chore: import upstream snapshot with attribution

python/samples/02-agents/harness/harness_data_processing.py

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+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-foundry",
+#     "textual>=6.2.1",
+#     "rich>=13.7.1",
+#     "azure-identity",
+#     "python-dotenv",
+# ]
+# ///
+# Run with any PEP 723 compatible runner, e.g.:
+#   uv run samples/02-agents/harness/harness_data_processing.py
+
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Harness Data Processing Assistant with Console UI and tool approvals.
+
+Demonstrates ``create_harness_agent`` configured with a ``FileAccessProvider``
+to give an agent access to a folder of CSV data files. The agent can read,
+analyze, and extract information from the data, then write results back as new
+files via the ``file_access_*`` tools.
+
+This sample also demonstrates **tool approval**. The ``FileAccessProvider``
+registers all of its tools with ``approval_mode="always_require"``, so every
+file operation would normally prompt the host for approval. To keep read-only
+exploration frictionless while still guarding mutations, the agent is given the
+:meth:`FileAccessProvider.read_only_tools_auto_approval_rule` auto-approval
+rule. With this rule:
+
+- Read-only tools (read, list files, list subdirectories, search) are
+  auto-approved and run without prompting.
+- Write tools (save and delete) still require explicit approval, so you are
+  asked before the agent modifies the file store.
+
+The sample includes a pre-populated ``working/`` folder with sales transaction
+data. The ``FileAccessProvider`` is pointed at that folder (resolved relative to
+this script) so it works regardless of the current working directory. Ask the
+agent to analyze the data, produce summaries, or create new output files. For
+example::
+
+    Please process the sales.csv file by first filtering it to only North region
+    sales, and then calculating the sum of sales by person. I'd like to write the
+    results of the processing to north_region_totals.csv
+
+When the agent reads ``sales.csv`` it proceeds automatically, but when it tries
+to save ``north_region_totals.csv`` you are prompted to approve the write.
+
+Unused harness features (todos, plan/execute mode, web search) are disabled to
+keep this a simple, conversational data-interaction sample.
+
+Environment variables:
+    FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint URL
+    FOUNDRY_MODEL            — Model deployment name
+
+Authentication:
+    Run ``az login`` before running this sample.
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import FileAccessProvider, FileSystemAgentFileStore, create_harness_agent
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+from console import build_default_observers, run_agent_async
+from dotenv import load_dotenv
+
+DATA_ANALYST_INSTRUCTIONS = """\
+You are a data analyst assistant. You have access to a folder of data files via the file_access_* tools.
+
+## Getting started
+- Start by listing available files with file_access_ls to see what data is available.
+- Read the files to understand their structure and contents.
+
+## Working with data
+- When asked to analyze data, read the relevant files first, then perform the analysis.
+- Show your analysis clearly with tables, summaries, and key insights.
+- When calculations are needed, work through them step by step and show your reasoning.
+
+## Writing output
+- When asked to produce output files (e.g., reports, summaries, filtered data), use file_access_write to write them.
+- Use appropriate file formats: CSV for tabular data, Markdown for reports.
+- Confirm what you wrote and where.
+
+## Important
+- Never modify or delete the original input data files unless explicitly asked to do so.
+- If asked about data you haven't read yet, read it first before answering.
+- Always explain your reasoning and thought process as you work through tasks.
+- Always explain what you learned and what you are going to do next between tool calls, so the user can
+  follow along with your thought process.
+"""
+
+MAX_CONTEXT_WINDOW_TOKENS = 1_050_000
+MAX_OUTPUT_TOKENS = 128_000
+
+
+async def main() -> None:
+    load_dotenv()
+
+    # Resolve the working/ folder bundled alongside this script. The agent reads
+    # the seed data from here and writes any output files back into it.
+    working_dir = Path(__file__).parent / "working"
+
+    # Create the chat client.
+    # For authentication, run `az login` in terminal or replace AzureCliCredential
+    # with your preferred authentication option.
+    client = FoundryChatClient(credential=AzureCliCredential())
+
+    # Create a harness agent with data-analyst instructions. Unused features are
+    # disabled. The read_only_tools_auto_approval_rule auto-approves the
+    # FileAccessProvider's read-only tools, so only write operations prompt.
+    agent = create_harness_agent(
+        client=client,
+        max_context_window_tokens=MAX_CONTEXT_WINDOW_TOKENS,
+        max_output_tokens=MAX_OUTPUT_TOKENS,
+        name="DataAnalyst",
+        description="A data analyst assistant that reads, analyzes, and processes data files.",
+        agent_instructions=DATA_ANALYST_INSTRUCTIONS,
+        file_access_store=FileSystemAgentFileStore(working_dir),
+        auto_approval_rules=[FileAccessProvider.read_only_tools_auto_approval_rule],
+        disable_todo=True,
+        disable_mode=True,
+        disable_web_search=True,
+    )
+
+    # Run the harness console. This sample has no plan/execute mode, so it uses
+    # the default observers (no planning observer) and no initial mode.
+    await run_agent_async(
+        agent,
+        session=agent.create_session(),
+        observers=build_default_observers(),
+        title="📊 Data Analyst",
+        placeholder="Ask me to analyze the data files, produce summaries, or create output files...",
+        max_context_window_tokens=MAX_CONTEXT_WINDOW_TOKENS,
+        max_output_tokens=MAX_OUTPUT_TOKENS,
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())