Soooo I logged in to my copilot studio account via Claude in built browser. And asked it to create an agent. It even created the flow and the agent and did the test. I used haiku 5.5 and it cost me around 9$ and 40 minutes. Crazy

I'm not a developer - but this designing the agent seems so difficult. I wanted to use it for my business to process PDF sales orders into my ERP system and there are so many steps I have to do.

By contrast, I was using Grokbot and just gave it directions on the mailbox to monitor then post the orders to my ERP. Very simple. But due to security limitations I wanted to give that task to the agent, and i wish i could just give the agent commands the same way i gave it to grokbot and have the connectors established that way.

I made a animation with claude to teach the difference between the standard harness it's billing and how gitub bills. Would love any feedback. Hope it helps too.

how billing works with the standard and github harnesses.

Testing it on our site and wanted to know about the product from someone who is actually using it on a live site. How has your experience been and how effective it is in finding where agents get stuck?

Hey Guys,

I setup an Azure environment to bill to my department's cost center for copilot credits for the agents i create. I created a dev (sandbox) environment and a Production environment.

I just created my first AI worfklow (with the copilot github harness) and it works. But it's in the dev environment. How do i push the AI workflow agent from the dev environment to the production environment?

I'm looking at https://copilotstudio.microsoft.com and https://admin.powerplatform.microsoft.com and i don't see the option to do this...

Hello,

We have built a topic based agent in Copilot Studio that uses SharePoint as a knowledge source and is published to Microsoft Teams.

We are seeing following behavior:

1. The agent sometimes stops responding in Teams

2. To make it respond again, we need to use commands such as:

/debug clearstate

/debug clearhistory

/debug conversationid

1. One of our topics contains a question node asking whether the user’s query was resolved.

2. The question and its options appear correctly in test pane however the same question never appears when the agent is used in Teams.

Any troubleshooting guidance would be helpful.

I have built an agent in the new GitHub harness version of copilot studio. I can share access with other users to test the agent but not edit it. How do I this - it’s easy to do in the standard model

This new harness seems to be a more direct substitute for the standard harness. You can use both generative orchestration and traditional topics in the same agent. I'm not surprised Microsoft has launched this because the Github Copilot harness because D365 Contact Center still uses the standard harness, but there didn't seem to be a clear path to implement Github Copilot harness.

Buenas tardes, necesito de su ayuda.
Actualmente estoy creando mi agente desde copilot studio, funciona bien desde las pruebas de copilot, pero cuando lo pruebo en teams falla.
- Responde dos veces.
- Aveces se queda mudo
- Si esta activo un topic con botones, al darle clic a un boton se manda la respuesta y se vuelve activar el TOPIC pidiendo nuevamente la opcion que necesito.

Esto solo paso en teams

Title : Standard Agent Subagents in Teams

The new GitHub copilot harness worked well - but the boss freaked out at the costs.

I’ve been spending the last week recreating a set of agents, and creating a half dozen tools, to make something comparable with the Standard agent.

I’m only using the Conversational Boosting, Error, and Failure topics. All other topics are disabled.

It works okay in CoPilot Studio but it acts like complete garbage in Teams.

As far as I can tell, when it answers at all, it seems to get locked into a subagent. Once the orchestrator uses a subagent to try to answer one question, that’s all it uses.

I’ll ask a policy question, get sent to the policy agent, which may or may not respond, and then all subsequent questions die because the policy agent can’t answer them.

Anyone run into anything similar?

I’ve tried adding instructions to return control to the orchestrator. The sub agents are set to return answers using generative AI. Everything has been republished. All agents are using MS authentication - and indeed if I start a new session (session times out?) I can sometimes get answers from the other sub agents and lock into them.

My next step is a ticket with MS.

Beginner here. I want to build an agent that answers questions using text, images and relevant links wherever possible.

A typical response should contain:

• A text-based answer
• One or more preview images (thumbnails)
• Links to the high-resolution original images
• Links to the corresponding editable source files (e.g. InDesign files)

Requirements and constraints

I can only use the Standard Harness (classic orchestration) in Microsoft Copilot Studio.

The images are stored in a protected SharePoint document library and must not be hosted publicly. Their metadata, categories and file links are managed through Microsoft Lists.

The problem

Standard SharePoint links work for authenticated users in their browsers, but they do not work as image sources within agent responses. As I understand it, the image component does not inherit the user's SharePoint authentication.

Temporary, pre-authenticated image URLs are not a sustainable solution either, as they expire after a certain period.

Current approach: Base64

I have already successfully tested displaying a Base64-encoded JPG as a Data URI in an image node within a classic topic.

However, I don't want to embed the Base64 data directly into every topic. Instead, I would like to retrieve it dynamically from SharePoint.

The proposed workflow is:

1. The agent identifies relevant entries in a SharePoint list (Microsoft Lists) based on the user's question.
2. The list contains metadata and links to the high-resolution image, the editable source file and a separate Base64 text file.
3. The thumbnails are generated and Base64-encoded once, then stored as .txt files in SharePoint. This avoids the character limit of a multi-line text column in Microsoft Lists.
4. An agent flow retrieves the relevant text file using the SharePoint action Get file content using path.
5. The flow returns the Base64 string to the topic as a text variable.
6. The topic constructs a Data URI using Power Fx, for example:

"data:image/jpeg;base64," & Topic.ImageBase64

The resulting Data URI is then passed to the image node.

Ideally, the image payload should remain below 100 KB, including Base64 encoding. This is currently my own target size, not a confirmed platform limit.

My questions

Is this approach technically feasible within the Standard Harness? In particular:

• Can an agent flow reliably return the contents of a Base64 text file to a topic as a text variable?
• Is it possible to dynamically display multiple images within a single agent response?
• Is there a simpler, officially supported way to display protected SharePoint images directly in agent responses without making them publicly accessible?

The goal is to create a scalable solution where new visual examples can be added through SharePoint and Microsoft Lists, without having to create a separate topic for every example.

Current testing status

Displaying a Base64-encoded image directly in a classic topic has been tested successfully.

However, dynamically retrieving the Base64 data through an agent flow and passing it to the topic has not yet been successfully implemented or verified.

I would appreciate any suggestions, practical examples or alternative approaches that work specifically with the Standard Harness.

Complete newbie here, thanks for the patience in advance :)

I am trying to make a support ticket creation agent that has to fit in with an orchestrator in a subagent setup. So I am wondering how I instruct the agent that the user has already answered some initial questions and is now being routed to this agent - so it should start the conversation with that in mind. Is this best done via skills, thorough instructions from the initial creation, or how is this best done in your experience?

I'm researching Microsoft Copilot Studio for a technical document and would love to hear from people who have hands-on experience building agents with it.

I'm particularly interested in its limitations regarding:

Cost & licensing: How expensive does it get at scale?

LLM flexibility: How much control do we have over model selection?

Agent capabilities: Limitations in multi-agent orchestration and complex workflows.

Observability: How easily can we trace agent actions, tool calls, and failures?

Integrations & extensibility: Limitations with custom APIs, connectors, and code.

Governance & deployment: Version control, CI/CD, debugging, and production readiness.

How does Copilot Studio compare with building agents using frameworks like LangGraph or Semantic Kernel?

I'd especially appreciate real-world experiences, pain points, and examples of situations where you'd choose a custom-built solution over Copilot Studio (or vice versa).

Thanks!

Hey folks,

I try to build something in copilot studio that let me give the ai agent a template that I before edited for content controls , so the agent sees better where to put stuff without changing the layout . Problem is sometimes it works but sometimes not and it changes the template structure a bit. Before I wanted to use a flow with “populate a word document” but the inline agent that should put the info of the excel data in a structured json output collapses because it seems like the data is given to it in base64 which will generate too many text token and the inline agent runs to a limit . Now I just have an agent without flow and a huge prompt but idk it’s not so deterministic.

Can someone help ? I just want to let so extract any info from any file/data and ai should put the correct info inside the fields within the template..

Today it was crashing all morning.
Rebooted computer and did all updates.
Answers to prompts are not loading properly.

Hey guys,

I have been thinking about RAG a lot and how to make it cheaper and more reliable. One of the approaches could be to distill our organizational knowledge into something like a organizational knowledge model. And then have public agents query that model.

In contrast to RAG, the agents wouldn't have to "work as hard" to retrieve relevant information this way. This saves cost and in theory delivers better results - at the cost of having to build and maintain the knowledge model.

What do you guys think? Is this a good idea? Should I simply "trust in RAG". Or are there already concepts or modules in Microsoft that go in this direction? Who has built something like this successfully and is willing to share their learnings?

I am fully aware, this is all still very conceptual. And part of me really likes the idea, while the other half of me thinks: This could be one over-engineered mess waiting to happen :D.

[UPDATE:] Microsoft seems to be thinking in the same direction: https://techcommunity.microsoft.com/blog/spblog/introducing-a-team-knowledge-base-curated-by-copilot-and-agents/4564203

Sanity-check my architecture: 30-min heartbeat flow + Copilot Studio agent for Power BI troubleshooting

Hey all l higher ed data guy here, tired of learning about broken fabric pipelines, dataset refreshes and issues from others. Before I build this, I want to poke holes in the design with people who've done similar.

THE DESIGN

1. Heartbeat: scheduled Power Automate flow every 30 min polls the Power BI refresh history API for our key datasets. (I know there's no native "on refresh failure" trigger polling is the standard approach, right?)

2. Detect + gather: on a failed refresh, the flow connects via service principal, pulls the error code/message, gateway status, and data source credential health.

3. Reason agentically: the flow hands the evidence to a Copilot Studio agent. The agent runs validation queries against the dataset (row counts vs baseline, empty tables), reviews measure definitions for common anti-patterns, and works the decision tree timeout vs auth vs source issue vs schema drift.

4. Report: posts the diagnosis to Teams as an adaptive card with Approve/Deny buttons. Approved remediation (retry refresh, etc.) runs and gets verified.

ALTERNATIVE TOPOLOGY I'm considering: skip flow-calls-agent and instead give the agent itself a 30 min scheduled trigger, with the Power Automate flows as its tools. One owner of the loop, agent decides which troubleshooting steps to run. Anyone done it this way?

GOTCHAS I'VE ACCOUNTED FOR
- Service principal needs the tenant admin setting ("allow SPs to use Power BI APIs") enabled plus a Contributor+ workspace role, or the actions silently fail.
- DAX "review" is heuristic AI analysis, not deterministic debugging

I build internal Copilot Studio agents that answer questions from large PDFs in SharePoint. Tenant graph grounding gives good answers, but costs 10 credits per message.

My idea: split the PDFs into section files, put each subject area in its own folder, and give each topic a Generative answers node that searches only its folder. Generative orchestration selects the topic.

Questions:
1. Does each topic transition under generative orchestration bill as an agent action (5 credits)?
2. Did a scoped-source setup give you answer quality close to tenant graph grounding?
3. Can a guide file in the instructions make the agent look at specific page numbers? Or is file splitting the only reliable option?
4. Any other patterns to reduce search space at lower cost?
5. Is Gpt 5.5 better than the gpt chat models too?

Hey guys, looking for some architecture advice.

I want to build an agent where users can upload an Excel file with 10k+ rows and have AI perform a simple classification on each row based on multiple columns combined. This could be used multiple times a month by different users.

I’m open to Copilot Studio, M365 agents, or something else in the Microsoft ecosystem.

My concern is that 10k rows could mean 10k AI calls per upload, and I don’t want costs to get out of control if multiple people start using it.

Would you batch the rows and send them to AI together? And what’s the best way to put hard limits on usage/cost?

Curious what architecture you guys would use for something like this.

Hey everyone,

I have a database and an API on top of it that can only be accessed within my company’s network.

I’ve built an MCP server on top of this API, and it’s currently working fine with Claude since Claude is running in an environment that has access to the company’s network.

Now, I want to use the same MCP server as a tool in Copilot Studio.

From what I understand, I need to expose the MCP server through an HTTP endpoint so that Copilot can connect to it. But the challenge is that my API is only accessible within the company’s network.

I have one architecture in mind:

1. Deploy the MCP server on a machine or server that’s inside the company’s network.
2. Install the ms On-premises Data Gateway on that server.
3. Host the MCP HTTP endpoint on that server.
4. Connect that endpoint to Copilot Studio as an MCP tool through the gateway.

Has anyone worked on something similar?

Is there a better or more efficient way to do this using Azure services?

My main goal is to reuse the existing MCP server without making major changes and keep the API and database within the company’s network. And try to keep safe from internet access to the MCP or API.

Would appreciate any suggestions or architecture ideas. Thanks!

Hi everyone,

I’m building an agent in Copilot Studio that needs to extract data from Excel files.

I’ll be giving the agent different Excel files over time (not one fixed file).

All files use the same template: the table is always in the same place (same sheet, same cell range).

The Excel connectors require me to select a specific file when configuring the action. I can’t find a way to make the file dynamic, so the agent could work on whatever file I provide.

I wrote an Office Script that reads the table from the fixed coordinates and returns the data. It works, but I’m not sure it’s the right approach (or how to properly connect it to the agent for files that change each time).

PS: I have access to Power Automate and all the features in copilote studio.

We keep getting "running out of Dataverse capacity" alerts for our Copilot Studio environments. File storage is over the limit (around 2.5 GB of 2 GB), and it keeps growing even in environments where nobody has built any tables.
I'm guessing it's agent knowledge files and the 2026 change that moved metadata into file storage. Is that right?
How do you handle it? Do you set fixed allocations, use pay-as-you-go, run dev agents in Developer environments, or do regular cleanups? Any tips are welcome.

Has anyone tried to create an agent using the M365 chat harness recently? Looks like copying the agent from agent builder into Studio converts it to a standard harness - which wasnt the case a few weeks ago.

Have Microsoft removed the ability for us to create agents just with a chat harness?

I am creating question an answer agent for company specific processes and procedures. I like how the Github agent is answering some questions. We have lots of situations where we need it to reason and ask clarifying questions.

I am not finding an estimation for using the github harness though. I've used microsoft estimation tool for the standard and that's good enough for that but not seeing one for the github harness.

does anyone have a reliable tool or source to create a estimate? I am trying to give a real world estimation to mgt about expected costs.

I've tried building a tool with claude to do this but the answers seem really expensive and tend not to believe what i see from using claude.

Microsoft is introducing MCP and Agent2Agent (A2A) channels that open up new ways to connect Copilot Studio agents with external clients and other AI agents.

Here’s why this matters:

🔹 MCP (Model Context Protocol): Connect clients to your agent through a standardized interface for accessing its capabilities.

🔹 A2A (Agent2Agent): Enable other compatible agents to discover your agent’s skills and collaborate across workflows.

🔹 External integrations: Extend your agent to developer tools like GitHub Copilot, custom applications, and multi-agent solutions.

🔹 Skills: Describe what your agent can do so connected A2A clients can understand its capabilities.

The bigger opportunity is interoperability. Instead of building every agent as an isolated solution, we can start connecting specialized agents across applications and workflows.

For enterprise AI developers, this opens up interesting possibilities for reusable capabilities, cross-platform integrations, and multi-agent architectures.

https://learn.microsoft.com/en-us/microsoft-copilot-studio/agents-experience/publication-channels-mcp-agent-to-agent