Orchestrator Agents: The Key to Managing Enterprise AI Workflows
AI agents are definitely on the rise in the world of enterprise AI.
As companies look to deploy these agents, they also want to figure out how to manage all the actions these autonomous or semi-autonomous bots will take. It’s a big task, and they want to avoid any conflicts along the way.
To tackle the potential chaos from different AI agents, both service providers and businesses are creating a new type of AI agent: the orchestrator agent.
So, what is an orchestrator agent? Think of it as a manager for other specialized agents. It understands each agent's role and activates them based on what needs to be done next to complete a task.
Most orchestrator agents, often called meta agents, keep an eye on whether an agent succeeds or fails. Then, they decide which agent to trigger next to get the desired outcome.
Good orchestrator agents have specific features that set them apart from other agents. These features make them especially effective for businesses.
Integrating agent ecosystems is crucial. It helps bring workflows together, even when tasks involve communicating with agents outside the current platform. Orchestrator agents need strong integrations with other systems. Otherwise, they end up isolated, only able to talk to themselves.
Dorit Zilbershot, Vice President of AI and Innovation at ServiceNow, emphasizes that businesses must check if the orchestration agents they’re building or buying offer integration points with other systems.
“Effective orchestration agents support integrations with multiple enterprise systems,” she says. “This holistic approach gives the orchestration agent a deep understanding of the business context, leading to intelligent task management and prioritization.”
Right now, AI agents tend to exist in silos. However, companies like ServiceNow and Slack are starting to integrate with other agents. For example, Slack has announced it supports agents from Salesforce, Workday, Asana, and Cohere. Meanwhile, Writer connects its agents to APIs from Amazon and Macy’s, allowing customers to sell products directly.
Don Schuerman, CTO at Pega, agrees. He believes an ideal orchestration agent should be “API-centric,” allowing it to work across agents and human channels. This way, humans can step in when needed.
Orchestrator agents need to understand how the business operates. They should have a clear view of the best next step to keep processes moving forward. Zilbershot points out that a good orchestrator agent “should quickly analyze the context to determine the best-suited AI agent and the optimal sequence of assignments.”
It’s not just about having insights into company data, though that’s important too. It’s also about grasping the processes that keep the business running.
Writer’s CEO, May Habib, mentioned that for an effective agentic system, businesses should provide the workflow for the orchestrator agent to follow, not the other way around.
“If you don’t get the nodes in a workflow right, then the automated workflow is just moving junk from one system to another,” she explains. “Over time, we’ve built an application that automatically knows which tools to access based on the workflow.”
Because of their nature, orchestrator agents often make reasoning decisions more than other AI agents. As AI agents take on more complex tasks, orchestrator agents will need to adapt and grow as well.
Large language models play a key role in creating these agents. Models with better reasoning skills can run through different scenarios before activating the next agent. Orchestrator agents must have strong reasoning abilities to keep workflows from breaking down.
Zilbershot also highlights that orchestration agents primarily manage the interaction between humans and agents. She believes businesses deploying AI agents will benefit from orchestrator agents that feature user-friendly interfaces and feedback systems. This way, the agents can continuously improve based on how employees interact with them.
“By serving as the link between specialized AI agents and human operators, orchestration agents make it much easier to streamline operations and enhance the overall effectiveness of an organization’s agentic AI system,” she adds.
Even though AI agents are designed to navigate workflows automatically, experts stress that ensuring a smooth handoff between human employees and AI agents is still crucial. The orchestrator agent allows humans to see where the agents are in the workflow and helps the agent figure out its path to complete the task.
“An ideal orchestration agent allows for a visual definition of the process, has strong auditing capabilities, and can use its AI to make recommendations on the best actions,” says Schuerman. “At the same time, it needs a data virtualization layer to keep orchestration logic separate from the complexities of back-end data stores.”
Orchestrator agents are already part of many agent frameworks. They could even become a differentiating factor for various agent libraries in the future. As businesses continue to experiment with agents, orchestrator agents are likely to improve further.