Skip to content

README.md

Latest commit

 

History

History
75 lines (53 loc) · 3.72 KB

README.md

File metadata and controls

75 lines (53 loc) · 3.72 KB
Raw

What this sample demonstrates

This sample demonstrates how to use a HarnessAgent with the FileAccessProvider to give an agent access to a folder of data files for reading, analyzing, and writing results. The HarnessAgent pre-configures function invocation, per-service-call chat history persistence, in-loop compaction, tool approval, and OpenTelemetry — so the sample only needs to supply the chat client, token limits, custom instructions, a FileAccessStore, and opt out of unused features.

Key features showcased:

  • HarnessAgent — a pre-configured agent that wraps a ChatClientAgent with function invocation, per-service-call persistence, and context-window compaction
  • FileAccessProvider — file access is opt-in; setting HarnessAgentOptions.FileAccessStore to the sample's working/ folder enables the provider's read/write tools
  • CSV data processing — the agent reads sales transaction data and performs analysis on demand
  • Output file creation — the agent can write summaries, filtered data, or reports back to the data folder
  • Streaming output — responses are streamed token-by-token for a natural experience
  • No planning mode — this is a simple conversational sample focused on data interaction

Prerequisites

Before running this sample, ensure you have:

  1. A Microsoft Foundry project with a deployed model (e.g., gpt-5.4)
  2. Azure CLI installed and authenticated (az login)

Environment Variables

Set the following environment variables:

# Required: Your Microsoft Foundry OpenAI endpoint
export AZURE_FOUNDRY_OPENAI_ENDPOINT="https://your-project.services.ai.azure.com/openai/v1/"

# Optional: Model deployment name (defaults to gpt-5.4)
export FOUNDRY_MODEL="gpt-5.4"

Running the Sample

cd dotnet
dotnet run --project samples/02-agents/Harness/Harness_Step03_DataProcessing

What to Expect

The sample starts an interactive conversation with a data analyst agent. The working/ folder contains a sales.csv file with ~50 rows of sales transaction data (date, product, category, quantity, unit price, region, salesperson).

You can ask the agent to:

  1. List available files — "What files do you have?"
  2. Analyze the data — "What are the total sales by region?" or "Which salesperson has the highest revenue?"
  3. Create output files — "Create a summary report as a markdown file" or "Write a CSV with monthly totals"
  4. Search for patterns — "Find all transactions over $1000"
  5. Type exit — to end the session

E.g. try the following prompt 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.

⚠️ Security: avoid tool-name collisions

This sample uses FileAccessProvider.ReadOnlyToolsAutoApprovalRule to auto-approve read-only file access tools. Built-in auto-approval rules match tool calls solely by tool name, so any other registered tool that shares one of the approved names (file_access_read, file_access_ls, file_access_grep) would be silently auto-approved, bypassing the human approval boundary. Ensure no other tool's name collides with the reserved names an auto-approval rule approves.

Sample Data

The included working/sales.csv contains sales transactions from January to March 2025 with the following columns:

Column Description
date Transaction date (YYYY-MM-DD)
product Product name
category Product category (Electronics, Furniture, Stationery)
quantity Units sold
unit_price Price per unit
region Sales region (North, South, West)
salesperson Name of the salesperson