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Build multi-agent AI systems by defining autonomous agents with specific roles, goals, and tools, then orchestrating their collaborative workflows to automate complex tasks like research and content creation pipelines.

Yes, multi-agent orchestration supports integration with various LLMs including OpenAI, Anthropic, and Ollama, allowing you to customize the language models powering your autonomous agents for enhanced functionality.

Sequential task execution processes agent workflows in a linear order, while hierarchical task execution structures agents in a managed hierarchy, enabling complex automation scenarios where a manager agent delegates tasks to worker agents.

Create an automated pipeline by assigning one agent to research a topic, another to analyze the findings, and a third to write a report, defining specific roles and goals for each agent in the workflow.

You can equip autonomous agents with both custom and built-in tools to enhance their capabilities, enabling them to perform specialized actions required for complex automation scenarios and collaborative task execution.

Multi-agent systems require defining specific roles, goals, and backstories for each agent, and complex hierarchical workflows may introduce orchestration overhead, requiring careful design to ensure efficient collaborative task execution.