code. Attendees will receive a live workshop, recording, slide deck, technical notes, an architecture blueprint, and a tool design reference.

Key takeaway

For AI Architects and AI Engineers designing advanced information retrieval systems, this workshop offers a structured approach to building reliable deep search agents. You should consider attending to gain insights into multi-agent architectures, tool design, and validation strategies that move beyond basic RAG, enabling more robust and accurate information synthesis for complex queries.

Key insights

Multi-agent deep search systems enhance retrieval by planning, validating, and merging information from diverse sources.

Principles

• Deep search requires multi-agent orchestration.
• Validation is crucial for information quality.
• Iterative refinement improves search outcomes.

Method

Design an agentic workflow with planner, executor, validation, merging, and improvement agents to manage search, tool use, evidence collection, conflict resolution, and structured output generation.

In practice

• Implement planner agents for sub-question breakdown.
• Integrate OCR, scraping, and video-to-text tools.
• Develop source reliability scoring mechanisms.

Topics

• Multi-Agent Systems
• Deep Search Agents
• Large Language Models
• Retrieval-Augmented Generation
• Tool Design

Best for: AI Architect, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by To Data & Beyond.