Introducing the Logic Apps local CLI: develop Standard workflows from your terminal
We're introducing the public preview of the Logic Apps Standard extension for the Azure command-line interface (CLI). It brings local project creation, connector discovery, connection configuration, runtime management, and execution diagnostics into az logicapp . Why a CLI for Logic Apps Standard? The designer remains useful for understanding and editing workflows visually. The CLI gives you another interface to the development work around that workflow: checking dependencies, discovering operations, validating definitions, starting the local host, and inspecting runs without switching tools at every step. You can use those commands yourself, put them in a script, or let a coding agent invoke them with your approval. Existing Azure CLI app-management and app-settings commands remain a separate part of the same CLI surface. This extension adds Standard developer workflows; it doesn't replace the designer or reinterpret existing resource-management commands. Your coding agent needs more than workflow JSON A coding agent can propose a plausible connector action. Plausible isn't authoritative. The operation identifier, required inputs, and authentication parameters need to come from the connector catalog for your target, not from the agent's recollection of a similar example. That's where deterministic tools matter. The CLI gives the agent a defined operation to execute and evidence to inspect: discover the connector, read its operation schema, author the workflow, validate it, then examine execution results. It reduces guesswork and avoids making the agent reconstruct platform calls it doesn't have available by default. The CLI doesn't make the agent's decisions deterministic, and a successful command doesn't prove your business process is correct. You still review the workflow and its effects. But now that review is grounded on the same type of information the extension and runtime's have. Install the public preview The preview package is logicapp version 1.0.0b1 . It declares Azure CLI 2.67.0 or later and Python 3.10 or later for the interpreter hosting Azure CLI. Install it through the public Azure CLI extension mechanism: az extension add --name logicapp --allow-preview true az extension show --name logicapp --query version --output tsv az logicapp --help For later releases, use az extension update --name logicapp --allow-preview true . Local runtime dependencies are separate from extension installation. What can you do with the extension? The preview registers 59 extension-owned commands. Every group below follows az logicapp ; inherited core commands aren't included. Group Commands Purpose project create Scaffold a local Standard project. context create, use, list, show, update, delete Save target coordinates. workflow create, validate, list, show, export, set, delete, enable, disable Author, inspect, and manage workflows. dependency check, install, uninstall Manage supported local tools. runtime doctor, start, show, stop Diagnose and operate local hosts. connector , connector operation list, show in each Discover runtime connectors and operations. connector managed , connector managed operation list, show in each Discover regional managed APIs. connection create, list, show, test, delete Create local service-provider wiring; inspect and manage deployed connections. connection managed create Create, authorize, and wire a deployed managed connection. connection acl list, show, add, remove Manage connection access policies. identity assign, show, remove Manage deployed app identities. run start, list, show, wait, cancel, resubmit Invoke, observe, and control supported executions. run action list, show, show-content Inspect action results. trigger history resubmit Replay an exact retained trigger event. deployment package Build a deterministic ZIP. test create, plan, run Scaffold, plan, and execute tests, with limitations described below. test results show Inspect test results. Walkthrough: save email attachments to Blob Storage Use Office 365 Outlook as the managed connector and Azure Blob Storage as the in-process service-provider connector. The workflow runs on your workstation; the mailbox, managed connection, and storage account remain in Azure and Microsoft 365. Local doesn't mean offline. Use a test mailbox, a storage container, and an existing non-production Standard app with an assigned identity and valid connections.json . You need permissions for connection creation, consent, access policies, and storage data access. The PowerShell examples use placeholders: replace them before execution. Don't run them against production. 1. Create the project and check the environment From a new solution directory, keep the host under app and create a sibling artifacts folder for exports and packages. az logicapp project create --path .\app --workflow save-attachments --workflow-kind Stateful az logicapp context create --name attachments-local --path .\app --port 7071 --use az logicapp dependency check --path .\app az logicapp runtime doctor --path .\app --port 7071 Install only missing supported tools with dependency install --kind <kind> . The command doesn't install the .NET SDK. Read the doctor's findings: exit code 2 can mean compatibility is unknown, not that everything needs reinstalling. Contexts save coordinates, not your Azure subscription or credentials. 2. Discover both connector catalogs Use az login and check az account show before querying the Azure catalog. These are standard Azure CLI commands. az logicapp runtime start --path .\app --port 7071 --background --ready-timeout 90 az logicapp runtime show --port 7071 az logicapp connector list --local --port 7071 az logicapp connector show --local --port 7071 --connector "<blob-connector-id>" az logicapp connector operation list --local --port 7071 --connector "<blob-connector-id>" --search "upload" az logicapp connector managed show --location "<region>" --connector office365 az logicapp connector managed operation list --location "<region>" --connector office365 --kind trigger az logicapp connector managed operation show --location "<region>" --connector office365 --operation OnNewEmailV3 az logicapp connector operation show --local --port 7071 --connector "<blob-connector-id>" --operation uploadBlob Take the Blob connector identifier from connector list . Runtime catalogs describe the service provider; the regional managed catalog describes Outlook. They aren't interchangeable. Use each family's operation show command to inspect the selected operation before authoring it. This is a useful agent task: "Read both connector schemas. Identify an email trigger, attachment retrieval, and Blob upload. Show me the required inputs and authentication before changing the workflow." The Outlook reference documents attachment retrieval through GetAttachment_V2 ; the Blob service-provider reference documents uploadBlob . 3. Create Outlook access, then wire the local project Stop the discovery host with az logicapp runtime stop --port 7071 before changing settings. Then create the managed connection: az logicapp connection managed create --resource-group "<group>" --logic-app-name "<nonproduction-app>" --name outlook-local --connector office365 --location "<region>" --auth-mode oauth az ad signed-in-user show --query id --output tsv az logicapp connection acl add --resource-group "<group>" --connection outlook-local --object-id "<developer-object-id>" --tenant-id "<tenant-id>" Approve mailbox consent in the browser. Creation also adds the app's access policy and changes the deployed app's connection wiring. The additional policy authorizes your local developer principal. These are real Azure changes, even though the example workflow stays local. Failed creation can retain resources; inspect them and the wiring before retrying. The CLI doesn't write local managed-connector wiring. Read the created entry with connection show against that app and deliberately use --show-runtime-urls for this setup step. Manually merge its API identifier, connection identifier, runtime endpoint, and ManagedServiceIdentity authentication into the local managedApiConnections.outlook-local entry. Keep the runtime endpoint in a private app setting, referenced with @appsetting('OUTLOOK_RUNTIME_URL') ; don't paste returned URLs into the post or shared logs. The documented local identity feature requires the Standard VS Code extension 5.961.19 or later and workflow bundle 1.165.52 or later. Merge this value into both the project's local.settings.json and its workflow-designtime\local.settings.json , preserving existing settings: "WORKFLOWS_AUTHENTICATION_METHOD": "managedServiceIdentity" After completing the connection configuration, start the local runtime as shown in step 5 so it loads the updated settings. Make sure the local runtime authenticates as the developer identity you granted access to the managed connection. 4. Configure Blob access without an account key Read the service provider's managed-identity parameter set and endpoint parameter from discovery. Don't reuse the managed Blob connector's parameter-set name. Substitute those discovered values in this local wiring command: az logicapp connection create --path .\app --name blob-local --connector "<blob-connector-id>" --parameter-set "<discovered-identity-set>" --% --parameter "<endpoint-parameter>=@appsetting('BLOB_ENDPOINT')" authProvider.Type=ManagedServiceIdentity --app-setting BLOB_ENDPOINT=https://<storage-account>.blob.core.windows.net Keep this PowerShell command on one line. The --% marker preserves the app-setting expression through az.cmd ; variables don't expand after it. Review the discovered identity fields before using the command. Grant the local developer Storage Blob Data Contributor at the intended container scope, using your approved Azure role-assignment process. In Azure, grant the app's managed identity its own required access. A workstation doesn't impersonate that identity, and assigning an identity to an app doesn't grant storage permissions. 5. Author and validate the workflow Replace the scaffold's starter definition with Outlook's polling When a new email arrives (V3) trigger, OnNewEmailV3 , an attachment loop, GetAttachment_V2 , and the Blob upload action. Configure the trigger's recurrence and message splitting from its schema. Bind Outlook operations to outlook-local and Blob operations to blob-local . Set includeAttachments to false and retrieve attachments separately. Map each message and attachment identifier explicitly. For uploadBlob , supply containerName , blobName , and content . Decode Outlook's base64 contentBytes before writing binary content; use a unique blob name so repeated filenames don't overwrite unrelated attachments. These are workflow-authoring decisions, not generated business logic from the CLI. For an attachment action named Get_attachment , the upload content expression is @base64ToBinary(body('Get_attachment')?['contentBytes']) . Match action names to your authored definition. Validate offline, restart with the new settings, then validate against that host: az logicapp workflow validate --path .\app --workflow save-attachments az logicapp runtime start --path .\app --port 7071 --background --ready-timeout 90 az logicapp runtime show --port 7071 az logicapp workflow validate --path .\app --all --with-runtime --local --port 7071 Check that the host is running, readiness.managementEndpointReachable is true, and the workflow is registered. Validation doesn't establish connector authorization or business correctness. Static reference warnings can remain after runtime validation and block --fail-on-warning ; don't remove configuration references to manufacture a pass. 6. Send a test email and inspect the run Send a synthetic email with one harmless attachment. Identify the matching execution by timing and message correlation, not by assuming the newest run is yours. az logicapp run list --local --port 7071 --workflow save-attachments --max-items 10 --query "value[].{runId:runId,status:status,startTime:startTime}" --output table az logicapp run wait --local --port 7071 --workflow save-attachments --run-id "<actual-run-id>" --timeout 300 az logicapp run action list --local --port 7071 --workflow save-attachments --run-id "<actual-run-id>" az logicapp run action show-content --local --port 7071 --workflow save-attachments --run-id "<actual-run-id>" --action "<actual-action-name>" --max-bytes 16384 For Stateful runs, run wait exits 0 only after observing success. Its timeout doesn't cancel execution. If outputs are absent, use run action show for failure metadata. Bounded content can still contain private data. Verify the saved blob's bytes independently; a green status isn't an attachment-integrity check. 7. Stop and package az logicapp runtime stop --port 7071 az logicapp deployment package --path .\app --output-path .\artifacts\attachments.zip Verify shutdown, including unshared CLI-owned Azurite processes. Review the package manifest and ZIP: exclude local settings, design-time files, editor content, reports, and private fixtures through .funcignore . Don't confuse it with .gitignore . Packaging is deterministic archive creation, not validation, deployment, or a guarantee that no secrets exist. Add the Logic Apps skill to GitHub Copilot The azure-logic-apps-standard-cli skill is now available on Awesome Copilot. It gives Copilot procedures for using the CLI: inspect the project first, discover connector schemas, preserve existing layouts, prefer managed identity for new connections, and distinguish validation from actual execution. It covers Logic Apps Standard, not Consumption-only development. Install the complete skill package With GitHub CLI 2.90.0 or later, preview the skill and review its bundled references before installing. The following commands install it for your personal Copilot use across projects: gh skill preview github/awesome-copilot azure-logic-apps-standard-cli gh skill install github/awesome-copilot azure-logic-apps-standard-cli --agent github-copilot --scope user For a repository-specific installation, run the install command from that repository with --scope project instead. The installer places the package in the appropriate skills directory. Alternatively, download the complete skill folder and place it under .github\skills in your repository or .copilot\skills in your home directory. Keep SKILL.md , README.md , and all five references files together; copying only SKILL.md leaves required procedures unavailable. The skill is guidance, not the Azure CLI extension or a runtime installer. Install the extension separately as described above; runtime and test dependencies still need their own checks and approval. Use it in Copilot CLI Start copilot from your project directory. If a session is already open, reload skills, then check that the package is available: /skills reload /skills info azure-logic-apps-standard-cli Copilot can select relevant skills automatically. To request this one explicitly, name it in your prompt: Use the /azure-logic-apps-standard-cli skill to inspect this existing Standard project and validate its workflows offline. Preserve its layout, workflow kinds, and authentication. Do not start a host, install dependencies, or access Azure. Report any checks you cannot complete. To approach the attachment walkthrough with Copilot, begin with a plan rather than authorizing every operation at once: Use the /azure-logic-apps-standard-cli skill to plan an Outlook-to-Blob workflow for this project. Identify the connector discovery steps, authentication requirements, and permissions needed. Separate offline work from host startup and Azure changes, and wait for approval before those operations. Review proposed commands and their effects before approving them. The skill requires operation-specific bundled procedures and explicit authorization for cloud changes, replay, and native-test prerequisites; it doesn't replace Copilot's permission controls or prove that a workflow works. See GitHub's Copilot CLI skills guide for installation and usage details. Beyond this walkthrough Use workflow list/show/export against a running host to inspect or save a definition. Against Azure, connection list/show/test , connection acl list , and identity show expose wiring and access. A connection with no available test remains unverified. For compatible callable triggers, run start invokes a workflow; it doesn't manufacture an incoming Outlook email. Cancellation and replay are explicit operations with external effects. Guarded workflow replacement and state changes also need deliberate approval; they aren't transactional shortcuts. Before you build: preview realities and what comes next Commands and outputs can change during preview. Testing commands already provide scaffolding, planning, approved SDK execution, and result inspection, but the current SDK/engine combination has a reported typed-output limitation. They aren't part of this walkthrough. Proposed improvements include testing, local connection inspection, trigger/history discovery, and managed-connection deployment export. These are areas of work, not delivery commitments or commands you can use today. Try it with a workflow you know Take a non-production workflow and ask your coding agent to discover its connectors before editing it. Compare the proposal with the catalog, then inspect the actual execution. If a command is missing or the results don't help you diagnose a failure, tell us which step broke and what you needed instead. Giving us feedback early will help us getting the features ready to you faster!
Amplify Healthcare Intelligence: Data, AI, and Agent-Powered Transformation
Join Microsoft for Amplify Healthcare Intelligence, a webinar and in-person workshop series designed for healthcare and life sciences organizations building the foundation for trusted AI. Every session starts from the same premise: you cannot deliver trusted AI without a trusted data foundation. Across the series you will see how leading organizations unify their data estate, ground AI agents in real business context, and turn that foundation into results they can measure. What You Will Learn How to build a unified, AI-ready data foundation across a fragmented healthcare data estate How to ground AI agents in trusted healthcare data and real business context How to modernize analytics while reducing complexity and cost How to accelerate innovation with Microsoft Fabric, Azure AI Foundry, Microsoft IQ, and Copilot technologies How to deliver measurable impact across clinical, operational, research, and business scenarios Whether you are defining your AI strategy, modernizing your analytics platform, or scaling AI across your organization, these sessions offer practical guidance, real-world customer examples, and hands-on learning to help you move from AI ambition to business impact. Webinars & In-Person Workshops 🎥 Webinars One-hour virtual sessions with actionable guidance, live demonstrations, customer stories, and best practices from Microsoft experts. Each webinar shows how leading healthcare organizations are turning data, AI, and enterprise intelligence into measurable business outcomes. All sessions are from 3-4 ET (12-1 PT) and are free to attend. Date Topic Register Oct 28 3-4 ET (12-1 PT) Reduce BI Sprawl, Cut Cost and Build an AI-Ready Analytics Foundation Register Nov 11 3-4 ET (12-1 PT) Connecting Microsoft Fabric with ExpressRoute: Enterprise Network Architecture Register Nov 18 3-4 ET (12-1 PT) From Question to Visuals: Data Insights with Fabric, Rayfin & GitHub Copilot​ Register Dec 2 3-4 ET (12-1 PT) The Intelligent Healthcare Enterprise: Microsoft IQ in Action Register Dec 9 3-4 ET (12-1 PT) From Trusted Data to Action: Microsoft Fabric + Copilot Cowork for Healthcare & Life Sciences Register Cannot join live? Register anyway. We will send you the recording and session materials after the event. Additional sessions will be added through the end of the year, so check back or register for one session to be notified as new dates are announced. 🏢 In-Person Workshops Our two-day workshops combine executive strategy, healthcare-specific use cases, architecture guidance, and hands-on labs designed to help teams identify and accelerate high-value AI opportunities. Attendance is free. Participants are responsible for their own travel and accommodation, and space at each location is limited. Workshops run 9am to 4pm local time on both days. Day 1: From Healthcare Data to Healthcare Intelligence Day 1 focuses on healthcare transformation strategy, customer examples, and the architectural patterns that make trusted AI possible at scale. The day closes with a networking reception and peer exchange. The Frontier Transformation imperative: from AI ambition to measurable impact Microsoft IQ: turning data into enterprise intelligence Building the unified data foundation Building trusted AI: security, governance, privacy, and compliance Healthcare transformation in action: clinical, operational, research, and finance scenarios Activating data with AI data agents and Copilot experiences Day 2: Hands-On Healthcare AI and Analytics Lab Day 2 is a guided, end-to-end lab. Participants build a working healthcare intelligence solution from raw data through to a grounded AI agent, using Microsoft Fabric, Azure AI Foundry, Copilot technologies, and modern data architectures. Build the foundation: data ingestion, lakehouse architecture, and data engineering Create actionable insights: semantic models and dashboards Prepare data for AI: AI-ready data assets and data governance Build and ground AI agents in trusted enterprise data From insight to intelligent action: planning your organization's next steps What to bring: a laptop with a current browser. Lab environments and credentials are provided on site, and no prior Fabric or Foundry experience is assumed. Date City Venue Register November 10-11, 2026 9am - 4pm Chicago Microsoft Chicago (AON Center) 200 East Randolph Drive, Suite 200 Chicago, IL 60601 Register December 8-9, 2026 9am - 4pm New York Microsoft Garage 300 Lafayette Street New York, NY 10012 Register Who Should Attend This series is built for the people who own the data estate and the people who depend on it. Sessions are technical enough for practitioners and strategic enough for the leaders who prioritize the work. Data & analytics leaders Data platform, data engineering, and business intelligence teams Data architects, engineers, and data scientists AI and innovation leaders Healthcare and life sciences executives Clinical, operational, research, and finance transformation leaders No prior Microsoft Fabric or AI experience is required for any session in this series. Questions Contact Camille Whicker if you have any questions about our upcoming events.Turn GitHub notifications into code with GitHub Copilot in Microsoft Teams
GitHub can now bring repository updates into a channel, group chat, or private chat. When an update needs action, reply to the notification and @mention GitHub with the task you want GitHub Copilot to carry out. GitHub Copilot uses the issue or pull request linked in the notification and the conversation in the thread to understand the task. It can reproduce a bug, run tests, implement a fix, and prepare a pull request for review. Turn an issue notification into a pull request If the player doesn’t load, open the video in a new window: Open video For a bug report, the workflow starts in the notification thread: A GitHub Issue notification arrives in a Teams channel with a link to the reported bug. You @mention GitHub to investigate the issue and prepare a proposed fix in a pull request. The team reviews the pull request, iterates with GitHub Copilot, tests the proposed changes, and decides whether to merge. Starting from the linked issue and thread lets your team delegate the work to GitHub Copilot without rebuilding the request from scratch. The same workflow can be used to implement a feature, expand test coverage, update documentation, and more. Product and engineering teams at Microsoft are already leveraging the GitHub app in Teams to build products today. Get started Explore the new GitHub app in Microsoft Teams (previously called GitHub Notifications). If you already have GitHub Notifications installed, update the app to the latest version. The legacy GitHub app will be renamed “GitHub (deprecated)” and retired on November 16. Learn more at teams.github.com! Turn the updates your team follows into code your team can review.The Context Advantage
A strange thing happens when enterprises upgrade their AI models. The model gets smarter. The benchmarks improve. The demos become more impressive. And sometimes, the business outcome barely moves. The agent still asks questions the organization answered months ago. It recommends something policy doesn't allow. It treats a strategic customer like any other customer. It proposes an architecture your engineering team rejected six months ago. The problem isn't necessarily intelligence. It is ignorance of the business. That distinction matters because the world's most capable models are becoming available to almost everyone. Your competitors can increasingly access the same frontier intelligence you can. What they cannot access is your customer history, operating knowledge, organizational memory or understanding of how decisions actually get made. Models are becoming shared. Context remains proprietary. And that may be where the next enterprise AI advantage is built. Intelligence Without Context is Surprisingly Ordinary Consider a simple enterprise question: Can we offer this customer a larger discount? A powerful model can reason about discounts. But answering that question properly may require the customer's lifetime value, current contract, payment history, margins and approval thresholds and perhaps a conversation yesterday in which the account executive learned that a competitor had entered the deal. The model isn't missing intelligence. It is missing context. Humans rarely make important decisions from one piece of information. We combine data, experience, relationships, history and situational awareness almost unconsciously. Agents need a version of the same capability. That is why enterprise AI is moving from an era dominated by prompt engineering toward one increasingly shaped by context engineering. Satya Nadella recently described the emerging market simply as “a world of context engineering.” Prompt engineering asks: What should I tell the model? Context engineering asks the larger question: What does the model need to understand before it can act well? That difference becomes critical as AI moves from answering questions to performing work. The Enterprise Context Stack In discussions about enterprise agents, “context” can easily become shorthand for giving a model access to more data. That definition is too narrow. I think about enterprise context as a stack: DATA → SEMANTICS → KNOWLEDGE → WORK → MEMORY → JUDGMENT Data tells the agent what happened: orders, transactions, inventory, claims and customer records. Semantics tells it what that data means to the business: what constitutes a strategic customer, how margin is calculated, which assets belong to which production line. Knowledge tells it what the organization knows: policies, contracts, procedures, research and accumulated expertise. Work tells it what is happening now: who is involved, what has been discussed and which commitments already exist. Memory tells it what happened before: previous interactions, decisions, exceptions and outcomes. And judgment helps determine what should happen next. Memory deserves particular attention because it is context agents can accumulate through experience. Microsoft Foundry now distinguishes between session, user and procedural memory - moving agents beyond remembering a conversation toward remembering preferences and learning how work gets done across repeated interactions. Sometimes the cheapest way to make an agent meaningfully better may not be a bigger model. It may be a better memory. Not every agent needs every layer. But the more consequential its responsibility, the richer, and more carefully governed, its context usually needs to become. This is where context stops being a retrieval problem and starts becoming an enterprise architecture problem. Context Is Becoming Infrastructure This shift is now visible well beyond any one technology platform. Gartner increasingly describes a context layer as an architectural foundation for agentic AI - bringing together semantics, operational state, provenance and other enterprise signals so agents can act reliably. Anthropic, approaching the problem from AI engineering, argues that context should be treated as a finite resource that must be carefully curated rather than simply expanded. Microsoft's recent direction is particularly interesting through this lens. Work IQ, Fabric IQ, Foundry IQ and Web IQ increasingly form a shared intelligence layer across Microsoft's stack. Work IQ provides context about how work happens. Fabric IQ brings business semantics and relationships. Foundry IQ provides reusable enterprise knowledge and agentic retrieval. Web IQ extends that understanding with fresh external intelligence. The architectural implication matters more than the product names. Instead of every new agent rebuilding connectors, retrieval logic, semantic definitions and permissions, context becomes reusable infrastructure from which many agents can reason. Microsoft captured the shift particularly well: As agents become the new application model, context becomes critical infrastructure. Your Data Estate Is Not Automatically Your AI Moat For years, organizations have been told their data is a competitive advantage. Sometimes it is. But possessing data and making that data useful to an agent are very different things. Most enterprises don't suffer from a shortage of information. They suffer from an abundance of it -scattered across databases, documents, emails, meetings, applications and people's heads. The advantage doesn't come from connecting an agent to all of it. It comes from turning information into usable context: preserving business meaning, understanding relationships, distinguishing authoritative knowledge from convenient information, respecting permissions and retrieving what matters when a decision is being made. The enterprise with the most data may not build the best AI. The enterprise that makes its context most usable might. The Context Budget There is a trap on the other side too. If context improves AI, the natural instinct is to give the agent as much as possible. That is the wrong objective. Context consumes attention and compute. Irrelevant information creates noise. Stale information can mislead. Conflicting context can degrade reasoning rather than improve it. The objective should therefore not be maximum context. It should be minimum sufficient context the smallest trusted, relevant and permission-aware set of information required to produce the right outcome. In Beyond Tokens, I introduced the idea of an Intelligence Budget: How much intelligence does this outcome deserve? Enterprise agents increasingly need a companion concept. The Context Budget. How much context does this decision deserve? A scheduling agent may need calendars and preferences. A pricing agent may need customer economics, margins, commitments and approval policies. A clinical agent requires an entirely different level of completeness, provenance and governance. Every unnecessary token of context is cost. Every missing piece of necessary context is risk. Which gives us a useful way to think about enterprise agents: Right Intelligence × Right Context → Right Outcome Spend more intelligence where the outcome warrants it. Provide more context where the decision requires it. Do neither indiscriminately. What An Agent May Know Matters Too! There is another dimension to the Context Budget: permissions. Enterprise context belongs to someone. A system that retrieves the right answer but ignores whether the agent is entitled to see it isn't intelligent. It is a security problem. Enterprise context must therefore remain identity-aware and permission-aware as it moves from source systems into agent reasoning. This becomes especially important as agents gain persistent identities and greater autonomy. What an agent may know is as much a part of its job description as what it may do. Autonomy defines the boundary of action. Context defines the boundary of understanding. From RAG Projects To Context Architecture The first generation of enterprise GenAI often started with RAG: connect documents to a model and retrieve relevant passages. That was an important step. But an agentic enterprise cannot become a collection of hundreds of independent RAG pipelines, each separately rediscovering the organization's customers, products, policies, terminology and permissions. That risks creating another generation of technology fragmentation. The opportunity is to move from agent-specific retrieval toward reusable enterprise context. What knowledge should agents inherit? Which business entities should have common definitions? Which sources are authoritative? Which memories should persist—and which should be forgotten? And perhaps the hardest question: Who owns enterprise context? Today, responsibility is scattered across data teams, application teams, knowledge management, security, business functions and increasingly AI teams. In an agentic enterprise, context may need to be managed as a product, not everyone's side project. The Moat Is Not The Model Imagine two competing organizations using exactly the same frontier model. One connects it to fragmented documents and a handful of applications. The other has strong data foundations, shared business semantics, governed knowledge, connected workflows and organizational memory. They have access to the same AI. They do not have the same intelligence system. The second organization's agents understand customers more deeply, interpret signals more accurately and act with greater awareness of how the organization actually works. And that advantage compounds. Every workflow creates more context. Every interaction adds organizational memory. Every decision creates another signal from which future agents can reason. Models depreciate the moment a better one ships. Context appreciates every day you operate. That may be the most important strategic shift of all. Anyone can rent intelligence. No one can rent your context. Intelligence Is Becoming Abundant. Understanding Isn't. The first wave of enterprise AI taught us how powerful models could be. The next may teach us that models were only one part of the equation. An agent needs intelligence to reason. But it needs context to understand your business. As frontier intelligence becomes more accessible, enterprises may discover that their most defensible AI asset was never the model. It was everything the organization had learned before the model arrived: its data, semantics, knowledge, relationships, history, workflows and judgment. The organizations that turn those assets into trusted, reusable context will give every agent they build an advantage from day one. So perhaps the strategic question is no longer simply: Which model should we use? It is: What does our organization know that our competitors don't and can our AI actually use it? Because in a world where everyone can access intelligence, context may be what makes that intelligence uniquely yours. References Microsoft — FY2026 Q2 Earnings Conference Call Microsoft — FY2026 Q4 Earnings Conference Call Microsoft Learn — What is Foundry IQ? Microsoft Foundry — Foundry IQ: Build Smarter Agents Faster with Unified Knowledge and Serverless Retrieval Microsoft Foundry — What's New in Microsoft Foundry | Build 2026 Gartner — Context Engineering Is the New Prompt Engineering Gartner — Market Overview for AI Context Platforms Gartner — The 3 Core Components of the Context Layer for AI AgentsCopilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 29th of October we'll have following agenda: Latest on SharePoint Framework (SPFx) Latest on Copilot prompt of the week PnPjs CLI for Microsoft 365 Dev Proxy Reusable Controls for SPFx SPFx Toolkit VS Code extension PnP Search Solution Demos this time Ranjith D – Build Power Automate Flows and Power Apps from Copilot Studio - Just by Asking Marc André Schröder-Zhou – Designing Adaptive Cards with AI support in SharePoint David Warner - Using Copilot Notebooks - Instant Agentic Experiences & More 📅 Download recurrent invite from https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/m365-powerplat-dev-call-join 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/samples Microsoft 365 & Power Platform community details - https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/home 🧡 Sharing is caring!Copilot, Microsoft 365 & Power Platform product updates call
Copilot, Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Copilot, Microsoft 365 and Power Platform. 📅 On the 27th of October we'll have following agenda: News and updates from Microsoft Together mode group photo Joe Komban - Copilot in SharePoint: Using Custom Plugins for Enterprise AI Rabia Williams - Introduction on Copilot prompt sample gallery Paolo Pialorsi - Understanding Copilot Cowork extensibility 📞 & 📺 Join the Microsoft Teams meeting live at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/samples Microsoft 365 & Power Platform community details - https://un5mzpanxv5t0qg.subtlesoundimmersion.net/community/home 🧡 Sharing is caring!🆕Orchestrate Foundry Prompt and Hosted Agents with Logic Apps Automation
Microsoft Foundry is retiring workflows on December 1, 2026. After that date, the visual workflow designer and running workflows in the Foundry portal won’t be supported. For business process automation that combines deterministic steps with AI reasoning, the Foundry migration guide recommends Azure Logic Apps. Logic Apps is especially well suited to building these processes visually: Foundry agents become steps in a workflow, running alongside built-in actions, APIs, and MCP servers in the same run. Beyond the migration, there’s a broader question many teams face once an agent is working. Most agents start out as interactive, chat-based experiences: a person asks, and the agent responds. Putting that same agent to work in a business process means it has to run autonomously, started by an API call or an event instead of a person, running in the background and at scale. It also has to fit alongside the deterministic steps the process already depends on, such as gathering data before the agent runs and validating and acting on its output afterward. With support for Foundry Prompt Agents and Foundry Hosted Agents, Azure Logic Apps Automation gives you a direct way to do this. Any agent you’ve built in Foundry can become a step in a workflow. It can be started by an API call, a schedule, or an event from one of 1,400+ connectors, run in the background at scale, and sit between the deterministic steps that come before and after it. Before we get to the workflow side, here’s a quick look at the two agent types. If you want the full picture, the Foundry Agent Service overview compares them in detail. What are Foundry Prompt Agents A Prompt Agent is an agent you configure rather than code. You write instructions, pick a model, attach tools, and Foundry runs it. There’s nothing to deploy and no scaling to think about. It can use the Foundry model and tool catalogs, including knowledge sources like Azure AI Search and Foundry IQ. You pay for the tokens and tool calls it uses, with no separate platform charge. If you can write down what the agent should do and which tools it needs, a Prompt Agent will usually do the job, and it’s the quickest way to get something working. The prompt agent quickstart walks through building one. What are Foundry Hosted Agents A Hosted Agent is your own agent code, packaged as a container and run on Microsoft Foundry Agent Service. You can build it with Microsoft Agent Framework, LangGraph, the OpenAI Agents SDK, or your own code. Foundry handles the hosting and scaling. Since it’s your code, you decide how the agent loop works. Each session gets its own isolated sandbox with the CPU and memory you choose, and files and state persist between turns. Each Hosted Agent also gets its own Microsoft Entra agent identity, so you can see and audit what it does like any other identity in your tenant. Hosted Agents use the same models and tools as Prompt Agents. On top of tokens and tool calls, you pay for the compute they use (vCPU and memory hours). You can read more about how Hosted Agents work. A simple way to choose: start with a Prompt Agent. Move to a Hosted Agent when you need custom code or custom orchestration or want to bring your own code and framework. Orchestrate Foundry agents with Logic Apps workflows An agent in Foundry runs when someone or something calls it. Most business processes start somewhere else: an email comes in, a ticket is opened, a file lands in SharePoint, or a schedule fires. They usually end somewhere else too, with a record updated or someone getting a message. Logic Apps covers both ends and everything in between: a trigger to start the agent, deterministic steps around it, and branching, retries, timeouts, and parallel branches for the agent call. The next two sections show what that looks like. Every run shows up in run history, with the inputs, what the agent returned, and what happened next. When something goes wrong, you can see exactly where. The Learn article on automating Foundry agents with workflows covers the setup step by step. Run agents autonomously from any trigger The simplest setup is a trigger, an agent, and an action. Say a support email arrives in Outlook. The workflow sends it to a Prompt Agent that classifies it and drafts a reply, then creates a ticket with the result. This runs every time an email arrives, with no one starting it. Schedules work the same way. A recurrence trigger could call a Hosted Agent every Monday to go through last week’s data and post a summary in Teams. Mix agentic reasoning with deterministic steps Most processes are largely rule-based, with a step or two that needs adaptive reasoning or a decision. In Logic Apps you use an agent for those steps and keep everything else as deterministic steps. Before the agent runs, collect what it needs. Look up the customer in your CRM, pull the contract from SharePoint, or query a database, and pass all of it in. After it runs, check what came back. Validate it against a schema, apply your business rules, and send anything uncertain to a person for approval. Then write the result to the right system with a connector. Use both agent types in one workflow You don’t have to pick one type of agent per process. A workflow can call a Prompt Agent for a quick classification and send only the harder cases to a Hosted Agent for a longer analysis. The workflow passes output from one agent to the next, runs agents in parallel when they don’t depend on each other, and brings the results back together. Each agent stays small, and the end-to-end logic lives in the workflow, where you can see it and change it. How to use Foundry Agents from Workflows Hosted Agent You can create Foundry Hosted Agents directly from your Logic Apps Automation application. Pick your Foundry project and model deployment, and the app takes care of identity, permissions, and readiness checks for you. In a workflow, the Foundry Hosted agent action sends the agent its instructions, task, files, and any workflow actions you’ve added as tools. It returns the agent’s final response and generated files for the next steps to use. For details, see Foundry Hosted Agents for an overview and Create and use a Foundry Hosted Agent for step-by-step setup. https://un5hgz1xtk5y2.subtlesoundimmersion.net/foN9oPu5lRE Prompt Agent You can call an existing Foundry Prompt Agent from a workflow or create a new one right from the designer . Connect to your Foundry project, then either pick an agent from the list or select Create new agent and give it a name, a model, and instructions. The agent is created and hosted in Foundry. You can also give the agent tools from Logic Apps: actions from 1,400+ connectors, built-in actions, APIs, MCP servers, and other workflows. https://un5hgz1xtk5y2.subtlesoundimmersion.net/YkM7dHzHjdM Roadmap Today, workflows can invoke Hosted Agents that you create from your Logic Apps Automation app. We’re adding support for selecting an existing Hosted Agent from your Foundry project and invoking it from a workflow. Get Started Read to automate! Start building at auto.azure.com Automation QuickStart Automation Documentation - https://un5mzrtrgjgpdgnw3w.subtlesoundimmersion.net/docs/
