AI Videos
  • All Technology
  • AI
  • Autonomy
  • B2B Growth
  • Big Data
  • BioTech
  • ClimateTech
  • Consumer Tech
  • Crypto
  • Cybersecurity
  • DevOps
  • Digital Marketing
  • Ecommerce
  • EdTech
  • Enterprise
  • FinTech
  • GovTech
  • Hardware
  • HealthTech
  • HRTech
  • LegalTech
  • Nanotech
  • PropTech
  • Quantum
  • Robotics
  • SaaS
  • SpaceTech
AllNewsDealsSocialBlogsVideosPodcastsDigests

AI Pulse

EMAIL DIGESTS

Daily

Every morning

Weekly

Sunday recap

NewsDealsSocialBlogsVideosPodcasts
AIVideosBuilding the Agent Graph | Multi Agent Workflows for Beginners | Part 9
AI

Building the Agent Graph | Multi Agent Workflows for Beginners | Part 9

•November 30, 2025
0
Data Science Dojo
Data Science Dojo•Nov 30, 2025

Why It Matters

Mapping agents as a graph with conditional edges clarifies orchestration and decision-making in multi-agent systems, enabling scalable automation of complex tasks and easier debugging. This approach is practical for teams building coordinated agent workflows and accelerates deployment by separating roles and routing logic.

Summary

The presenter walks through constructing an agent graph for a multi-agent workflow, demonstrating how to define nodes (researcher, coder, supervisor), import required libraries, and instantiate a class to set up the workflow. They explain adding conditional edges that route decisions between nodes—essentially encoding logic for whether tasks go to the coder or researcher. The session covers compiling the graph and notes that an existing supervisor component need not be rerun. A notebook link and Q&A were promised for follow-up.

Original Description

In Part 9, we bring everything together and construct the agent graph that powers our workflow.
In this session:
Map out nodes (supervisor, researcher, coder) as logical units
Define edges and conditional routing between agents
Structure the workflow as a graph-based execution flow
Compile and initialize the graph for multi-agent coordination
This part focuses on turning independent components into a connected, decision-driven multi-agent system ready to process tasks and route intelligently.
#AI #MultiAgent #AgenticAI #LLM #GraphWorkflows #RoutingLogic #GenerativeAI #Automation
Learn data science, AI, and machine learning through our hands-on training programs: https://www.youtube.com/@Datasciencedojo/courses
Check our community webinars in this playlist: https://www.youtube.com/playlist?list=PL8eNk_zTBST-EBv2LDSW9Wx_V4Gy5OPFT
Check our latest Future of Data and AI Conference: https://www.youtube.com/playlist?list=PL8eNk_zTBST9Wkc6-bczfbClBbSKnT2nI
Subscribe to our newsletter for data science content & infographics: https://datasciencedojo.com/newsletter/
Love podcasts? Check out our Future of Data and AI Podcast with industry-expert guests: https://www.youtube.com/playlist?list=PL8eNk_zTBST_jMlmiokwBVfS_BqbAt0z2
0

Comments

Want to join the conversation?

Loading comments...