Unanswered Questions on GitHub Copilot Harness in Copilot Studio
We're piloting the GitHub Copilot harness in Copilot Studio (GA August 2026) and several operational and architectural details remain undocumented in the GA FAQ, Microsoft Learn, or licensing guides. Looking for official answers or PM contacts on: Architecture & Execution – When the harness breaks tasks into subtasks, does it use internal sub-agents or only skills/connected agents, what are the exact timeout/retry/max-execution-duration limits for long-running workflows, and are planning/context-retrieval/orchestration internals documented anywhere or is the orchestrator a black box? Model Selection – Can individual skills within one agent use different models or is selection strictly agent-level, how are models chosen internally when multiple skills execute, are any internal models developer-configurable, and what's the roadmap for models being added/retired/deprecated plus the lag between public release and Copilot Studio availability? Cost & Token Optimization – How exactly is the ~45% token reduction achieved, how much control do makers have over context/caching/retrieval/tool calls, what are per-model credit consumption characteristics, which models are most cost-effective for specific workloads, and what's the minimum credit cost for trivial interactions? Memory Management – What are retention periods for session/working/agent memory beyond the documented 28-day user-memory expiry, is true long-term memory supported, and what changed versus earlier implementations? Knowledge Retrieval – Can skills or system instructions influence retrieval strategy/document selection/prioritization/filtering, can planning stages perform conflict/duplicate/version detection before retrieval, and how does the harness decide which sources to search? Apps Feature – What is the "Apps (preview)" capability for, when does it GA, and how does it differ from workflows/skills/adaptive cards/agents? Billing & Credit Sizing – Is there a framework to classify users/agents by expected consumption and size credit allocation per group (citizen vs pro developers), and what's the minimum/typical consumption for simple/medium/heavy interactions? Governance & Admin – Can usage limits be set at user level (not just environment/agent), is there an API/IaC path for large-scale credit assignment, can non-admins view their own consumption/remaining allocation, and is there a self-service request-more-credits dashboard? ALM & Environments – What's the recommended path to move harness agents across Dev/Test/UAT/Prod (Solutions/ALM "setup differs" per parity chart—how?), does GitHub integration replace or complement solution-based deployment, are there recommended AgentOps practices for source control/releases/versioning, and what baseline credits and onboarding model are suggested for citizen developers under usage billing—any enterprise reference implementations?
Copilot Studio: “Get file content using path” fails for table-heavy DOCX files
Hi everyone, I'm investigating an issue with a Copilot Studio Standard Harness agent and would like to know whether others have encountered similar behavior. Scenario I have configured a tool in my agent using: Get file content using path The document is stored in my own OneDrive/SharePoint location and I have full access to it. This is not a chat file upload scenario and not a knowledge source indexing scenario. The agent is retrieving the document through a configured tool action. Behavior Observed Case 1 - Works The Word document contains approximately: ~4.1K words ~20 tables The agent successfully: Finds the file Retrieves the file Reads the content Generates a summary Case 2 - Fails I increase the document slightly: ~4.2K words More tables added The agent now returns a message similar to: The file was retrieved, but its content could not be converted to text. The response still indicates that: File found File retrieved Text extraction/conversion failed Important Observation Initially I thought this might be a word-count limitation, but additional testing suggests otherwise. I tested another document containing: More than 4.3K words Very few or no tables The agent processed that file successfully and generated a correct summary. Because of this, the issue appears to be related more to table-heavy DOCX content than to the total number of words. What Has Been Ruled Out The following do not appear to be the cause: File path issues Permissions/access issues File not found issues Connector authentication issues Large file size issues Chat file upload limitations Knowledge source indexing limitations The same tool and same retrieval approach continue working until the document becomes more table-heavy. Questions Has anyone experienced similar behavior with Get file content using path in Copilot Studio? Does Copilot Studio Standard Harness perform an internal DOCX-to-text conversion step after retrieving the file? Are there known limitations around processing Word documents that contain a large number of tables? Has anyone identified practical thresholds around table count, table complexity, or table-heavy technical documents? Is there a recommended workaround besides splitting the document into smaller files? Any insights, similar experiences, or Microsoft guidance would be greatly appreciated. Thanks!
All Copilot Studio Workflow Tools Suddenly Returning HTTP 403 Before Execution
Hello Copilot Studio Community, I am experiencing an authorization issue with multiple workflows connected to an agent built using the Copilot Studio new experience and new Workflows experience. These workflows worked successfully for multiple users yesterday. Today, all workflow tools connected to the agent began returning an immediate HTTP 403 authorization error. I did not intentionally change the agent, workflows, environment, or workflow permissions before the issue started. Error message: You don’t have permission to use this tool. You’re signed in, but access to this resource is blocked. Error details: Authorization - 403 Example error information: Status: Failed Error message: Flow returned HTTP 403 Error code: Http403 Inner error code: NotSpecified Tool duration: Approximately 93 milliseconds Configuration: - Copilot Studio new agent experience - Copilot Studio new Workflows experience - Agent and workflows are in the same Power Platform environment - Workflows use the "When an agent calls the workflow" trigger - Each workflow includes a "Respond to the agent" action - Workflows are saved and published - Agent is saved and published Observed behavior: The problem affects several independent workflows, including: - New-request submission - Current-user identity resolution - Approval decisions - Requester justification - Executive decisions - Fulfillment updates For every affected workflow: - The agent fills the workflow inputs correctly. - The tool call fails almost immediately. - No corresponding run appears in the workflow Activity history. - The workflow trigger is never reached. - No workflow actions execute. Because no workflow run is created, the rejection appears to occur before workflow execution, possibly within the Copilot Studio agent-to-workflow authorization or invocation layer. Troubleshooting already completed: - Confirmed that all workflows are published. - Confirmed that the agent is published. - Tested in a completely new conversation. - Removed an affected workflow tool from the agent. - Saved the agent. - Added the same published workflow back to the agent. - Reconfigured and verified the tool inputs. - Republished the agent. - Confirmed that no workflow Activity run is created. - Confirmed that the issue affects multiple workflows rather than one specific workflow. Removing and re-adding the workflow did not resolve the problem. Questions for the community: 1. Is anyone else currently experiencing HTTP 403 errors when Copilot Studio agents invoke workflows? 2. Is this a known issue or regression in the new Workflows experience? 3. Is there an environment-level or tenant-level permission that controls agent-to-workflow invocation? 4. Could a tenant policy, Conditional Access change, service principal, connection reference, or workflow-sharing configuration cause all workflow tools to fail simultaneously? 5. Where can an administrator find detailed authorization logs when the workflow never creates a run? 6. Has anyone found a workaround for this issue? Any guidance or confirmation from others experiencing the same behavior would be appreciated. I can provide screenshots, complete error details, timestamps, and additional configuration information if needed. Thank you.More powerful agents and workflows for autonomous business processes: Introducing a new harness for Copilot Studio
Over the past two months, we’ve been previewing a new capability within Copilot Studio that lets you build agents capable of taking on more complex business processes. Today, we’re excited to share that this capability is now generally available for production use and to introduce its new name: the GitHub Copilot harness in Copilot Studio. Meet the GitHub Copilot harness The GitHub Copilot harness gives Copilot Studio the coding and reasoning capabilities behind our most advanced agent experiences (like Copilot Cowork and the GitHub Copilot coding agent). This means your agents can now handle work that used to be out of reach, including processes that have many steps, many sources, and ambiguous decision points. The new harness is built for complex, long-horizon work using the latest frontier reasoning models like Opus 5, GPT-5.6 Sol, and Fable 5. It can plan, reason through dynamic problems, run an agentic loop, use skills, integrate workflows, connect to tools and agents in other platforms, and produce rich, multi-part outputs. In our own testing with real world business process evals, Copilot Studio shows significant performance and quality gains when using the GitHub Copilot harness. Improvements in multi-tool use, file analysis, code analysis, and knowledge quality mean these agents are more capable of taking on your most complex business processes. To help you build these more capable agents, we’re also delivering new experiences for makers building agents with the GitHub Copilot harness. The agent designer is more intuitive for authoring, putting the most important tools right within reach to make authoring faster, while retaining full agent lifecycle management features. Then, the workflow designer gives a visual canvas to understand and edit workflows, including adding agent nodes and running workflow evals. And soon, natural language authoring will let you describe your business goal and assemble the right combination of agents and workflows through a multi-turn conversation. Agents running on the GitHub Copilot harness use usage-based billing for all work, regardless of Microsoft 365 Copilot licensing. You pay for your agent’s usage based on the models you choose, the organizational context and tools you add, and runtime used. Certain AI-driven maker experiences, like natural language authoring, evaluations, and testing, will also fall under usage-based billing if building with the GitHub Copilot harness. You can learn more about Copilot Studio usage-based billing here. Continued Support for Copilot Chat and Standard harnesses Adding a new harness does not change the ones you already rely on. Different harnesses can be optimized for different outcomes, so rather than relying on a single one-size-fits-all approach, Copilot Studio now lets you choose the harness that best fits your scenario. This means Copilot Studio supports three harnesses today: The Copilot Chat harness, which uses the same harness as Microsoft 365 Copilot Chat and is ideal for customizing Copilot Chat experiences. The Standard harness, which most agents built in Copilot Studio use today and is great for conversational agents with rules-based topics. The new GitHub Copilot harness, which uses the power of GitHub Copilot SDK to automate complex, agentic business processes. We will continue to support the Copilot Chat and Standard harnesses in Copilot Studio for both existing agents and authoring of new agents. Microsoft 365 Copilot licensed users continue to benefit from fair use of Copilot Chat or Standard harness agents included in their Microsoft 365 Copilot license. For all other usage, the Copilot Chat and Standard harnesses will continue to be billed using the existing fixed rate card. Get started today The GitHub Copilot harness is generally available now, and you can start creating these agents directly from the Copilot Studio homepage. We can’t wait to see what you build. Share your thoughts using the feedback control in the top right corner of the product. Your input directly shapes what we build—and how you build—next. Try Copilot Studio today!
The Hidden Reason Your Foundry Agent Can't Reach Any of Your Private (Bring-Your-Own) Resources
The setup You've built the deployment the enterprise architecture review asked for. Nothing touches the public internet. Your Microsoft Foundry resource is private — public network access disabled, a private endpoint fronting it inside your virtual network. Every resource the agent depends on is locked down the same way: Azure AI Search — private endpoint, public access off. Azure Storage — private endpoint, public access off. Azure Cosmos DB — private endpoint, public access off. An MCP server (say on Azure Functions, Container Apps or APIM) — private, reachable only from inside the VNet. You've delegated a subnet for network injection, stood up Private DNS zones for the Foundry account and every data resource, kept the Foundry resource and the VNet in the same region, and configured network injection at account-creation time like the docs require. On paper, this is a textbook private standard agent setup. Then you run your first agent. The problem: network errors everywhere The agent can't reach anything. And the errors look exactly like what you'd expect from a private-networking problem — connection failures and name-resolution errors reaching Search, Storage, and Cosmos; MCP tool calls timing out. Every message points at the network. (Some of you will see the quieter version of this bug instead: no errors at all, but your Cosmos container, your blob container, and your search index stay stubbornly empty. Same root cause — hold that thought.) So, you do the natural thing. You open Network Watcher and start debugging the network. That's the trap. In the vast majority of these cases, the network is fine. The failure is happening one layer up, in a resource most people barely touch — and it presents as a network problem because of what it does when it's misconfigured. The layer you're probably staring at The network layer is the obvious suspect, and to be fair, private standard agent setup has a lot of moving parts that can genuinely break: Private endpoints and Private DNS zones for the Foundry account and for each data resource, so name resolution stays inside the VNet. A dedicated subnet delegated to Microsoft.App/environments for network injection. Foundry resource and VNet in the same region. Network injection configured at account creation — it can't be added to an existing account afterward. Here's the useful distinction, though, and it's the pivot of this whole post: not every "unreachable resource" belongs to the same layer. The layer that's actually failing: the capability host A capability host is a sub-resource you configure at both the Foundry account scope and the Foundry project scope. Its entire job is to tell Agent Service where to store and process agent data — conversation history, file uploads, and vector stores. Capability hosts let you bring your own Azure resources instead of using the default Microsoft-managed platform resources. This gives you: Data sovereignty - Keep all agent data within your Azure subscription. Security control - Use your own storage accounts, databases, and search services. Compliance - Meet specific regulatory or organizational requirements. If you don't create capability hosts, Agent Service doesn't error out. It quietly uses Microsoft-managed resources for all the communications. That's a fine default for development. The moment you bring your own resources, you're in "standard agent setup," and the contract changes: you now have to declare your resources explicitly, in two places, through connections that resolve correctly — or things break. And here's why it masquerades as a network problem. The capability host resolves each of your resources through a connection, and that connection has to carry enough information to bind to the right resource at runtime. Four properties matter: Property What it must be authType The auth type, e.g. AAD category The resource type: AzureStorageAccount, AzureCosmosDb, or CognitiveSearch target The service endpoint URL — not the resource ID metadata.ResourceId The full Azure resource ID of the resource If target is wrong, or metadata.ResourceId is missing or malformed, the agent tries to reach the wrong endpoint — or can't resolve the resource at all. The symptom you see is a connection or resolution failure. The symptom looks like DNS or a private endpoint. The cause is a bad connection object. This is a resolution problem wearing a **connectivity** costume. Hidden reason #1: There is no inheritance from account to project This is the big one, and it's the reason most private setups fail. It's natural to assume that if you configure things at the account level — the account capability host, account-level connections — everything cascades down to the projects underneath. It doesn't. The project-level capability host is the only thing Agent Service reads to decide which storage, conversation, and vector store resources a project uses. There is no automatic inheritance of BYO resource configuration from the account capability host to the project. Even if your account capability host references every connection perfectly, Agent Service will not use those resources for a project unless that project has its *own* capability host that explicitly references them. Account-level connections are inherited by new projects — but a connection and a capability host are different things. Inheriting the connection only means the project can see it. You still have to create a project capability host that points at it. So the classic failure looks like this: you create the account capability host and your connections, maybe reference them at the account level, and stop — because it feels complete. You never created a project capability host, or created one that doesn't reference your connections. Now Agent Service has no project-level instruction to use your resources, so it either falls back to managed defaults (the silent, empty-container variant) or fails to resolve your resource entirely (the loud, network-error variant). Either way, from your private resource's point of view, the agent never correctly reached it. The fix is the project capability host that ties it all together: PUT https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/projects/{projectName}/capabilityHosts/{name}?api-version=2025-06-01 { "properties": { "capabilityHostKind": "Agents", "threadStorageConnections": ["my-cosmos-db-connection"], "vectorStoreConnections": ["my-ai-search-connection"], "storageConnections": ["my-storage-account-connection"], "aiServicesConnections": ["my-azure-openai-connection"] } } aiServicesConnections is optional — include it only if the project should use models from your own Azure OpenAI resource. And ordering matters: you can't create a project capability host until an account-level one already exists. Account host first, then project host. How to diagnose this in five minutes Before you touch Network Watcher again, run these checks. Capability hosts are managed through the REST API today (no SDK surface yet), so this is all ARM. If required, you can use Azure Cloud Shell: Get Access Token: $TOKEN=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) Confirm the account capability host exists: curl -X GET "https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/capabilityHosts?api-version=2025-06-01" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" | jq . If account capability host doesn't exit, create one: curl -X PUT \ "https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/capabilityHosts/{capabilityHostName}?api-version=2025-06-01" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "properties": { "capabilityHostKind": "Agents", "customerSubnet": "{subnetId}", "threadStorageConnections": [ "my-cosmos-db-connection" ], "vectorStoreConnections": [ "my-ai-search-connection" ], "storageConnections": [ "my-storage-account-connection" ], "aiServicesConnections": [ "my-azure-openai-connection" ] } }' Confirm the project capability host exists — and references the connections you expect. This is the one people are missing. curl -X GET "https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/projects/{projectName}/capabilityHosts?api-version=2025-06-01" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" | jq . Look for threadStorageConnections (Cosmos), vectorStoreConnections (AI Search), and storageConnections (Storage). Empty or missing? That's your bug - the agent has no instruction to use your resources. If project capability host doesn't exit, create one: curl -X PUT \ "https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/projects/{projectName}/capabilityHosts/{capabilityHostName}?api-version=2025-06-01" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "properties": { "capabilityHostKind": "Agents", "threadStorageConnections": [ "my-cosmos-db-connection" ], "vectorStoreConnections": [ "my-ai-search-connection" ], "storageConnections": [ "my-storage-account-connection" ], "aiServicesConnections": [ "my-azure-openai-connection" ] } }' Inspect each referenced connection. Verify authTyp, category, target, and especially metadata.ResourceId. Watch the classic swap: target is the endpoint URL, metadata.ResourceId is the ARM resource ID. Reversing them is an easy hand-authoring mistake, and it produces failures that read as network errors. A correct Storage connection looks like this: { "properties": { "authType": "AAD", "category": "AzureStorageAccount", "target": "https://{storageAccountName}.blob.core.windows.net/", "metadata": { "ResourceId": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Storage/storageAccounts/{storageAccountName}" } } } Run a test conversation and check where the data actually landed. Conversations should appear in your Cosmos DB, uploaded files in your Storage account, and vector data in your AI Search index and communication to private MCP server should work. The gotchas that keep you stuck Even once you know the fix, a few constraints conspire to make this harder than it should be: Capability hosts can't be updated. Wrong connection name? You can't PATCH it — you delete and recreate. Build your automation around delete-then-create, not an idempotent upsert. One capability host per scope. A second one with a different name at the same scope returns 409 Conflict. Same name + same config is idempotent; same name + different config returns a 400. Deleting a capability host affects every dependent agent. Delete the project and account hosts and your agents lose access to the files, conversations, and vector stores they were using. Treat deletion as a breaking change. Permissions. You need Contributor on the Foundry account to create capability hosts, and User Access Administrator or Owner to grant the agent access to your Azure resources. The mental model to take away Split the question in two. Connectivity asks: can the agent reach this resource? That's the network layer — private endpoints, DNS, subnet delegation, the data proxy. It owns your MCP server and any tool egress. Resolution asks: is the project even told to use this resource, and is the connection pointing at the right place? That's the capability host layer. It owns your Search, Storage, and Cosmos DB. They fail in completely different ways, and - this is the whole point - a resolution failure can look like a connectivity failure. When all of your data resources go dark at once, that's your signal it's resolution, not connectivity. No amount of DNS troubleshooting fixes a project that was never wired to your resources in the first place. Match the symptom to the layer, and you'll stop losing days to the wrong one. A 60-second checklist [] Account-level capability host exists. [] Project-level capability host exists (the one everyone misses). [] The project capability host references your Cosmos, AI Search, and Storage connections by name. [] Each referenced connection has a valid metadata.ResourceId. [] target is the endpoint URL and metadata.ResourceId is the ARM resource ID — not swapped. [] authType and category are correct for each resource type. If all of these pass and it still doesn't work — *then* go back to the network. Further reading Capability hosts for Foundry Agent Service Set up private networking for Foundry Agent Service Networking options for Foundry Agent Service Deep dive into Foundry Agent Service networking Standard agent setup Connect to MCP server endpoints for agents Closing Private-by-default is the right posture for enterprise agents — but it moves the failure modes around. The instinct to blame the network is strong precisely because the network is where you spent all your effort. Next time your agent can't reach its private resources, resist that instinct for five minutes and check the project capability host first. More often than not, that's where the day was hiding.Pattern for governing Copilot Studio agents before they reach production
I keep seeing the same gap across Copilot Studio deployments: agents get built and shared before anyone has answered three basic governance questions. Before I call an agent production-ready, I check: -> Who's the sponsor? Not just who built it, who's accountable for it existing. -> What's it grounded on, and who can actually read that data? A knowledge source that's technically "in scope" isn't the same as verified content people should be answering from. -> Is there an audit trail for what the agent said and to whom, if someone asks later? None of this shows up in a demo. It only shows up a few months in, when the agent's still running and nobody remembers why. I wrote a longer breakdown of this governance model, including how Microsoft Agent 365 fits into it, here: https://jpmarquez.com/blog/microsoft-agent-365-licensing-what-you-actually-buy/ Curious what pattern others are using here, especially for the sponsor/ownership question, since that's the one I see skipped most.Looking for ideas: Reducing "Allow" prompts when using SharePoint Knowledge
Hello community members! I'm hoping to get some advice from people who may have come across a similar challenge with their Copilot Studio "SharePoint knowledge retrieval" agents 🤞 Current setup Agent set up in Copilot Studio and published to channels - M365 Copilot and Teams Agent is connected to 4 SharePoint folders (I'm using Method#1 - see below screenshot for reference). This is essential because the documents inside the folders contain both text and image-based content. This is the only method that brings results Users have permissions to access the content in these folders Problem We are currently preparing to roll out this agent but we've hit a user experience issue that is becoming a blocker for moving forward. When a user interacts with the agent for the first time, they are asked to click "Allow" ... 4 times .. one for each of the folder that the agent is connected to This experience feels heavy for users (based on our pilot feedback) and people give up after the first or second time because they think it is not working. In spite of comms and a video about having to repeat this action until done. We want the first time experience to be as smooth as possible to drive adoption. Also, we are going to expand the solution to include more folders soon - so there is also a concern that number of consent prompts could grow further. What I've tried Used Method#2 to connect to SharePoint folders. While this didn't pop up the "Allow" prompts, as indicated earlier, this approach doesn't work because it doesn't return any results from documents as they contain both text and image content. Tried Method#1 to connect to the library (and use instructions to indicate necessary folders) - this didn't work because the method doesn't allow to connect to libraries. Note - I cannot restructure the library and put all the documents into one folder because that's the way the documents have been maintained for a long time & direct links to the folders are being used across the organization via. emails, decks etc. Looking for advice Has anyone found a way to reduce or eliminate these consent prompts? Are there any other recommendations on using SharePoint as a knowledge source that respects user permissions without these consent prompts? Does anyone know why Method#1 and Method#2 have different user experiences where the first one prompts for consent and the second doesn't?
Fix: Hyperlinks render as plain text in Copilot app (but work in the test pane in Copilot Studio)
My use case SharePoint knowledge-retrieval agent built in Copilot Studio to answer employee questions from internal policy and guidance documents. These documents, in addition to text content, contain embedded hyperlinks to internal apps and resources (intranet pages, expense app, ticketing tools etc.). It was important for these links to be surfaced in the agent response so employees can directly follow the link - so they needed to be clickable hyperlinks, otherwise the answer was effectively incomplete. Note that I am not using specific topics/actions to create generative answers. I'm relying solely on instructions. The problem Hyperlinks from the source documents appeared as clickable links in Copilot Studio test pane, but when the same agent was published and accessed via the M365 Copilot app, the links were displayed as plain text - not clickable. My original instructions told the agent to include hyperlinks in content & I also verified that the hyperlinks in the source documents were all absolute URLs. 🙌 What changes finally worked Keeping the original "include links" directive and adding a strict formatting rule that requires both Markdown formatting and an HTML <a> tag wrapper, with the URL preserved exactly. The final instruction lines I included: If hyperlinks are contained within the source content and related to the answer, ALWAYS include them in the response. ALWAYS format the link as full Markdown and preserve the URLs exactly within an HTML <a> tag. What was the problem Copilot Studio is a forgiving renderer. M365 Copilot app uses a stricter markdown rendered that can supress hyperlinks that don't come be default in the format it expects. Hence, including an explicit hyperlink formatting in the instructions helps. I hope this helps someone and saves a few hours in troubleshooting the issue 😉
Microsoft AI Agent Creator Associate Certificate
Hello everyone, I have a question about the Microsoft AI Agent Creator Associate certification. I’m passionate about artificial intelligence and Microsoft Copilot Studio. I’m currently taking the training course and working toward earning the Microsoft AI Agent Creator Associate certification. My question is: Will earning this certification improve my chances of getting a job at Microsoft? If anyone in this community has earned this certification or has experience with it, I’d really appreciate your feedback. Has it helped you get hired by Microsoft or one of its partners? Thank you in advance for your advice and insights!
