What is the best file format for an AI agent knowledge base?
This is a best practice sharing the best format for an agent and show you why you should convert your PPT, PDF, WORD into a TXT markdown. I had an issue with my agent, time taken to answer was too long, and usually we spend a lot of time asking: What is the best prompt? Why is my agent slow? Why does retrieval sometimes work and sometimes fail? How can I improve answer quality? But I realised I was asking another question much less often: What is actually the best file format for the knowledge base? PDF? Raw text? Markdown? Pre-chunked text? Semantic sections? Context-enriched text? And more importantly: How much does the format alone affect agent performance? I tried to find a quantified benchmark answering this specific question, with the same agent, same source knowledge and same questions, but different knowledge representations. I couldn't find one that really answered what I wanted to measure. So I decided to run the experiment myself on a real case. My first exploratory tests were already surprising: depending on the representation, the agent could be significantly faster and more accurate, despite working from the exact same source information. So I decided to push the test further. My objective I want to identify, without assumptions and based on actual evaluation data, how a long document should be prepared for an LLM knowledge base so that the agent can retrieve, understand, ground and answer from it as reliably as possible. I focused on five dimensions: Answer quality Retrieval reliability Source grounding / citations Execution time Robustness across single-turn and multi-turn questions The broader question I'm trying to answer is: How should we structure knowledge so that an LLM can retrieve and use it as reliably as possible? The test case I deliberately chose a document that isn't particularly friendly for RAG: a 46-page European regulation, https://eur-lex.europa.eu/eli/reg/2011/1169/oj?locale=fr, on the provision of food information to consumers. The information is distributed across articles, definitions, exceptions, annexes, tables, numerical thresholds and cross-references. That makes it useful for testing retrieval: answering correctly often requires finding a very specific piece of information while preserving enough context to understand how it applies. I used the native PDF as the baseline and created 6 additional knowledge-base representations of the same document: Raw TXT Markdown Chunk-ready TXT RAG-oriented units Semantic TXT Contextual TXT One rule: same knowledge, same agent, same instructions, same questions. Only the knowledge representation changes. The benchmark I used two evaluation sets: 42 single-turn questions testing broad coverage of the document: direct facts, thresholds, exceptions, annexes, lists and cross-references. 5 multi-turn conversations containing 13 questions, to see what happens when a user asks a question and then follows up with things like: "And in this case?" "What are the exceptions?" "And for dietary fibre?" This gave me: 47 evaluated test cases / 55 actual questions per format Across all 7 formats: 329 evaluated conversations 385 user questions executed First results Metric Native PDF Best structured representation Overall pass rate 66.0% 85.1% - Contextual TXT Best single-turn score 69.0% 88.1% - Chunk-ready TXT Multi-turn benchmark 40% 80% - Contextual TXT Multi-turn execution time 14m24 5m54 Total benchmark time 44m31 24m49 The quality gap was already substantial: 66.0% → 85.1% That's +19.1 percentage points while keeping the underlying knowledge unchanged. I also saw a major difference in execution time. On the multi-turn test: 14m24 → 5m54 That's approximately 2.4× faster. Across the complete benchmark: 44m31 → 24m49 Around 44% less execution time. These timings represent the complete agent evaluation pipeline, so they shouldn't be interpreted as pure LLM inference latency. But the difference under identical test conditions is large enough that I want to understand it better. Findings There wasn't one format dominating every benchmark. Chunk-ready TXT scored highest on independent questions: 88.1%, while Contextual TXT performed better across multi-turn conversations and finished with the highest overall score. That may suggest that the way we optimise a document for isolated retrieval isn't exactly the same as the way we should prepare it for conversational retrieval. In the contextual version, I tried to make every section understandable when retrieved independently by keeping useful information around it: Source references Section context Retrieval cues Relevant cross-references For regulatory documents, this seems particularly important. A numerical value retrieved alone can be meaningless without knowing which rule it belongs to, under which conditions it applies, and whether another article contains an exception. Where I am now This remains an exploratory benchmark: One document One domain One agent setup One evaluation framework One run per configuration There are plenty of things I still want to test: repeated runs, retrieval-level evaluation, token consumption, larger knowledge bases, other document types, chunk sizes, overlap, contextual headers, and more. But these first results already convinced me that the preparation of the knowledge base deserves much more attention when evaluating an agent. We often spend hours refining instructions while the same information may behave very differently depending on how it reaches the retrieval layer. Next step I'll share the prompts, knowledge-base formats and evaluation methodology on GitHub so the experiment can be reproduced and challenged. I'll keep enriching the repository as I test new formats, improve the evaluation set and add new results. If people here have ideas, edge cases or formats worth testing, I'd genuinely like to include some of them in the next iteration. What would you test next?
Snippet Files Truncating Data - Excel
Dear community, Background I am looking for feedback regarding a best practice for using Microsoft Excel files as a knowledge source. My data is structured in a tabular format, stored across 3-4 columns in Excel (~500 rows, one sheet). For context, it is a simple mapping file that can be used to query about team responsibilities and who to contact for a specific topic/region/etc. I use this file in parallel with Outlook and Teams tools to escalate topics directly to individuals. I have a skill already created (contacts-responsibilities) that very clearly details the file and how the Agent should behave. Issue In the "developer" preview, I can see the Agent is identifying the correct knowledge source, but the processing technique is resulting in truncated snippet files. After multiple fail-retry loops, the Agent concludes that the data is not present in the file (which it most certainly is). Here are some of the notes the Agent returns: "the snippet seems to be limited to a certain portion of the file" "the search result only returned partial data from the spreadsheet." Ask Any suggestions on what exactly to do here? I have read that issues should only occur when you hit 1500+ rows of data. I could split my data apart, but this would mean more maintenance on my side. Thank you! Any hint is appreciated.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.
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!
New Agent Experience - Adding Excel Tool
Hello everyone, I want an agent in the new Copilot Studio experience to read the latest row from an Excel table and then append a new row. Has anyone successfully configured the Excel Online (Business) tools dynamically in the new Copilot Studio experience? Which values should be passed for Location, Document Library/Drive, and File, etc.? In preview, the agent fails but when I prompt it, it ends up adding the row. If anyone could also guide me to resources to learn about how to configure tools in the new experience, I would even be more grateful. Thank you !
Can we create local and global variable in the new copilot studio
I've recently gotten access to the new Copilot Studio experience and have been exploring the new Skills-based architecture. In the previous experience, we could create global and local variables, making it easy to store information during a conversation and reuse it across topics, actions, and conditions. In the new Copilot Studio, I haven't found an equivalent concept. Is there currently a way to create and manage variables that persist throughout a conversation? If not, what's the recommended approach to storing information collected during a conversation so it can be reused across multiple Skills, Tools, and Agent Instructions without asking the user for the same information again? I'd love to hear how others are handling this in the new experience. Any best practices or workarounds would be greatly appreciated!Token Limit Exceeded? What's Actually Going On and What to Do About It ?
Hi All, Please check out my latest blog on “Token Limit Exceeded” would love to hear your thoughts https://techcommunity.microsoft.com/blog/1c769f9e-c0b0-45a7-af52-fecceca10bb2/token-limit-exceeded-whats-actually-going-on-and-what-to-do-about-it-/4536271
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?
