Microsoft released the Agent Framework Harness to stable on July 22, 2026 — and if you’re building autonomous agents in .NET or Python, it’s worth understanding what you’re getting and how to approach it. This guide walks through the key concepts, the setup process at a high level, and pointers to the official resources you’ll need to get running.

Accuracy note: This how-to is a conceptual walkthrough. For exact package names, method signatures, CLI commands, and SDK version requirements, always refer to the official Microsoft Agent Framework documentation. MAF is actively evolving, and the docs are the authoritative source for current syntax.

What You’re Actually Getting

Before diving in, it’s worth being clear about what the Harness provides versus what you bring.

What the Harness provides:

  • A durable agent execution loop (so you don’t write your own)
  • Context compaction — automatic summarization when context fills up
  • History persistence across turns
  • Built-in planning and to-do tracking
  • Memory and skills support
  • Web search as a first-class capability
  • Tool approval flows (including “don’t ask again” for trusted tools)
  • OpenTelemetry tracing for production observability

What you provide:

  • Your agent’s system instructions
  • The tools your agent needs (functions it can call)
  • Your choice of underlying chat client / model

The Harness is the scaffolding. You bring the intelligence layer (model + instructions) and the domain logic (tools).

Prerequisites

To use the Microsoft Agent Framework Harness, you’ll need:

  • For Python: Python 3.10 or newer is recommended. Check the official Python quickstart for the exact minimum version requirement.
  • For .NET: The relevant .NET SDK version — consult the official .NET quickstart for the exact version requirement.
  • A model endpoint: The Harness sits on top of any compatible chat client. Azure AI Foundry, Azure OpenAI Service, and compatible OpenAI-protocol endpoints are all supported. Refer to the docs for the full list of supported model configurations.
  • An Azure subscription (optional but recommended): Many of the Harness capabilities, including hosted agent deployment, integrate with Azure services.

Installing the SDK

Installation is via the standard package managers for each language. Refer to the official installation guide for the current package names and version requirements — the SDK is actively releasing updates and the package names should be confirmed from official docs rather than guessing from memory.

For Python, you’re looking at a pip install or uv add command for the MAF Python package.
For .NET, you’re adding a NuGet reference to the relevant MAF packages.

The official docs quickstart section will give you the exact commands.

Core Concepts: What You Configure

Once the SDK is installed, building a harness agent involves a few key configuration decisions:

1. System Instructions

This is your agent’s persona, goals, and constraints — the system prompt that defines what the agent is trying to do. The Harness doesn’t prescribe what goes here; that’s your domain.

2. Tool Registration

Tools are the functions your agent can call. Common examples:

  • File system operations (read, write)
  • Web search (the Harness has a built-in web search capability)
  • External API calls
  • Code execution

Each tool needs a name, a description (which the model reads to decide when to use it), and the function implementation.

3. Tool Approval Configuration

One of the Harness’s standout features is its tool approval flow. For sensitive operations — anything that modifies state, sends messages, or touches external systems — you can configure the Harness to request human approval before executing.

The “don’t ask again” mode lets users whitelist specific tools for trusted operations so the approval prompt doesn’t interrupt every turn. This is a critical production feature for maintaining oversight without crippling throughput.

4. Context Compaction Settings

The Harness manages context automatically, but you can configure compaction behavior: when to trigger summarization, how aggressively to compact, and what to preserve verbatim. For long-running agents, getting this tuned correctly matters a lot for maintaining coherence.

Refer to the Harness documentation on context management for the exact configuration options.

5. Memory Configuration

The Harness supports agent-level persistent memory — information the agent retains across sessions, not just within a single conversation. This is the feature that turns a stateless chat agent into something that can actually build context about its work over time.

Running Your First Agent

At a high level, the flow for running a harness agent looks like:

  1. Initialize your chat client — connect to your model endpoint
  2. Create the harness agent — using create_harness_agent (Python) or HarnessAgent (C#), passing your instructions, tools, and configuration
  3. Invoke the agent — pass a user message or task; the Harness handles the loop, tool dispatch, planning, and compaction internally
  4. Handle approval callbacks — if you’ve configured tool approvals, implement the callback that receives approval requests and routes them to a human or automated policy

The Harness handles the loop itself. You don’t need to write while True: call_model(); parse_tools(); call_tools(); repeat. That loop is the Harness.

Language-Specific Entry Points

Python:

  • Primary class/function: create_harness_agent (exact import path: see official docs)
  • Recommended starting point: Python quickstart in the official docs

.NET (C#):

  • Primary class: HarnessAgent (or the AsHarnessAgent extension method to wrap an existing agent)
  • Recommended starting point: .NET quickstart in the official docs

Both languages provide the same set of capabilities — the Harness is designed for cross-language consistency, which is one of the MAF design goals after the AutoGen/Semantic Kernel unification.

Observability: OpenTelemetry

Every Harness agent emits OpenTelemetry traces by default. This is a significant advantage for production deployments — you get a structured, queryable record of every planning step, tool call, approval request, and context compaction event.

If you’re already running an OpenTelemetry-compatible backend (Jaeger, Honeycomb, Azure Monitor, Grafana Tempo, or any compatible OTLP receiver), harness agents will plug in with minimal additional configuration. Refer to the observability docs for the exact setup.

Where to Go From Here

The official resources you should bookmark:

  1. Agent Harnesses overview — Conceptual documentation with language toggle for Python and C#
  2. Microsoft Agent Framework overview — The full MAF picture
  3. Release announcement blog post — Wes Steyn’s post explaining the design philosophy
  4. GitHub: Microsoft Agent Framework samples — Working code examples (check the repo for the latest samples)

The Harness is production-ready — this is a stable release, not a preview. If you’ve been waiting for the batteries-included layer to mature before committing to MAF for serious agent work, that wait is over.

Sources

  1. Agent Harnesses — Microsoft Learn (learn.microsoft.com/en-us/agent-framework/agents/harness)
  2. The Microsoft Agent Framework Harness is now released — Microsoft Dev Blogs
  3. Microsoft Agent Framework Overview — Microsoft Learn
  4. Microsoft Agent Framework at Build 2026 — Dev Blog

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