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Tool guide
Azure AI Foundry
Defines source-backed project, model catalog, deployment, prompt, agent, evaluation, content filter, quota, identity, network, and monitoring review, evidence handling, and action boundaries.
Read the fileOverview
Azure AI Foundry Overview
source-backed tool bundle for project, model catalog, deployment, prompt, agent, evaluation, content filter, quota, identity, network, and monitoring review, evidence reconciliation, reviewable decisions, and.
Read the fileWorkflow
Azure AI Foundry source-backed Triage
Inspect-first workflow for project, model catalog, deployment, prompt, agent, evaluation, content filter, quota, identity, network, and monitoring review.
Read the fileTemplate
Azure AI Foundry review brief
Review-ready artifact for project, model catalog, deployment, prompt, agent, evaluation, content filter, quota, identity, network, and monitoring review, evidence quality, verification, and controlled actions.
Read the fileIs this bundle right for your task?
Who it is for
- People who configure, operate, integrate, govern, or review work performed in Azure AI Foundry
- Teams working in Technology, Business operations
When to use it
- A proposed Azure AI Foundry configuration or workflow change needs current IDs, permissions, dependencies, tests, and rollback evidence.
- A report, export, integration, or automation result needs to be reconciled against actual workspace state and current product documentation.
What you need to provide
- The product version or workspace scope, relevant configuration or export, desired outcome, permissions, and accountable owner.
- Current IDs, settings, records, logs, screenshots, integration details, and test evidence needed to verify the requested change.
Tasks and expected outputs
Questions it helps answer
- Prepare an azure ai foundry review brief without fabricating local facts.
- Separate verified, provided, assumed, and missing evidence.
- Produce a review-ready decision with explicit verification and approval boundaries.
What it helps produce
- Azure AI Foundry review brief
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Provide the product version or workspace scope, relevant configuration or export, desired outcome, permissions, and accountable owner. Ask the agent to review Azure AI Foundry and produce Azure AI Foundry review brief that maps configuration evidence, dependencies, permissions, tests, rollback, and actions that still require approval. Begin with Microsoft Learn — Ai Foundry / What Is Azure Ai Foundry, then confirm that the reference is current and applicable. Inspect Azure AI Foundry before drafting.
Context path: bundles/tools/azure-ai-foundry
What the bundle includes
Tools
- Azure AI Foundry
Frameworks
- source-evidence matrix
- Azure AI Foundry application matrix
- qualified-review gate
Evaluations
- Azure AI Foundry source verification check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Changing live configuration, records, permissions, automations, integrations, or shared data without verified scope, testing, rollback, and approval.
Known limitations
- Use the listed authoritative or identified source surfaces for general Azure AI Foundry guidance; local facts, configuration, records, values, states, permissions, and results require inspected evidence.
- Task-specific work requires current evidence for Azure tenant, subscription, region, Foundry project, model and version, deployment type, quota, endpoint and authentication method, prompts, tools and data connections, evaluation dataset and metrics, content filters, networking, logging, costs, approvals, and rollback.
- Do not infer model availability, output quality, safety, quota, cost, data handling, deployment state, evaluation result, or production readiness.
Safety notes
- Minimize personal, customer, employee, financial, credential, security, privileged, health, student, and other sensitive data.
- Require explicit confirmation before actions that deploy or invoke models or agents, enable tools, change content filters, quotas, identities, networks, endpoints, or data connections; send sensitive data; expose credentials; or incur spend.
- Route legal, privacy, security, compliance, financial, employment, clinical, safety, and other qualified judgments to an evidenced accountable reviewer.