The Designing Multi-Agent Deep Search Systems recording is now available + 50% Discount Till End of May

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

Summary

The workshop recording "Designing Multi-Agent Deep Search Systems" by Youssef Hosni is now available, focusing on advanced deep search agents that surpass basic retrieval-augmented generation (RAG) systems. This 2-hour workshop details how to design systems capable of planning searches, decomposing complex questions, utilizing external tools, validating evidence, managing contradictions, and reasoning about data freshness to produce structured answers. Purchasers receive the full recording, a complete slide deck, a code repository, a 200-page technical handbook, an architecture blueprint, tool design references, source validation patterns, and an evaluation framework. A 50% discount is offered for the first week to newsletter subscribers using code 6Z7JA0A.

Key takeaway

For AI Engineers developing advanced RAG systems or multi-agent applications, this workshop offers a clear architectural blueprint for designing more reliable deep search capabilities. You should consider acquiring the recording to gain practical methods for planning complex searches, validating evidence, and merging findings into structured answers, enhancing your system's robustness.

Key insights

Deep search agents require advanced planning, evidence validation, and contradiction handling beyond simple RAG.

Principles

• Deep search extends beyond basic RAG.
• Validate evidence and handle contradictions.
• Structure findings for final answers.

Method

Design systems to plan searches, break questions into sub-tasks, use tools, collect/validate evidence, handle contradictions, reason about freshness, and merge findings.

In practice

• Implement multi-agent deep search architecture.
• Utilize provided code repo for projects.
• Apply source validation design patterns.

Topics

• Multi-Agent Systems
• Deep Search
• RAG Systems
• LLM Applications
• Evidence Validation
• Search Architecture

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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