multi-agent-pipeline

You orchestrate multiple LLM agents by deploying a centralized orchestrator and runner that coordinate domain-specific agents in parallel or sequentially, aggregating their results into deterministic structured outputs for downstream pipelines.

You need a multi-agent pipeline when a product feature requires consulting multiple domain experts, such as pricing, user intent, and risk assessment, and synthesizing their separate outputs into a single coordinated decision.

The orchestrator coordinates domain agents by utilizing a base agent contract and runner logic to execute agents either in parallel or sequentially, applying logging and deterministic output formatting to aggregate structured results.

Yes, you can implement domain-specific agents by extending a base agent class that enforces clear contracts and prompts, allowing the orchestrator to reliably manage and execute each specialized agent within the pipeline.

The best way to structure LLM agent outputs for analytics is to use a pipeline runner that applies deterministic output formatting, ensuring the aggregated results from all domain agents are structured consistently for downstream consumption.

No external dependencies are required to build the multi-agent orchestration pipeline, as it implements the base agent contract, orchestrator logic, runner, and logging internally to support end-to-end workflows.