Knowledge Graph Engineering For Agents: The Multidomain Problem

2021-11-04 · Source: High ROI AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, short

Summary

The article highlights a fundamental challenge in AI agent development: agents struggle with multi-domain knowledge integration, unlike humans who can "figure it out" through experience. It draws a parallel between teaching data science to expert engineers who fixate on math, and AI agents failing to bridge discrete knowledge domains like "Sales" and "Marketing for Sales." The author introduces Karpathy's LLM Wiki, a system that uses an LLM agent to create a structured markdown wiki from raw documents, improving over time. This LLM Wiki, described as an "early Hadoop for agents," addresses the need for persistent memory and efficient context delivery by batching information generation, akin to knowledge graph engineering. The core problem for agents remains the inability to accumulate knowledge and synthesize information across domains without explicit engineering, leading to discussions on batch versus streaming generation patterns for agentic memory.

Key takeaway

For AI Engineers designing agentic systems, recognize that agents cannot "figure out" multi-domain interactions or accumulate knowledge organically. You must explicitly engineer persistent memory and context delivery, choosing between batch generation for stable, predictable information requirements and streaming generation for volatile or unpredictable needs. This approach ensures agents can reliably perform complex workflows like "Content To Cash" by providing structured, integrated knowledge.

Key insights

AI agents require explicit engineering to integrate multi-domain knowledge and accumulate persistent memory, unlike human pattern-matching.

Principles

• Information is the new code.
• Domain-siloed knowledge fails to represent work.
• Agents need engineered memory for context.

Method

Karpathy's LLM Wiki uses an LLM agent to ingest raw documents, extract key ideas into a structured markdown wiki, answer queries from the wiki, and lint for inconsistencies, effectively creating a guided knowledge graph.

In practice

• Batch information generation for predictable needs.
• Stream information generation for unpredictable needs.
• Use LLM Wiki for structured knowledge accumulation.

Topics

• Knowledge Graph Engineering
• AI Agents
• Multidomain Problem
• LLM Wiki
• Batch Generation

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by High ROI AI.