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Agent in Azure AI Foundry not able to access SharePoint data via C# (but works in Foundry portal)
Hi Team, I created an agent in Azure AI Foundry and added a knowledge source using the SharePoint tool. When I test the agent inside the Foundry portal, it works correctly; it can read from the SharePoint site and return file names/data. However, when I call the same agent using C# code, it answers normal questions fine, but whenever I ask about the SharePoint data, I get the error: Sorry, something went wrong. Run status: failed I also referred to the official documentation and sample here: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/sharepoint-samples?pivots=rest I tried the cURL samples as well, and while the agent is created successfully, the run status always comes back as failed. Has anyone faced this issue? Do I need to configure something extra for SharePoint when calling the agent programmatically (like additional permissions or connection binding)? Any help on this would be greatly appreciated. Thanks!
Chaining and Streaming with Responses API in Azure
Responses API is an enhancement of the existing Chat Completions API. It is stateful and supports agentic capabilities. As a superset of the Chat Completions class, it continues to support functionality of chat completions. In addition, reasoning models, like GPT-5 result in better model intelligence when compared to Chat Completions. It has input flexibility, supporting a range of input types. It is currently available in the following regions on Azure and can be used with all the models available in the region. The API supports response streaming, chaining and also function calling. In the examples below, we use the gpt-5-nano model for a simple response, a chained response and streaming responses. To get started update the installed openai library. pip install --upgrade openai Simple Message 1) Build the client with the following code from openai import OpenAI client = OpenAI( base_url=endpoint, api_key=api_key, ) 2) The response received is an id which can then be used to retrieve the message. # Non-streaming request resp_id = client.responses.create( model=deployment, input=messages, ) 3) Message is retrieved using the response id from previous step response = client.responses.retrieve(resp_id.id) Chaining For a chained message, an extra step is sharing the context. This is done by sending the response id in the subsequent requests. resp_id = client.responses.create( model=deployment, previous_response_id=resp_id.id, input=[{"role": "user", "content": "Explain this at a level that could be understood by a college freshman"}] ) Streaming A different function call is used for streaming queries. client.responses.stream( model=deployment, input=messages, # structured messages ) In addition, the streaming query response has to be handled appropriately till end of event stream for event in s: # Accumulate only text deltas for clean output if event.type == "response.output_text.delta": delta = event.delta or "" text_out.append(delta) # Echo streaming output to console as it arrives print(delta, end="", flush=True) The code is available in the following github link - https://github.com/arunacarunac/ResponsesAPI Additional details are available in the following links - Azure OpenAI Responses API - Azure OpenAI | Microsoft Learn
Predictions for Artificial Intelligence in next 2-3 years!!!!
2025 - start of agentic AI -Oct 2025: Chatgpt 5 get released (proven to be 10000x times more powerful than chatgpt 4 and can run task automatically) 2026 AI benchmark matches human, beginning of Artificial general intelligence 2027 A new website called letsbuiltai is open source and encourages everyone to train AI. Instead of you training your own AI or an Ai company training their own AI. This would involves everyone training a particular AI simultaneously, paving way for faster and quicker AI growthPush for Rapid AI Growth
There is a key factors of why AI is not growing as quick as speed of light, the reason is because most AI are either built by a specific company (e.g Open AI for chatgpt, Microsoft for Copilot, Google for Gemini). or individuals/small groups building agents for fun or for their workplaces. But what would happen if we merge them together. Imagine, if a website that is own by no one and it is open source and it allows everyone to train the same AI simultaneously at the same time, what would happen. Imagine instead of Microsoft building Copilot, the whole world is building Copilot at the same time, training Copilot at the same time through all global computing power. This would led to an shocking and exponential growth of AI never seen before. This is why I think Copilot should allow everyone to train its AI.Push for hyperrealistic AI Video Generator
I fervently believe that Microsoft must pioneer the development of AI-generated videos. OpenAI has already set the stage with Sora, and if Microsoft doesn't act now, it risks falling behind in the fiercely competitive AI market. This isn't just about keeping pace—it's about leading the charge. Furthermore, the rollout of AI-generated videos must be nothing short of exceptional. These videos need to boast impeccable quality and clearly convey the intended content. Mediocrity has no place in this vision. And let's not forget about preparing Clipchamp for the 2030s. It's imperative to equip it with cutting-edge capabilities that will redefine video creation and editing for the future. Together, these initiatives will not only keep Microsoft at the forefront but will also revolutionize the AI and video landscape.
Azure OpenAI: gpt-5-mini chat/completions streaming returns empty response.
Summary When calling gpt-5-mini via Chat Completions with "stream": true, the server opens the stream but no assistant tokens are emitted and the final JSON is empty (choices: [], created: 0, empty id/model). The same code path streams correctly for gpt-5 and gpt-4o deployments. Also, non-streaming ("stream": false) with gpt-5-mini returns valid content as expected. Environment API: POST /openai/deployments/{deployment}/chat/completions?api-version=2025-01-01-preview Model / Deployment: gpt-5-mini (Azure OpenAI deployment) Date/Time observed: 26 Aug 2025, ~13:00 IST (UTC+05:30) Region: useast2 Note: Same client, headers, and network path work for gpt-5 and gpt-4o streaming. Request Endpoint /openai/deployments/gpt-5/chat/completions?api-version=2025-01-01-preview Body { "messages": [ { "role": "system", "content": "give the best result you can" }, { "role": "user", "content": "Hello" } ], "stream": true } Actual Response (final aggregated JSON after stream ends) { "choices": [], "created": 0, "id": "", "model": "", "object": "", "prompt_filter_results": [ { "prompt_index": 0, "content_filter_results": { "hate": { "filtered": false, "severity": "safe" }, "jailbreak": { "filtered": false, "detected": false }, "self_harm": { "filtered": false, "severity": "safe" }, "sexual": { "filtered": false, "severity": "safe" }, "violence": { "filtered": false, "severity": "safe" } } } ] } Notes: No delta tokens arrive on the SSE stream. No assistant message content is ever emitted. Content filter result is safe across categories. Expected Behavior With "stream": true, server should emit SSE chunks with assistant delta tokens and finish with a populated final message in choices[0].message.content. Azure OpenAI: gpt-5-mini chat/completions streaming returns empty response (choices: [], created: 0) while other models stream fine
Do you have experience fine tuning GPS OSS models?
Hi I found this space called Affine. It is a daily reinforcement learning competition and I'm participating in it. One thing that I am looking for collaboration on is with fine tuning GPT OSS models to score well on the evaluations. I am wondering if anyone here is interested in mining? I feel that people here would have some good reinforcement learning tricks. These models are evaluated on a set of RL-environments with validators looking for the model which dominates the Pareto frontier. I'm specifically looking to see any improvements in the coding deduction environment and the new ELR environment they made. I would like to use a GPT OSS model here but its hard to fine-tune these models in GRPO. Here is the information I found on Affine: https://www.reddit.com/r/reinforcementlearning/comments/1mnq6i0/comment/n86sjrk/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
Using AI to convert unstructured information to structured information
We have a use case to extract the information from various types of documents like Excel, PDF, and Word and convert it into structured information. The data exists in different formats. We started building this use case with AI Builder, and we hit the roadblock and are now exploring ways using the Co-pilot studio. It would be great if someone could point us in the right direction. What should be the right technology stack that we should consider for this use case? Thank you for the pointer.Aidemos Microsoft site doesn't work https://aidemos.microsoft.com/
Hello MS team, I am learning AI-900 in Coursera. The course guides me to try AI demos on https://aidemos.microsoft.com/. But it seems broken for weeks. According to the error message, it could be the issue of the backend. Could the MS team fix it, please? Best Regards, Dale
