Orchestrator agents: Integration, human interaction, and enterprise knowledge at the core

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More There is no doubt AI agents will continue to be a fast-growing rend in enterprise AI . But as more companies look to deploy agents, they’re also looking for a way to help them make sense of the many actions these autonomous or semi-autonomous, AI guided bots will take, and avoid conflicts.

To combat the potential sprawl of different AI agents deployed by users , service providers and enterprises alike have been building another type of AI agent: the orchestrator agent. Enter the orchestrator: these type of agents function as managers of other, more specialized agents, understanding each one’s role and activating each based on the next steps needed to finish a task. Most orchestrator agents, sometimes called meta agents, monitor if an agent succeeded or failed and choose the following agent to trigger to get the desired outcome.



Good orchestrator agents exhibit certain features that make these work different from other agents, and for enterprises, elements make them work much better. Integration Agentic ecosystems would eventually bring workflows together, even if the task involves talking to an agent outside the current platform. Orchestrator agents need to have robust integrations with other systems.

Otherwise, agents remain an island able to communicate only with itself. ServiceNow vice president of AI and Innovation Dorit Zilbershot said enterprises need to investigate if the orchestration agents they’re building or buying offer integration points to other systems. “Effective orchestration agents support integrations with multiple enterprise systems, enabling them to pull data and execute actions across the organizations,” Zllbershot said.

“This holistic approach provides the orchestration agent with a deep understanding of the business context, allowing for intelligent, contextual task management and prioritization.” For now, AI agents exist in islands within themselves. However, service providers like ServiceNow and Slack have begun integrating with other agents.

Slack announced it offers integration for agents from Salesforce, Workday, Asana and Cohere . Full stack AI company Writer connects its agents to Amazon and Macy’s APIs so customers can directly sell products. Don Schuerman, CTO at Pega , echoed the sentiment, saying an ideal orchestration agent is “API-centric so it can work both across agents but also across human-centric channels so that humans can be pulled in when needed.

” Knowledge of enterprise processes Like all agents, orchestrator agents need to know how the business works. Orchestrator agents need a more holistic view of the best next step while moving the process forward. Zilbershot said a good orchestration agent “should be able to quickly analyze the context to determine both the best-suited AI agent and the optimal sequence of AI agent assignments to optimize workflows and minimize delays.

” It’s not just about having insight into company data — though that is another essential component for agentic ecosystems — it’s also about understanding the processes enterprises do to run their business. Writer CEO May Habib told VentureBeat in an earlier interview that enterprises that want an effective agentic system provide the workflow for an 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 crap from one system to another,” Habib said.

“Over time, we built an application that, automatically with AI, knows based on the workflow suggests which tools to access.” Reasoning capabilities Due to its nature, orchestrator agents make reasoning decisions more than other AI agents. As AI agents are tasked with more complex tasks, so will the orchestrator agents that help manage them.

Large language models underpin agent creation, and models with greater reasoning capabilities can run different scenarios before triggering the next agent. Orchestrator agents must have strong reasoning skills to ensure the workflow doesn’t break down. Smooth communication between agents and human employees ServiceNow’s Zilbershot pointed out that orchestration agents are primarily responsible for the interaction between humans and agents.

She said enterprises deploying AI agents would benefit from orchestrator agents with user-friendly interfaces and feedback networks. Hence, the agents continue to improve based on how employees interact and use them. “By serving as the connective tissue between specialized AI agents and human operators, orchestration agents make it exponentially easier to not only streamline operations but also enhance the overall effectiveness of an organization’s agentic AI system,” she said.

Although AI agents are designed to go through workflows automatically, experts said it’s still important that the handoff between human employees and AI agents goes smoothly. The orchestration agent allows humans to see where the agents are in the workflow and lets the agent figure out its path to complete the task. “An ideal orchestration agent allows for visual definition of the process, has rich auditing capability, and can leverage its AI to make recommendations and guidance on the best actions.

At the same time, it needs a data virtualization layer to ensure orchestration logic is separated from the complexity of back-end data stores,” said Pega’s Schuerman. Orchestrator agents already ship out in many agent frameworks. It can even be a differentiator for many agent libraries in the future.

As enterprises continue experimenting more with agents, orchestrator agents may improve. Stay in the know! Get the latest news in your inbox daily By subscribing, you agree to VentureBeat's Terms of Service. Thanks for subscribing.

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