multi-agent-genie-orchestration

Multi-agent orchestration routes user queries by classifying intent, querying relevant Genie Spaces in parallel, and synthesizing the results into a single coherent answer using LangGraph and the Genie Conversation API.

Intent classification analyzes user queries to determine the relevant data domains, routing each request to the appropriate domain-specific Genie Spaces for targeted data retrieval and parallel execution.

The NO LLM fallback pattern ensures data integrity by preventing the model from hallucinating results when Genie Space queries fail, returning failures or partial data instead of fabricated information.

Yes, this multi-agent orchestration pattern uses LangGraph to execute parallel queries across multiple domain-specific Genie Spaces, enabling faster cross-domain data retrieval and comprehensive synthesis.

Cross-domain synthesis combines information from separate Genie Spaces, such as billing and jobs, by classifying the intent, querying both domains in parallel, and synthesizing the findings into one response.

The primary limitation involves query failures within Genie Spaces, which the NO LLM fallback pattern addresses by refusing to generate fabricated data, ensuring only accurate retrieved data is synthesized.