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Editor’s note: Emilia will lead an editorial roundtable on this topic at VB Transform next week. Register today.
AI agents seem like an inevitability these days. Most enterprises already use an AI application and may have deployed at least a single-agent system, with plans to pilot workflows with multiple agents.
Managing all that sprawl, especially when attempting to build interoperability in the long run, can become overwhelming. Reaching that agentic future means creating a workable orchestration framework that directs the different agents.
The demand for AI applications and orchestration has given rise to an emerging battleground, with companies focused on providing frameworks and tools gaining customers. Now, enterprises can choose between orchestration framework providers like LangChain, LlamaIndex, Crew AI, Microsoft’s AutoGen and OpenAI’s Swarm.
Enterprises also need to consider the type of orchestration framework they want to implement. They can choose between a prompt-based framework, agent-oriented workflow engines, retrieval and indexed frameworks, or even end-to-end orchestration.
As many organizations are just beginning to experiment with multiple AI agent systems or want to build out a larger AI ecosystem, specific criteria are at the top of their minds when choosing the orchestration framework that best fits their needs.
This larger pool of options in orchestration pushes the space even further, encouraging enterprises to explore all potential choices for orchestrating their AI systems instead of forcing them to fit into something else. While it can seem overwhelming, there’s a way for organizations to look at the best practices in choosing an orchestration framework and figure out what works well for them.
Orchestration platform Orq noted in a blog post that AI management systems include four key components: prompt management for consistent model interaction, integration tools, state management and monitoring tools to track performance.
Best practices to consider
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