Harness

What this lesson demonstrates

A plain Agent only calls the model and loops over the tools you hand it. An agent harness is the batteries-included scaffolding around that model: a ready-made autonomous pipeline with a function-calling loop, a persistent todo list, plan/execute modes, file and web-search tools, tool-approval heuristics, context compaction, and OpenTelemetry — all on by default and individually switchable. create_harness_agent(client, ...) assembles a fully wired Agent from your chat client; you just configure the parts you want to change.

The core call

return create_harness_agent(
    client=client,
    name="research-agent",
    agent_instructions=(
        "You are a concise research assistant. Break work into a short todo "
        "list, use your tools, and report the final answer plainly."
    ),
    tools=get_stock_price,
    disable_web_search=True,  # no hosted web search for this offline demo
)

Task-specific prompt goes in agent_instructions; general operating rules go in harness_instructions (default DEFAULT_HARNESS_INSTRUCTIONS). Extra tools pass via tools=.

The gotcha

Every default capability has a disable_* flag: disable_todo, disable_mode, disable_web_search, disable_file_memory, disable_file_access, disable_tool_auto_approval, disable_compaction. Compaction only turns on when you supply both max_context_window_tokens and max_output_tokens (or a custom strategy); otherwise it is auto-disabled. For looping, a loop_should_continue predicate (e.g. todos_remaining()) plus a loop_max_iterations cap re-invoke the agent until the predicate says stop.

The Azure / MAF mapping

create_harness_agent wraps a FoundryChatClient (with AzureCliCredential) in the full agentic pipeline and returns a normal Agent. It is stateful across a run, so drive it with a session: session = agent.create_session() then await agent.run(prompt, session=session).

Run it

uv run tutorial/02-agents/15_harness.py — needs Foundry credentials via az login.

Next: Code Act