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Graph Engineering for Agentic AI Systems. Design the topology. Don't inherit it Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu

Książka w języku angielskim angielski
Graph Engineering for Agentic AI Systems. Design the topology. Don't inherit it Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu - okladka książki

Graph Engineering for Agentic AI Systems. Design the topology. Don't inherit it Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu - okladka książki

Graph Engineering for Agentic AI Systems. Design the topology. Don't inherit it Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu - audiobook MP3

Graph Engineering for Agentic AI Systems. Design the topology. Don't inherit it Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu - audiobook CD

Autorzy:
Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
372
Dostępne formaty:
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Agentic AI systems need more than capable models: they need clear rules for how work runs. Graph Engineering for Agentic AI Systems helps you design that structure, starting with when a single-agent loop is enough and when multi-agent orchestration earns its complexity. Distinguish execution graphs, knowledge graphs, and learned graphs, then explore typed state, routing, parallel fan-out and joins, and human approval gates. Compare runtimes including LangGraph, Google ADK, Microsoft Agent Framework, and the OpenAI Agents SDK.

Worked examples, chapter exercises, and companion code connect architecture to implementation. Explore knowledge graph memory and GraphRAG, checkpointing, bounded retries, idempotency, and production observability. Examine permissions and authorization boundaries, then evaluate execution traces, node contracts, and stop conditions. By the end, you'll be equipped to design, test, and operate agent workflows with explicit topology, controlled side effects, and traceable decisions.

O autorze książki

Ken Huang is a renowned AI expert, serving as co-chair of AI Safety Working Groups at Cloud Security Alliance and the AI STR Working Group at World Digital Technology Academy under the UN Framework. As CEO of DistributedApps, he provides specialized GenAI consulting.
A key contributor to OWASP's Top 10 Risks for LLM Applications and NIST's Generative AI Working Group, Huang has authored influential books including Beyond AI (Springer, 2023), Generative AI Security (Springer, 2024), and Agentic AI: Theories and Practice (Springer, 2025)
He's a global speaker at prestigious events such as Davos WEF, ACM, IEEE, and RSAC. Huang is also a member of the OpenAI Forum and project leader for the OWASP AI Vulnerability Scoring System project.

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