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agent-framework/python/samples/05-end-to-end/workflow_evaluation
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Copilot b05fc9e849 Python: Add load_dotenv() to samples for .env file support (#4043)
* Initial plan

* Add load_dotenv() to 303 Python samples for environment variable loading

Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com>

* Update SAMPLE_GUIDELINES.md to document load_dotenv() requirement

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* Update samples README.md to document .env file usage

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* Run ruff format on all changed sample files

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* Clarify load_dotenv() usage in README - local dev vs production

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* Remove deprecated getting_started folder as requested

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* Document env_file_path parameter for per-client configuration

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* Merge main branch to resolve conflicts

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* Fix run_evaluation.py file that was empty in merge commit

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* Remove dotnet changes from merge - out of scope for this PR

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* Remove package and test changes from merge - only sample changes needed

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* Remove test_func_utils.py - only sample changes needed

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* Revert sample files not in original changeset - keep only load_dotenv additions

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* Move load_dotenv() outside snippet tag in 06_host_your_agent.py

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* Fix comment placement - move load_dotenv before code comments

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* Fix load_dotenv() placement across all samples - after docstring, before code comments

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* Merge latest main branch with load_dotenv changes

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* Remove non-sample changes from merge - keep only load_dotenv additions

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* Revert non-load_dotenv sample changes from merge

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* Fix run_evaluation.py - use main's improved version (file already had load_dotenv)

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* Manual update

* Manual update 2

* Fix Role usage and load_dotenv placement per PR review feedback

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* Fix Role usage - use string literals not enum attributes

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* Fix SAMPLE_GUIDELINES.md example - load_dotenv before docstring per guidance

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* Move load_dotenv() before docstrings in all samples per SAMPLE_GUIDELINES ordering

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* Address PR review: rename files, fix placement, add session usage, remove note

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* Update Redis README to reference renamed file redis_history_provider.py

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Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com>
Co-authored-by: Tao Chen <taochen@microsoft.com>
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Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
b05fc9e849 ยท 2026-02-19 10:55:13 +00:00
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2026-02-18 19:50:33 +00:00

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:

  1. Execute the multi-agent travel planning workflow
  2. Display response summary for each agent
  3. Create and run evaluation on hotel, flight, and activity search agents
  4. Monitor progress and display the evaluation report URL