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Azure AI Foundry

Azure AI Foundry AI Agent
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Overview

A comprehensive platform for building, customizing, and managing AI applications and agents at scale.

Azure AI Foundry is Microsoft's all-in-one platform designed to simplify the development, deployment, and management of AI applications and agents. It offers a rich set of AI capabilities and tools through a unified portal, SDKs, and APIs, facilitating secure data integration, model customization, and enterprise-grade governance. Developers can explore a diverse model catalog, including foundation, open-source, industry-specific, and task-based AI models, to build transformative AI solutions efficiently.

Product overview

Capabilities, pricing and fit

Azure AI Foundry is now documented as Microsoft Foundry, Microsoft's managed platform for building, deploying, evaluating, monitoring, and governing AI applications and agents. It is infrastructure and an agent-development platform, not a single agent, and supports both configuration-only prompt agents and code-based hosted agents.

Information as of August 23, 2026

What it can do

  • Multiple agent development paths

    Teams can create prompt agents managed by Foundry or deploy code-based hosted agents built with supported frameworks or custom code.[2]

  • Unified model and agent APIs

    The Foundry API provides a common contract across model providers, with SDKs for Python, C#, JavaScript or TypeScript, and Java.[1]

  • Deployment, monitoring, and governance

    The portal manages agents, models, and tools with tracing, metrics, model tracking, Azure Policy, identity, and publishing to Microsoft 365, Teams, BizChat, or containerized targets.[1], [3]

Where it fits best

  • Enterprise development teams building governed agents across Azure models, tools, data, identities, observability, and deployment channels.[1], [2]

Pricing and availability

The Foundry platform is free to use and explore, but pricing occurs at the deployment level. Models, agents, tools, hosted CPU and memory, monitoring, and other underlying Azure services have their own billing models.[1], [3], [4]

Platforms: Microsoft Foundry portal, REST API, Python SDK, .NET SDK, JavaScript or TypeScript SDK, Java SDK, Visual Studio Code extension, Microsoft 365, Microsoft Teams[1], [2], [3]

Access: Managed prompt agents, Managed hosted agents from containers, Managed hosted agents from source code, External applications using the Responses API, Containerized application publishing[1], [2], [3]

Important considerations
  • Foundry requires an Azure account, and underlying model, agent, tool, monitoring, storage, and networking services can all add separate charges.[1], [4]
  • Hosted agents require code packaging, deployment infrastructure, permissions, and supported-region availability, unlike configuration-only prompt agents.[2], [3]
  • Managed hosted-agent runtime billing is based on CPU and memory consumed during active sessions, in addition to model and other Azure service costs.[3], [4]
Sources (4)
  1. What is Microsoft Foundry? | Microsoft LearnDocumentation · checked 2026-08-23
  2. What is Microsoft Foundry Agent Service? | Microsoft LearnDocumentation · checked 2026-08-23
  3. Hosted agents in Foundry Agent Service | Microsoft LearnDocumentation · checked 2026-08-23
  4. Plan and Manage Costs | Microsoft FoundryPricing page · checked 2026-08-23

Autonomy level

89/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: Azure AI Foundry demonstrates high autonomy through its ability to automate complex workflows via AI agents that handle tool invocation/data interactions without manual intervention . The platform supports autonomous edge AI processing for real-time decisions , automated scaling for dynamic workloads , and serverless agent management requiring mini...

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Some of the use cases of Azure AI Foundry:

  • Developing and deploying custom AI applications with integrated tools and services.
  • Exploring and evaluating a wide range of AI models for various use cases.
  • Collaborating on AI projects with team members in a secure environment.
  • Scaling AI solutions from proof of concept to full production seamlessly.
  • Ensuring responsible AI practices with built-in safety and compliance features.

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Popularity level: 74%

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