You don't have access to talk to this bot, contact the owner. copilot studio
I created a copilot studio agent and then embedded it inside a code app. It works fine but i am facing the below error. You don't have access to talk to this bot, contact the owner. copilot studio I have provided Microsoft based authentication and am an owner so will have access to the bot. Yet facing the above issue. Is there something i am missing
Copilot Podcast Creations Stuck in “Creating” State
My Copilot podcast creations are stuck in the “Creating” state and will not delete. The delete button is greyed out. I have already tried closing all browsers, restarting my iPad, phone, and PC, and the issue persists across all devices. It looks like the jobs are stuck in the backend queue and cannot be cleared from my side. I also attempted to open a support chat, but the service request auto‑closed before an agent joined. Is there a way to clear these stuck podcast creations on the backend, or can someone from Microsoft confirm if this is a known issue?
Copilot studio Agents published on Sharepoint
Hi everyone, I'm hoping someone can confirm or correct my understanding of a limitation we're experiencing. We have a Copilot Studio agent published to a SharePoint site. When users interact with the agent, they are unable to see their previous chat history — unlike the built-in SharePoint AI agents (e.g. SharePoint Agents / Copilot for SharePoint), which do appear to retain and display conversation history. Is it correct that Copilot Studio agents published to SharePoint do NOT support persistent chat history visible to the end user, while native/built-in SharePoint AI agents do support this out of the box?New Agent experience - how to add Fabric data agent
When using the new (Agent) experience in Copilot studio how can you add Fabric Data agent as connected agent ? There does not seem to be an option. Also should adding it as an MCP server be supported (under Tools) ? https://learn.microsoft.com/en-us/fabric/data-science/data-agent-mcp-server says the following: Currently, you can use the Fabric data agent MCP server only in VS Code. If you're using your own MCP client, it can also work, as long as you set up authentication Has anyone tried this? If yes, by using with authentication? OAuth2? Thanks
Giving AI Agent access to move files between folders
Hi Community, I have built a PDF to Excel reconcilliation AI Agent using Microsoft Copilot that reconciles supplier statements against payables data for our organisation. The agent works well. it reads PDF supplier statements and Excel reports from a SharePoint document library, performs the reconciliation, and produces a structured audit-ready report. However, I am hitting a limitation at the final step. Once the reconciliation is complete, I would like the agent to automatically move the processed supplier statement PDF from its current folder to a subfolder called "reconcilled" within the same SharePoint document library. My question is What is the recommended way to give a Copilot AI Agent the ability to move files between SharePoint folders? Any guidance, documentation links, or examples from others who have implemented similar workflows would be greatly appreciated. Thank you!
Copilot Studio + SharePoint: Markdown (.md) Files in Doc Libraries Supported as Knowledge Sources?
Hi all, We’ve been doing some deeper testing with Copilot Studio agents grounded in SharePoint knowledge sources, and I’m hoping to clarify whether what we’re seeing is a known limitation or an undocumented gap. Scenario A Copilot Studio agent uses SharePoint document libraries as a knowledge source The library contains Markdown (.md) files that are intentionally used as canonical design references The same .md files: ✅ Work well when uploaded directly to the agent ❌ Are not retrievable or citable when stored in a SharePoint library and added as a SharePoint knowledge source To help with grounding, we created modern SharePoint index pages that: Explain what the markdown collections are (Patterns, ADRs, Guardrails) Link directly to the canonical folders and files Explicitly state that the .md files are the source of truth The agent can: Discover and summarize the index pages correctly Understand that .md artifacts exist and where they live But it cannot: Read the content of the individual .md files Apply a specific pattern or ADR from those files in a design conversation Cite them as sources, even when permissions and search indexing are confirmed What We’ve Checked Permissions (agent user has access) Folder depth (kept shallow) Search results (markdown files appear in SharePoint search) SharePoint indexing status Work IQ enabled Same content works when attached directly to the agent This behavior also seems consistent with what others have reported here: Markdown works when uploaded directly Markdown retrieval degrades when hosted in SharePoint libraries Questions for the Product Team / Community Are Markdown (.md) files in SharePoint document libraries officially supported as Copilot Studio knowledge sources today? If yes, are there specific constraints (file size, rendering, parsing, indexing) that differ from Word/PDF? If no (or “not yet”), is this a known limitation on the roadmap? Is the recommended pattern to: Convert important markdown files into .aspx pages, or Use thin “index / summary” pages and keep markdown canonical until retrieval improves? We’re happy to adapt our information architecture — just trying to align with the intended platform direction rather than work against it. Thanks in advance for any guidance or clarification. This capability is extremely powerful, and clearer expectations here would help a lot of teams make the right design tradeoffs.
Copilot Studio Agent resetting when processing PDF drawings (300MB+) via Claude 4.6 Sonnet
Hello everyone, I am building an automated drawing review verification agent inside Copilot Studio using the Claude 4.6 Sonnet model. The goal of the agent is to read a comments package (20-40MB) and verify if those design comments were successfully incorporated into a milestone drawing set (300MB–400MB). When testing this workflow natively within Claude, the model handles the token load perfectly and returns an accurate compliance/incorporation summary within approximately 20 minutes. However, when running the exact same agent setup within Copilot Studio, the conversational canvas repeatedly crashes and resets the session. I suspect I am hitting the 100-second synchronous conversational timeout or overloading the chat runtime payload limits due to the massive file sizes. Because of corporate compliance policies, this agent must live within our Microsoft tenant so it can be scaled across our operations team via Microsoft 365. How can I fix Copilot Studio to have its performance match Claude's, as it is utilizing the same agent model. I am fairly new to working with AI but am willing explore any avenue as if I can figure out a solution this will help save a lot of time for colleagues. Thanks in advance for any insights!
How to use agents response as an input to a topic?
Hi I would like to use the agents reponse to store it in a word or excel file. For that, I would like to use the response provided by the agent, pass it on to my custom topic which triggers an agentic workflow to store the agents response in a word or excel file. I cannot sort out how to pick the agents response and pass it on to my custom topic, any help will be highly appreciated, thanks!
Designing a Governed RTO Compliance Agent Using Copilot Studio and Databricks Genie
Enterprise AI adoption in HR scenarios comes with a unique challenge: how do you deliver actionable insights without compromising privacy, trust, or policy boundaries? In this blog, I’ll share how we built an RTO (Return‑to‑Office) Compliance Agent using Microsoft Copilot Studio and Databricks Genie, focusing on governance‑first design, controlled data access, and real‑world enterprise constraints. This solution was developed as part of an HRLT proof‑of‑value initiative and is designed to support people managers with clear, aggregated compliance insights, delivered conversationally inside Microsoft Teams. The Problem We Were Solving As hybrid work models mature, organizations need a reliable way to answer questions such as: How compliant is my team with RTO expectations? Are there trends across regions or time periods? Traditional dashboards often fall short because they: Require manual interpretation Expose too much granular data Are difficult to govern at scale Our objective was to create an AI‑powered conversational interface that provides: Only manager‑authorized, aggregated insights Zero visibility into individual‑level behavior Built‑in enforcement of HR and privacy policies Architecture Overview The solution integrates Copilot Studio with Databricks Genie, backed by curated data sources. (Image: High-level Copilot Studio and Databricks Genie architecture) Key Components Copilot Studio – Conversational orchestration, policy enforcement, and Teams deployment Databricks Genie – Governed natural-language interface to curated datasets RokFusion Platform – Trusted HR and badge-swipe data This layered approach ensures governance is applied before data is ever queried. Controlled End-to-End Data Flow The interaction pattern follows a strict, auditable flow: A manager asks a question in Copilot Studio Copilot forwards the request to Genie with instruction constraints Genie executes logic only on curated, approved tables Calculations are performed at team or manager level only Copilot formats and returns compliant responses (text, tables, or charts) At no point are employee IDs, badge events, or individual metrics exposed. Using Genie as a Governance Layer, Not Just a Query Tool One of the most critical decisions was to treat Databricks Genie as a policy‑enforcement layer, not merely a natural‑language SQL generator. (Image: Genie instruction configuration enforcing compliance rules) What We Configured in Genie Synonyms and NL mappings for HR terminology Strict filtering logic for employee categories Population threshold enforcement (minimum count) Explicit rejection of sensitive attributes such as gender, race, religion, or age Prevention of formula or row‑level data exposure This approach ensured that even malformed or risky prompts could not bypass policy constraints. Compliance Scenarios Supported The agent supports multiple business‑aligned interpretations of RTO compliance: Hybrid Compliance Hybrid employees counted only on eligible hybrid days Onsite Compliance Onsite employees counted across standard working days All Employees View Weighted aggregation combining hybrid and onsite logic These scenarios are embedded into the agent’s instruction logic, not dynamically inferred at runtime—ensuring consistency and auditability. Why We Chose Conversational AI Over Dashboards A key insight early on was that managers don’t want spreadsheets—they want answers. Instead of navigating filters and charts, managers can ask: “What was my team’s compliance last week?” “Show me a comparison across regions.” When required, the agent can also render simple visual outputs. (Image: Sample Microsoft Teams output with compliance visualization) Importantly, visuals follow the same governance rules as text responses. Publishing and Validation in Microsoft Teams Once configured, the agent was published directly from Copilot Studio to Microsoft Teams, making adoption frictionless. (Image: Publishing Copilot Studio agent to Microsoft Teams) End‑to‑end testing validated: Authorization boundaries Population rules Safe handling of incomplete or ambiguous queries Key Engineering Learnings Governance must be instruction‑driven Relying on frontend filtering alone is insufficient for HR data. Natural language needs strong guardrails Enterprise AI benefits from being constrained, not free‑form. Aggregation builds trust Managers are more comfortable with insights when they know individual visibility is impossible. Copilot Studio accelerates enterprise delivery Security, deployment, and integration stay within the Microsoft ecosystem. Closing Thoughts This RTO Compliance Agent demonstrates how Copilot Studio and Databricks Genie can be used to build governed, enterprise‑ready AI solutions—especially in sensitive domains like HR. By embedding policy into architecture, instructions, and data access, we were able to deliver: Useful insights Strong privacy guarantees High user trust This pattern is extensible well beyond RTO—opening the door for future HR intelligence use cases built on the same foundation.
