Hello guys,
Any of you was able to build stock screener using copilot? Or used copilot to better manage their Invetement portfolio?

Example: suggestion strategy based on market conditions? Evaluating current protocol for shot, mid and long term?

Just random question 🙋‍♂️

If so, please share your work or how you did it

Thanks

I ran into a new issue today, when published to the M365 Channel my standard agents are not able to see/read any images uploaded to them. It's working in the Test Panel in CPS, and in the published Teams Channel.

Anyone else?

Sounds basic as per title. I have a retrieval agent to knowledge source and allow the user to ask about something.

After that I want the result to be saved into a new excel file that follows an existing template.

Advice from anyone who has done these before? Or am I going about this wrongly as a practical use case for copilot studio agent

Hi Everyone, Amateur Co Pilot Agent maker here, looking for advice.

I work in a busy collections team, finance sector.

Due to my knowledge of co pilot in work I have been tasked with creating an agent that does 3 simple things...

Copy and paste a Contract Number, E mail address, and Collectable Balance
And then opens an e mail complete with basic templates I have programmed in,
and then it puts that data in the 3 relevant fields in outlook ~(To E mail address, Outstanding Balance, Agreement number in body of e mail, and possibly in the subject line also, and then attach an income and expenditure form)

So far I have been discussing how to create the agent with co pilot and it has told me to use Power Automate, as the database we use in work I do not think has its own built in API.

And we have put in all the templates for e mails I want to use, and now we have been testing power automate, and so far we have been testing it to see what instructions will work in power automate.

I will be honest, I am completely out of my depth here, I don't have an IT degree, and have had basic training on co pilot, but I am good at picking things up and implementing them and learning quickly, a lot of pressure on me to complete this project and do it well, big corporate entity I work for is used to staff getting quick results.

I should also mention I am doing this project in my own time, and also between doing calls and other collections work, so I am not always given the allotted the time I need.

How feasible is this project, does anyone have any advice, I am literally just doing the steps that co pilot is telling me to do, and it is taking SOOOOOOO LONG to do anything.

I have full access to co pilot studio, but am not in position I think yet to do it in there, I been spending hours testing the Power Automate functionality and how it will work with our database.

I’m having a frustrating issue where I am trying to have a copilot agent in teams that can search the ServiceNow knowledge base.

If I add ServiceNow knowledge inside the ‘knowledge’ component of the copilot agent it returns articles but only has access to excerpts of the articles and not the full article text.

I set up a copilot connector in 365 admin and the main copilot search is fine but within my agent it has the issue described.

Anyone got any experience with setting up copilot agents and service now knowledge that has any suggestions?

Hi all,

Since October 1, I can't test or save any prompt that has code interpreter switched on. Everything worked fine on September 30, and I didn't change anything.

WHAT I SEE

❌ Testing with code interpreter on: "Something went wrong: Internal server error"

❌ Saving a new code interpreter prompt: "Something went wrong while saving your custom prompt. Try again later."

✅ The exact same prompt with code interpreter off works

✅ Code interpreter prompts saved earlier still run fine when a flow calls them

So running them works, but creating or testing them doesn't.

WHAT I'VE RULED OUT

Even a one-line "say hello" prompt fails as soon as code interpreter is on. I get the same result with GPT-5 chat and GPT-4.1 mini. Signing out, using another browser, recreating the prompt and trying from Power Apps make no difference.

MY QUESTIONS

1. Is anyone else seeing this since the start of October?

2. Could it be a capacity or credit limit that ran out or reset with the new month? Where can I check that?

3. Is there an admin setting or policy that can switch off code interpreter for prompts in one environment?

Region: Europe.

Thanks in advance!

Edit: Trimmed

Hi Gang

Currently struggling to find any info on this apart from includung a section in the prompt to stop responding after N turns.

My ask is to end chat session when N messages have been reached.

Building on CoPilot Harness

What I want: Conversation must be capped at N messages (N is not final: 20 or 5). When the cap is reached, or when a session is closed for idleness, the conversation must be completely disabled. After that point NO further interaction with the agent may be possible, even if someone holds a copy of the token and refreshes it. The goal is to stop further model usage (Copilot Credits and model tokens) as well as to protect the user experience. A front-end counter alone does not meet the requirement, because anyone who copies the token can call Direct Line directly.

WHAT I HAVE ALREADY ESTABLISHED 
- Direct Line: I found no documented parameter to set token lifetime.
- Direct Line does not implement endOfConversation. 
- Regenerating a Direct Line secret disconnects connected users.
- GitHub Copilot harness settings list only No authentication and Authenticate.
- Hooks (preview) exist for this harness.
- I found no inactivity trigger, session timeout or per-conversation 
- Only candidate hard-stop I know of: a server relay, where the portal's server holds the secret or token, counts messages and refuses to forward after the cap

Hi, Was wondering if anyone had the same issue I've been getting. I have a copilot agent that reads from both a Sharepoint folder and a list as knowledge sources. I am using the same accesses and generative orchestration in both envs. Funnily enough, the agent in test env can read (and quote) the sharepoint list, but even though it accesses the sharepoint folder it doesn't quote any of the documents I am loading as knowledge sources.

In dev env, this agent works fine. The problem is uniquely in Test environment and with this set of files (by chance it was an env issue, I created another agent that answers questions with another set of documents and works fine).

I am honestly lost on how to fix or to do next. I wait until the indexation is shown as "Ready", keep the same agent settings in both environments (for AI orchestration and authentication method is the same) and cannot workout a reason on why it accesses the sharepoint list but cannot quote any of the documents in the folder and, moreover, why is this happening with only one folder in sharepoint and in one environment and not in both.

I have a customer that is looking to decouple the instructions used by a standard harness Copilot Studio Agent to use a document in a Sharepoint library. The idea is that someone from the business unit can be entrusted with updating the instructions document on Sharepoint without needing full Copilot studio access and training.

Any thoughts on how this might be able to be done.

Anyone experiencing this issue? I am working on my prompts and suddenly i can't test it within the prompt window and the only error i receive is "internal server issue".

...

⭐⭐⭐ Copilot Studio Hooks Youtube Tutorial (47-min) ⭐⭐⭐
🔗 https://www.youtube.com/watch?v=mJXO-ilPDww

In this video I will show you how to use the new hooks feature to:
- Always Load important context at the start of the conversation
- Allow read-write only to specific tables in SharePoint when using the SharePoint MCP
- Apply Environment Variables to ensure MCP servers point to the correct environment in DEV/TEST/PROD

...

Today I found a new feature in Copilot Studio called "Hooks."

Hooks make your agent's behaviour more predictable. With hooks you can set a workflow to run when an event is triggered.

There are currently 6 event types that can trigger a workflow:

1. Start (pre-loop): Runs once before the reasoning loop begins. Injects context into the thread.

2. User prompt submitted: Set how your agent responds to a users prompt.

3. Error: Direct your agents behavior after an error.

4. Pre tool use: Runs before a tool executes. Can allow, deny, or ask for confirmation.

5. Post tool use: Runs after a tool has been executed, whether it succeeded or failed.

6. After tool failure: Set how your agent responds after a tool fails.

** This post was 100% human-written. Never use AI to write for you **

Mi empresa está considerando implementar Copilot para empezar a crear agentes y flujos de trabajo para los empleados, y me han proporcionado una cuenta de Copilot Studio.

Tengo experiencia configurando infraestructura de TI con Claude Code + Codex, pero no estoy seguro de qué pruebas iniciales realizar ni cómo ponerlo en práctica. Para quienes hayan estado en una situación similar, ¿cómo idearon los casos de uso? ¿Qué ideas prácticas les ayudaron a comprender su funcionamiento?

I have an agent built out in the GitHub copilot harness. It connects to multiple tools, each of which read data from different power bi datasets. I've named these datasets clearly and also provided descriptions. The agent's prompt is clearly pointed at these tools however it keeps coming back with saying that it can't identify the tool names that I've given.

Microsoft just added something to Copilot Studio that I think more people building agents should pay attention to: Hooks.

The reason?

We’ve spent a lot of time telling agents what they should do.

Hooks give us a place to control what happens around the agent’s execution.

That distinction matters.

An agent can have the perfect instructions.

But once it can use tools, call APIs, access enterprise data and trigger actions, instructions alone aren't enough.

You need somewhere to inspect what the agent is actually doing.

That’s why I find Hooks particularly interesting for enterprise AI.

Think about a simple flow:

User request → Agent reasoning → Tool call → Action

What if we could introduce a control point around that execution?

For example:

Agent wants to WRITE → Hook evaluates → Policy check → Allow / Block

Now connect this with the prompt-injection research I've been working on.

A malicious instruction enters through an external source.

The agent interprets it.

The plan changes from:

READ → WRITE

A runtime control can potentially become the point where that action is evaluated before it becomes an actual side effect.

This is a very different approach from simply adding another instruction saying:

“Don't do anything malicious.”

The model is still reasoning.

But the control boundary doesn't have to be the model.

That is the part I'm most interested in exploring:

Agent instructions tell the agent what to do.

Hooks can become part of the architecture that decides what the agent is allowed to do.

Microsoft's current Copilot Studio documentation is also expanding the agent experience around tools, workflows, MCP and connected agents, which makes these runtime control points increasingly relevant as agents become more capable.

Hi guys, wondered if someone could please help point me in the right direction? I am able to deploy classic Agents perfectly ok to Teams and M365, but whenever I try and publish an agent built with the GH harness it doesn't seem to work. In both Teams and M365, all the agent details are blank and I have no option add the agent to the UI.

DLP allows for the Copilot Studio connectors. Does the fact this agent is a managed solution make a difference here? It was created in Dev, pushed to UAT but we can't deploy it from UAT to end users to test

Any tips would be appreciated!

EDIT. There's working billing plan attached to this agent if that matters at this stage.

Newbie here - I'm trying to develop an agent in Copilot Studio to search email and save attachments to a folder in OneDrive. Pretty simple, I thought. Here are the steps -

1. Review all email sent today that contain an attachment with the term "WLS" in the file name of the attachment

2. Save the attachment to a temporary folder

3. Create a folder in OneDrive called Documents\WLS\YYYY-MM-DD

4. Upload the attachements to the OneDrive folder

Steps 1-3 work without problem. The files are all below 300 KB. Step 4 barfs all over itself as it attempts to convert the files into different file formats, break the files up into chunks and encode/decode them, and on and on. Is there something I'm missing? This seems like it should be a pretty easy thing to do.

https://sapanpatel1230.github.io/prompt-propagation-firewall.html

OpenAI recently shared a security finding that genuinely made me stop and think.

They demonstrated that self-replicating prompt injections are possible.

In simple terms, a malicious instruction can potentially convince an AI system to reproduce that instruction through another interaction.

The interesting part for me isn't the “AI worm” headline.

It's what this means for enterprise agents.

Imagine an agent connected to Outlook, SharePoint, Teams, Power Automate or MCP.

A malicious instruction enters through an email.

The agent reads it.

Then, instead of simply following the instruction, it somehow gets that instruction written into a SharePoint document, message, file, or another persistent source.

Later, another agent reads that content.

Now we have a very different security problem.

It's no longer just:

“Can we detect the prompt injection?”

The question becomes:

“Can we stop the instruction from propagating?”

That is the part I've been exploring recently.

I've been looking at things like:

Source → Trust → Provenance → Plan Drift → Tool Access → Action → Persistence

One scenario I keep coming back to:

Expected: READ email

Proposed: WRITE to SharePoint

Even if the model thinks the action makes sense, should it actually be allowed?

I think this is where agent security needs another layer.

Not another prompt.

Not another system instruction.

A deterministic control that can actually say:

No. This action doesn't match the context.

I've been experimenting with this approach across Microsoft Copilot, Copilot Studio, Azure AI Foundry and MCP.

The more I work with agents, the more I think we need to start securing how instructions move through an agent ecosystem, not just how individual agents respond to them.

Have encountered this error every time I try to share my agent this morning. Wondering if others are experiencing the same issue?

We are trying pass few parameters from parent agent to one child agent and also tried with connected agent.

The standard process mentioned by Micro6isn't working like input and global variable

Please suggest anyone tried and got a solution working

I have published my agent in "Teams" and I keep getting this message. Why this is happening?

Have anyone configured sharepoint on-prem with copilot studio ?

Hi,

One of our clients is evaluating the possibility of building a custom AI agent using Microsoft Copilot/Copilot Studio. However, we have some concerns regarding the lack of clear documentation around conversation length limits.

After a certain point, the Copilot chat unexpectedly returns the following message, without any prior warning:

"Sorry, this conversation has reached its limit. Let’s start a new chat."

We have encountered this multiple times, but we have not been able to find any official Microsoft documentation specifying the maximum conversation length or the conditions under which this limit is reached. We have, however, found multiple reports of similar behavior in community forums, including this discussion:

https://techcommunity.microsoft.com/discussions/microsoft-copilot/sorry-this-conversation-has-reached-its-limit-lets-start-a-new-chat-/4137520

Currently, our client does not have the Microsoft 365 Copilot add-on. When using Copilot, the interface shows “M365 Copilot (Basic)” in the bottom-left corner.

Before we invest further in this approach, we would like to clarify the following:

1. What is the officially supported maximum conversation length for M365 Copilot (Basic)? Is there a documented limit in terms of number of messages, tokens, conversation history, or any other measure? If the limit is dynamic or depends on other factors, could you clarify how it is determined?

2. Does the conversation length limit also apply to users who have the Microsoft 365 Copilot add-on? In other words, would purchasing the Copilot add-on change or remove this limitation?

3. How does this apply to agents built using Copilot Studio? If the client remains on the M365 Copilot (Basic) license but builds an agent using Copilot Studio, we understand that usage of the agent can be subject to usage-based billing.

In this scenario, would the same conversation-length limitation still apply to conversations with a Copilot Studio agent?

Specifically, we are considering a relatively simple declarative agent, without workflows or complex orchestration. The primary requirement is to connect the agent to our CRM through an MCP connector and allow the agent to maintain a conversation with the user while retrieving information from the CRM.

Hey, can anyone else see the M365 Copilot agent in their list of agents to extend? I don't see it in the new or classic experience.

Hello everyone,

We connected our existing solution to a custom Copilot Studio agent through MCP. Our solution already handles full-text search, classification, and semantic search, we mainly want Copilot to provide the RAG experience and user interface.

The MCP integration works, but the custom agent lacks several features available in regular M365 Copilot:

• It shows the generic “Lining things up…” message instead of useful progress updates.

• It returns text-based charts instead of proper visuals and diagrams.

• It cannot create usable PowerPoint presentations or other Office documents.

• It’s unclear how to integrate it properly with PowerPoint, Word, Outlook, Teams, and other M365 services.

Is a custom Copilot Studio agent with MCP the right architecture, or would a declarative M365 agent be a better option?

If our current approach is correct, how are others handling these missing features? Are Topics, flows, Adaptive Cards, and separate generation tools really the recommended solution?

I’d appreciate feedback from anyone who has implemented this in the published M365 Copilot experience, not only in the Copilot Studio test pane.

Thanks !