As businesses move deeper into autonomous AI systems, many are deploying multi-agent architectures; frameworks where different AI agents handle specialized tasks and collaborate on shared goals. At the heart of many of these systems lies one key enabler: the Coordinating Agent.
It’s the agent that manages other agents.
It’s the conductor of your orchestra.
And if left unguarded, it may become your riskiest weak spot.
In many agentic systems, multiple AI agents are deployed in tandem, each assigned a specific role. One agent might summarize research, another might write content, and another might perform system queries or take real-world actions.
To keep this ensemble working together, most architectures rely on a Coordinating Agent: a “manager” that interprets high-level goals, assigns sub-tasks, oversees execution, and integrates outputs across agents.
This structure makes the system more efficient. But it also introduces centralized vulnerabilities.
When a single agent is responsible for interpreting, assigning, and validating actions, compromise or confusion at that level affects everything downstream.
If the Coordinating Agent:
...the result could be a cascade of failures across your system.
In high-risk environments like those handling private data, automated cybersecurity, financial transactions, or supply chain logistics, these failures don’t just break internal workflows. They can lead to:
In short: the smarter your AI, the higher the stakes when it fails.
At the executive level, the temptation is to see Coordinating Agents as operational glue (central to driving automation, cost savings, and faster outputs.) But these systems are often deployed without thorough threat modeling or fail-safes.
Ask yourself:
Most businesses don’t yet have answers. And that’s exactly the problem.
Here’s how to mitigate risk while maintaining the benefits of agentic deployment:
Simulate what happens if the agent gives flawed outputs or gets hijacked. Identify what’s vulnerable and how quickly you’d detect the error.
Don’t let one agent control too much. Limit access to critical systems and sensitive data unless fully auditable.
Insert validation steps between task assignment and execution. Use human-in-the-loop reviews for higher-risk actions.
Coordinating agents may evolve based on feedback loops. Regularly assess whether its decision-making patterns are staying aligned with business rules.
Ensure your C-suite, board, and department heads understand the difference between a smart tool and a self-directing actor. Governance starts with clarity.
Remember: One Smart Agent Can Turn the Whole System Rogue
The more embedded your AI becomes in your business operations, the more critical it is to treat agent orchestration as a strategic risk area, not just a technical one.
At Galson Research, we work with executive teams to:
Because when tech makes sense, you make better decisions and lead with confidence.
Agentic AI refers to autonomous systems that make decisions, perform tasks, and coordinate actions with minimal human input. These systems go beyond prediction by taking initiative based on goals and context. For a deeper explanation, read our full article: What Executives Need to Know About Agentic AI.
AI agents are often categorized into five types based on how they make decisions:
Intelligent agents typically follow four core principles:
Not by itself. ChatGPT is a conversational model that responds to prompts but does not act on its own or plan across steps. However, when connected to tools, code execution, or workflows, it can become part of an agentic system. In that context, oversight and control are essential.