How to govern multi-agent systems at scale?*

2026-04-16 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Intermediate, quick

Summary

Galileo co-founder Yash Sheth and CrewAI founder Joao Moura will host a live session on April 21st focused on governing multi-agent systems at scale. The session will address the challenges of ensuring safety, managing costs, and maintaining compliance for both first-party and third-party agents. Attendees will learn strategies for enforcing security policies, dynamically steering agents to optimal models and fallback tools to enhance accuracy and control token expenditures, and centralizing policy management across diverse agent types, including CrewAI. A key aspect covered is enabling non-technical stakeholders, such as risk and compliance teams, to contribute to policy creation and maintenance without requiring coding expertise.

Key takeaway

For AI Architects and MLOps Engineers deploying multi-agent systems, prioritizing robust governance is critical for scalable, secure, and cost-effective operations. You should explore centralized policy management solutions that allow non-technical stakeholders to define and maintain compliance rules, ensuring your systems meet safety and regulatory requirements while optimizing resource use. This approach mitigates risks associated with agent behavior and token expenditure.

Key insights

Effective governance is crucial for scaling multi-agent systems safely, managing costs, and ensuring compliance.

Principles

• Centralize policy management
• Involve non-technical stakeholders

Method

Implement runtime steering for model/tool selection, enforce safety policies, and enable non-technical policy contributions to govern multi-agent systems.

In practice

• Enforce agent safety policies
• Steer agents to optimal models
• Control token costs

Topics

• Multi-agent Systems
• Agent Governance
• Policy Enforcement
• Cost Optimization
• Compliance Management

Best for: MLOps Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.