Episode 5: Multi-Agent Systems & Workflow Orchestration with @SambaNovaSystems

Why It Matters

Multi‑agent architectures turn costly, brittle single‑model pipelines into scalable, cost‑effective systems, unlocking reliable AI automation for complex enterprise workflows.

Key Takeaways

• Single-agent context limits cause noise and error compounding.
• Multi-agent patterns—supervisor/worker, parallel fan-out, writer‑critic—enable effective task specialization.
• Isolated sub‑agents keep supervisor context clean, improving scalability.
• Selecting appropriate model per sub‑task reduces token costs dramatically.
• SambaNova’s Deep Agents library simplifies building and orchestrating multi‑agent workflows.

Summary

The webinar, part of a series on AI agents, focused on building multi‑agent systems and orchestrating workflows with SambaNova’s platform. Lead AI architect Quasi Ankama from Canva walked the audience through design patterns, tooling, and practical implementation.

Ankama explained why single‑agent pipelines quickly hit context limits, accumulate errors, and suffer from diluted expertise. He introduced three core multi‑agent patterns—supervisor/worker coordination, parallel fan‑out, and writer‑critic loops—that address these issues by enabling specialization, concurrency, and built‑in quality checks.

The session used an incident‑postmortem use case, where a supervisor agent spawns log‑analysis, metric‑analysis, and reporting sub‑agents. A live demo showed how the Deep Agents library generates isolated task tools from a simple dictionary, allowing each sub‑agent to run with its own model and prompt while returning concise summaries to the supervisor.

By adopting these patterns, enterprises can scale AI‑driven processes, keep token usage low by matching models to tasks, and maintain auditability through isolated contexts. SambaNova’s fast inference and tooling lower operational costs, making large‑scale multi‑agent deployments feasible for thousands of users.

Original Description

Modern AI is moving beyond single agents. In this session, Kwasi Ankomah (Lead AI Architect, SambaNova Systems) breaks down how multi-agent systems solve scaling challenges through orchestration, supervisor-worker models, parallel execution, and writer-critic loops.

What you'll learn:

- Why single agents fail at scale

- Core multi-agent design patterns

- Supervisor-based agent systems

- Parallel and recursive workflow design

- Debugging multi-agent failure modes

Live demos included. Built for AI engineers, developers, and anyone building scalable LLM applications.

#AgenticAI #multiagentsystems #sambanova #workflows

-----------

👉 Learn more about Data Science Dojo here:

https://datasciencedojo.com/

👉 Watch the latest video tutorials here:

https://datasciencedojo.com/tutorials/

👉 See what our past attendees are saying here:

https://datasciencedojo.com/data-scie...

--

At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 8000+ employees from over 2000+ companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.

--

🔗 Subscribe to our newsletter for data science content & infographics: https://datasciencedojo.com/newsletter/