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Eduard van Valkenburg
0521f5bed8
* [BREAKING] Rename ChatAgent -> Agent, ChatMessage -> Message, ChatClientProtocol -> SupportsChatGetResponse Simplify the public API by removing redundant 'Chat' prefix from core types: - ChatAgent -> Agent - RawChatAgent -> RawAgent - ChatMessage -> Message - ChatClientProtocol -> SupportsChatGetResponse Also renamed internal WorkflowMessage (was Message in _runner_context) to avoid collision. No backward compatibility aliases - this is a clean breaking change. * [BREAKING] Rename Agent chat_client parameter to client * Fix rebase issues: WorkflowMessage references and broken markdown links * Fix formatting and lint issues from code quality checks * Fix import ordering in workflow sample files * fixed rebase * Fix test failures: use WorkflowMessage and A2AMessage after ChatMessage→Message rename - Replace Message(data=..., source_id=...) with WorkflowMessage(...) in workflow tests - Fix isinstance check in A2A agent to use A2AMessage instead of Message - Fix import in test_workflow_observability.py (Message→WorkflowMessage) * Fix lint, fmt, and sample errors after ChatMessage→Message rename - Auto-fix 70+ ruff lint issues across samples (ChatMessage→Message refs) - Fix HostedVectorStoreContent→Content.from_hosted_vector_store in file search sample - Fix _normalize_messages→normalize_messages in custom agent sample - Fix context.terminate→raise MiddlewareTermination in middleware samples - Fix with_update_hook→with_transform_hook in override middleware sample - Add TOptions_co import back to custom_chat_client sample - Add noqa for FastAPI File() default in chatkit sample - Fix B023 loop variable capture in weather agent sample * fix: update Agent constructor calls from chat_client to client in declaration-only tool tests * fix: add register_cleanup to devui lazy-loading proxy and type stub * fixed tests and updated new pieces * fix agui typevar * fix merge errors * fix merge conflicts * fiux merge * Remove unused links --------- Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
0521f5bed8
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2026-02-10 23:04:32 +00:00
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Multi-Agent Travel Planning Workflow Evaluation
This sample demonstrates evaluating a multi-agent workflow using Azure AI's built-in evaluators. The workflow processes travel planning requests through seven specialized agents in a fan-out/fan-in pattern: travel request handler, hotel/flight/activity search agents, booking aggregator, booking confirmation, and payment processing.
Evaluation Metrics
The evaluation uses four Azure AI built-in evaluators:
- Relevance - How well responses address the user query
- Groundedness - Whether responses are grounded in available context
- Tool Call Accuracy - Correct tool selection and parameter usage
- Tool Output Utilization - Effective use of tool outputs in responses
Setup
Create a .env file with configuration as in the .env.example file in this folder.
Running the Evaluation
Execute the complete workflow and evaluation:
python run_evaluation.py
The script will:
- Execute the multi-agent travel planning workflow
- Display response summary for each agent
- Create and run evaluation on hotel, flight, and activity search agents
- Monitor progress and display the evaluation report URL