Copilot Studio, Foundry, Fabric Data Agent or Operations Agent? Which one to pick
Most confusion comes from the word "agent" being used for all four. The fastest way to choose is to ask two questions: who builds it (maker or developer) and what triggers it (a person asking, or data changing). Meet the four Copilot Studio is Microsoft's low-code agent builder on Power Platform. You describe the agent in plain language, add knowledge (SharePoint, Dataverse, websites), plug in 1,000+ connectors and Power Automate flows, then publish to Teams, Microsoft 365 Copilot or a website. It is also the easiest place to orchestrate other agents, including Foundry and Fabric agents. Microsoft Foundry (formerly Azure AI Foundry) is the pro-code platform for engineers. You pick from a large model catalog, write orchestration in code, run evaluations, trace every call and deploy behind private networking. Use it when the agent is a product component, not just a chat window. Fabric data agent is a read-only, conversational analyst over your Fabric data. It turns a question into SQL, DAX or KQL against up to five sources (lakehouse, warehouse, Power BI semantic model, KQL database, ontology) and respects each user's permissions. It is generally available. Fabric operations agent lives in Real-Time Intelligence. You give it an Eventhouse, a business goal and allowed actions. It writes its own monitoring rules, wakes up when a condition hits, reasons over the data, and messages people in Teams or runs a Power Automate action. It reached general availability in mid-2026. Which one should I pick? Ask the questions in order and stop at the first "yes". A "yes" does not rule the others out: a data agent or Foundry agent is often published through Copilot Studio. Side by side Copilot Studio Foundry Fabric data agent Fabric operations agent Core job Converse and automate Build custom AI apps and agents Answer questions over data Monitor live data and act Triggered by A person (or event/flow) Your code A person or another agent A data condition Builder Maker, IT, business AI engineer, developer Data/BI team Ops and RTI team Skill level Low-code Pro-code Low-code + good data modelling Low-code, chat-based setup Data it reaches Connectors, SharePoint, Dataverse, other agents Anything you wire up Lakehouse, warehouse, semantic model, KQL, ontology Eventhouse (KQL) streams Writes or acts? Yes, via flows and tools Yes, whatever you code No, read-only Yes, Teams messages and Power Automate actions Where users meet it Teams, M365 Copilot, web, other channels Your app, API, Teams Fabric, Copilot Studio, Foundry, M365 Copilot, MCP Teams chat or channel Billing Copilot Credits Azure consumption Fabric capacity (CU) Fabric capacity (CU) Pick it when You need speed and M365 reach You need model choice, evals, isolation People Real-world scenarios These are the patterns that come up most often in the community. Each names the tool that fits and why. HR policy helper in Teams → Copilot Studio. A 3,000-person company wants staff to ask "How many leave days do I have?" and "Book Thursday off." Knowledge sits in SharePoint, leave balances in an HR system with a connector. A maker builds it in days and publishes to Teams. No developer needed. Insurance claims triage inside a customer app → Foundry. An insurer reads uploaded photos and PDFs, extracts fields, scores fraud risk and returns a decision to its claims system through an API. It needs a vision model, evaluation datasets, tracing and a private network. That is a product feature, so it belongs in Foundry. Sales leaders asking about pipeline → Fabric data agent. "Which regions missed Q3 target, and by how much?" The data agent sits on the sales semantic model, writes the DAX, and only shows rows each person may see. Publish it into Microsoft 365 Copilot or Copilot Studio so leaders never open Fabric. Cold-chain monitoring for a food distributor → Fabric operations agent. Truck temperature sensors stream into an Eventhouse. The goal: "Keep chilled loads between 2 and 5 °C." When a truck drifts, the agent posts in the fleet Teams channel with the truck, route and trend, and can trigger a Power Automate flow to reroute or open a ticket. Retail "what should I do?" assistant → all four together. Microsoft's Zava reference demo shows this: an operations agent spots a stock risk, a Fabric data agent pulls the numbers, Foundry agents for sales and product reason over them, and Copilot Studio is the front door in Teams where the manager approves a reallocation. Better together The strongest designs use more than one. Copilot Studio meets people where they work, Foundry adds custom reasoning, and the two Fabric agents keep answers and actions grounded in governed data. The operations agent is the only one that starts on its own: it reads live events and reaches people in Teams without anyone asking first. Status as of October 2026, and gotchas Fabric data agent is GA, and its Copilot Studio integration went GA at FabCon Barcelona (October 2026). Data agents in Microsoft Copilot Chat were only a sneak peek there. The data agent inside Copilot in Power BI was retired on 26 August 2026. Use the data agent directly, or through Foundry, Copilot Studio, Microsoft 365 Copilot or its MCP endpoint. Operations agent is GA (June to July 2026). Newer abilities, such as root-cause investigations and pre-approved actions, are still in public preview. Operations agents need a paid Fabric capacity. Trial capacity is not supported, and an admin must turn on the Copilot/AI and operations agent tenant settings. A data agent is only as good as the model under it. Clear table names, descriptions, relationships and agent instructions matter more than prompt tricks. An operations agent automates your mistakes too. Start with "recommend" actions that a person approves, then move to automatic actions. Don't pick Foundry just for governance. Both Copilot Studio and Foundry agents now show up in the same Microsoft governance and security tooling. Pick on build style, model needs and network needs. Rule of thumb: prototype in Copilot Studio for a week. If you hit a wall (model choice, evals, isolation, scale), that wall is your Foundry requirement. Sources Operations agent (Generally Available), Fabric Updates Blog Improving operational efficiency with operations agents, Fabric Updates Blog Build, deploy, and govern Fabric at scale (FabCon 2026), Fabric Community FabCon Barcelona 2026 announcements, James Serra Retirement of data agent in Copilot in Power BI, Fabric Community What are Fabric data agents: GA status and limits, Atlan Fabric data agent vs operations agent, Daymark One agent, many channels: querying Fabric data agents, Data Science at Microsoft Copilot Studio vs Microsoft Foundry, Nasstar Zava Real-Time Intelligence demo, Microsoft CDX
Monitoring - enable new experience
we are trying to explore workspace monitoring which need be done by enabling 'new experience' option under monitoring in workspace settings. when we click on the button nothing happens, sometimes it goes to next screen - once we click on Save, shows error saying, 'feature is not available'. did anyone have similar issue?
Workspace Activities 'Monitoring' not working properly
Hi, I'm using the Monitoring option in a workspace to track the actual activity in my Fabric workspace. I've enabled "Log workspace activity", and it correctly created the Eventstream and the KQL database. However, it seems incomplete. When I open the KQL database, I want to see information such as pipeline activities, semantic model refreshes, and other details. But the only data being collected appears to be SemanticModelLogs. According to Microsoft’s documentation, this monitoring setup should also create a table called ItemJobEventLogs: https://learn.microsoft.com/en-us/fabric/data-factory/monitor-pipeline-runs But it seems that this table is not being created or the data is not being collected in my monitoring KQL. Do you have any idea why this might be happening? Is there an additional setup required? PS: This image is from the Microsoft Documentation, in my Monitoring KQL it doesnt have this table.Fabric Data Agents Get Smarter on 2 November. Can You Prove Yours Still Answers Correctly?
Microsoft announced Advanced NL2SQL and advanced DAX generation as generally available on 19 October 2026. The core improvements roll out to the standard runtime by default, together with a move to GPT-5.6 Terra. Separately, the old custom-tool way of calling a Fabric data agent from Microsoft Foundry stops being supported on 6 November. Date (2026) What happens What you should have done 19 Oct GA announced Read this post Now to 1 Nov Changes available on the preview runtime Build a ground-truth set, baseline standard, test preview 2 Nov Standard-runtime rollout begins, no opt-in Baseline frozen and stored 6 Nov Foundry custom-connection integration unsupported Foundry agents moved to Fabric IQ By 10 Nov Rollout expected to complete Re-run, diff the queries, tell affected users What Microsoft actually announced The query generators stop writing in one pass and start investigating first. That is why behaviour can change even where accuracy improves. Until now, NL2SQL and NL2DAX generated a query in a single pass, resolving schema, business definitions and filter values at once. As the GA announcement puts it, a wrong guess still runs, and the wrong answer looks like a right one. Advanced NL2SQL explores the schema, tests joins and looks up real filter values before writing SQL. It works in three modes: Example query following: adapts a relevant example query, for instance by swapping a filter value. Clarifying questions: asks the user when it can't answer reliably. Data exploration: infers logic from your examples and checks assumptions against the data. Advanced DAX generation brings the same idea to semantic models: an agentic tool-calling loop plus instance value indexing, so a user's "California" maps to the stored "CA". Microsoft's internal benchmarks Microsoft's 2026 internal evaluations, standard versus preview runtime. Microsoft notes results vary by workload, model and configuration. Benchmark Smart-answer accuracy P50 latency P95 latency NL2SQL, standard 78.8% 28.4 s 46.8 s Advanced NL2SQL, preview 90.9% 26.0 s 32.9 s NL2DAX, standard 89.15% 39.1 s 97.5 s Advanced DAX, preview 91.86% 20.4 s 34.7 s SQL gains 12.1 accuracy points. DAX gains only 2.7 points, but its tail latency drops by about 64%. If your semantic-model agents felt slow, that is your headline. What moves, and what doesn't Moving to standard: Advanced NL2SQL, advanced DAX generation and the GPT-5.6 Terra model upgrade. Staying in preview: schema descriptions and topics for SQL sources. Two details catch people out (runtime docs): A published agent keeps the runtime it was published with. Publish on preview and it stays on preview until you republish. Check every production agent. Runtime choice doesn't pick the model. A preview run today is a strong signal, not a guaranteed copy of standard after 10 November. Re-run after the rollout regardless. Why "better on average" can still surprise you A benchmark counts right answers. Your users notice changed answers. The second one generates support tickets. Workaround instructions start fighting the agent. Rules written to patch single-pass behaviour may no longer be needed, and unneeded guidance can steer an agent away from a path it would have found itself. Example queries get followed more literally. Good when they are current. An example with last year's fiscal logic now gets reproduced more faithfully. Ambiguous questions get a question back. Better for users, but it breaks anything downstream that expects a number, such as a Copilot Studio flow or a Foundry agent chain. A correct new answer can be a business problem. "Active customers in Q3" returned 41,200 because of a wrong date column; now it correctly returns 38,900. Someone may already have put 41,200 in a board pack. The pass count hides the churn. 40 of 50 pass before, 44 after. Looks like +4. Underneath, 6 newly pass and 2 now fail: 8 changed answers, 2 regressions. Question status Count Action Pass both, same query 34 Nothing Pass both, different query 4 Right for the right reason? Failed before, passes now 6 Tell anyone who acted on the old answer Passed before, fails now 2 Fix instructions or examples first Fail both 4 Existing backlog Illustrative numbers. The structure is what your own run should produce. The pre-rollout routine: six steps for this week You need a Fabric notebook, a lakehouse and the Fabric data agent SDK. The SDK and its evaluation features are in preview, so note the version you used. You also need F2 or higher capacity, the data agent tenant settings enabled and read access to the sources. Step 1: Inventory agents and dependencies For each published agent, record the runtime it was published on, its source types, where it is consumed (Fabric, Teams, M365 Copilot, Copilot Studio, Foundry) and its business owner. Anything called from Foundry via the old custom tool also goes on your 6 November list. Step 2: Build a ground-truth set that covers the risky cases The SDK takes a DataFrame or CSV with question and expected_answer. Aim for 30 to 60 questions per agent, ideally from real usage, covering filter values ("California" stored as "CA"), example-adjacent questions, multi-join questions, ambiguous questions and high-stakes KPI figures. %pip install -U fabric-data-agent-sdk import pandas as pd gt = pd.DataFrame(columns=["question", "expected_answer"], data=[ ["Total net sales in California for September 2026", "1,284,550.20"], ["Returns rate by region for FY26", "North 3.1%, South 2.7%, East 4.0%, West 3.4%"], ["What was revenue last year?", "CLARIFY: should ask gross or net, fiscal or calendar"], ["Active customers in Q3 2026", "38,900"], ]) gt.to_csv("/lakehouse/default/Files/agent_eval/ground_truth_sales.csv", index=False) The values are placeholders. Use answers your business owner has signed off. Step 3: Use a critic prompt that accepts clarifying questions The default grader would mark a good clarifying question as wrong. The SDK accepts a custom critic_prompt with {query}, {expected_answer} and {actual_answer} placeholders. critic_prompt = """ Query: {query} Expected Answer: {expected_answer} Actual Answer: {actual_answer} If the expected answer starts with CLARIFY:, respond 'yes' only if the actual answer asks a clarifying question about the same ambiguity. Otherwise respond 'yes' only if the actual answer is numerically and semantically equivalent. Is the actual answer equivalent to the expected answer? """ Step 4: Capture the standard baseline before 2 November Run against the production stage, which is what users hit today. Give each run its own table. from fabric.dataagent.evaluation import ( evaluate_data_agent, get_evaluation_summary, get_evaluation_details) agent_name = "SalesAgent" baseline_table = "eval_sales_standard_baseline_2026_10" baseline_id = evaluate_data_agent( gt, agent_name, table_name=baseline_table, data_agent_stage="production", critic_prompt=critic_prompt) print(get_evaluation_summary(baseline_table)) baseline = get_evaluation_details(baseline_id, baseline_table, get_all_rows=True) print(baseline.columns.tolist()) # check column names in your SDK version This writes <table_name> with per-question results and <table_name>_steps with the detailed reasoning and step-by-step execution. Look there for the generated SQL or DAX, and keep both tables as evidence. For a single question, the Diagnostics button exports the full steps too. Step 5: Record P50 and P95 latency through the MCP endpoint The evaluation tables focus on correctness, so time each question against the published agent's MCP endpoint. This adapts Microsoft's SDK sample: import time, numpy as np from azure.identity import InteractiveBrowserCredential from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client mcp_url = ("https://api.fabric.microsoft.com/v1/mcp/workspaces/<workspace-id>" "/dataagents/<data-agent-id>/agent") cred = InteractiveBrowserCredential() async def ask(q): tok = cred.get_token("https://api.fabric.microsoft.com/.default").token async with streamablehttp_client(mcp_url, headers={"Authorization": f"Bearer {tok}"}) as (r, w, _): async with ClientSession(r, w) as s: await s.initialize() tool = (await s.list_tools()).tools[0] arg = next(iter(tool.inputSchema["properties"])) t0 = time.perf_counter() await s.call_tool(tool.name, {arg: q}) return time.perf_counter() - t0 secs = [await ask(q) for q in gt.question for _ in range(3)] print(f"P50 {np.percentile(secs, 50):.1f}s P95 {np.percentile(secs, 95):.1f}s") Run each question several times; single timings are noisy. The MCP response returns answer text only, not the query, which is why query evidence comes from the steps table. Step 6: Preview now, standard after 10 November, then diff Preview, today. Switch your draft to preview and evaluate the sandbox stage, leaving production untouched. The switch comes from the runtime docs (the ribbon's Runtime dropdown works too); check the SDK reference for the FabricDataAgentManagement import. data_agent = FabricDataAgentManagement(agent_name) data_agent.update_configuration(enable_preview_features=True) preview_id = evaluate_data_agent(gt, agent_name, table_name="eval_sales_preview_2026_10", data_agent_stage="sandbox", critic_prompt=critic_prompt) data_agent.update_configuration(enable_preview_features=False) # don't publish on preview by accident Standard, after 10 November. Repeat Steps 4 and 5 against production with new table names. The diff. Compare per question, never just the summary. Fill in your SDK's pass-flag and answer column names. def diff_runs(before, after, passed="<pass-col>", answer="<answer-col>"): m = before.merge(after, on="question", suffixes=("_b", "_a")) def status(r): b, a = r[passed + "_b"], r[passed + "_a"] if b and a: return "same" if str(r[answer + "_b"]) == str(r[answer + "_a"]) else "answer changed" return "newly correct" if a else ("REGRESSION" if b else "fail both") m["status"] = m.apply(status, axis=1) return m.sort_values("status") Then compare the generated queries from both _steps tables. A changed query with the same answer deserves a look: it may be right by coincidence on this month's data. When a result changed, check the run steps for: which example query it picked, which join path it chose, which instruction it followed, and whether a clarifying question was the right call. Most fixes will be in your examples and instructions, not the platform. The 6 November deadline: move Foundry agents to Fabric IQ If a Foundry agent calls your data agent through the original custom tool, that path stops being supported on 6 November 2026 (retirement notice). The catch: the replacement, the Fabric IQ (OneLake Catalog) tool, is in preview. Production agents move from a deprecated integration to a preview one, four days into the query rollout. Make that an explicit risk decision, with a fallback plan. What you gain: pick agents by name from the catalog, connect several data agents to one Foundry agent (the old tool allowed one), and an MCP endpoint per agent. Microsoft also says responses will soon include the query used. Portal migration, in Microsoft's order: Publish the data agent with a rich, detailed description. In the Foundry portal, open the agent and add the Fabric IQ (OneLake Catalog) tool, then select your data agent. Update the Foundry agent's instructions to name each data agent and what it handles. Remove the old custom tool, save, and test with representative questions. For code, use the MCP URL from the agent's Settings > Model Context Protocol tab and the FabricIQPreviewTool in azure-ai-projects>=2.2.0. Full samples are in Connect agents to Microsoft Fabric with Fabric IQ. Test the two changes separately. If you migrate on 5 November and answers shift on 8 November, you won't know why. Migrate now and baseline through Foundry before 2 November. The connection calls Fabric as the signed-in user, so test with accounts that match real users' permissions and row-level security. When the new answer is right, who tells people? The hardest part is not technical. Someone acted on the old number. Decide now who owns that conversation: Each agent has a business owner who reviews "newly correct" and "answer changed" rows. High-stakes answers get a change note: old answer, new answer, why, and since when. The note goes where the old answer was used: report owners, Teams channels, Copilot Studio makers. Regressions get fixed, not avoided. Standard is changing anyway, so plan the fix. Also tell users before 2 November that answers may become more precise and the agent may ask follow-up questions. A warned surprise becomes a feature. Your checklist Inventory agents, runtimes, consumers and owners Build ground-truth sets, including ambiguous and high-stakes questions Write a critic prompt that accepts correct clarifying questions Capture the standard baseline, steps and latency before 2 November Run the set on preview in the sandbox stage Migrate Foundry agents to Fabric IQ and baseline them before 6 November Decide explicitly on running production on the preview Fabric IQ tool Re-run after 10 November and diff per question and per query Retire workaround instructions and refresh stale examples Send change notes for high-stakes answers that moved
Fabric capacity metrics vs activity logs for cost attribution
I have been working on the backend of a packaged Fabric Spend Analyzer, and the difficult part has been reconciling usage rather than building the dashboard. Capacity metrics, activity data, and workload-level signals can behave differently around spikes, background operations, and delayed telemetry. We added pipeline checks for missing or late data before it reaches the spend model, and tightened the DAX calculations around those cases. A few “correct-looking” numbers turned out to be wrong once we traced them back to the underlying usage data. For those building Fabric capacity reporting, how are you validating cost attribution when the underlying telemetry is incomplete or arrives at different times?
DP-600 Voucher
a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } Hi everyone, I recently passed the DP-700 Microsoft Fabric Data Engineer Associate certification and am now planning to take DP-600 (Microsoft Certified: Fabric Analytics Engineer Associate) while the Fabric concepts are still fresh in my mind. Unfortunately, I wasn't able to use a previous DP-600 voucher when I first received it as I wasn't ready for the exam at the time. I was wondering if anyone is aware of any current voucher opportunities for DP-600, or has an unused voucher that they won't be using and is transferable (if permitted by the voucher terms). Any guidance would be greatly appreciated. Thanks in advance! Regards, Amanul
High CU consumption after enabling Workspace Monitoring
Hi everyone, I’m wondering if anyone else has experienced very high CU consumption after enabling Workspace Monitoring in Microsoft Fabric. In my case, I tested it on an F4 capacity, and the additional CU usage was significant enough that the capacity became difficult to use for the actual workloads running in the workspace. I understand that Workspace Monitoring creates and uses Fabric resources for collecting and storing logs, so some additional capacity consumption is expected. However, the overhead I’m seeing feels quite high and it makes no sense to enable this feature I’m interested if there are any answers on following questions: Have you noticed a significant CU increase after enabling Workspace Monitoring? Is the impact especially noticeable on smaller capacities such as F2/F4/F8? Have you found any practical way to reduce the monitoring overhead? Are you using Workspace Monitoring in production on smaller capacities, or do you avoid it because of the CU cost? At the moment, my concern is that on smaller capacities the monitoring workload itself can consume enough capacity to materially affect the workloads it is supposed to help monitor. Would be interested to hear about other people’s experience and whether this is expected behaviour.
Unable to commit the following item type "Activator"
Hi Fabric Community, I'm running into an issue with a type activator in Fabric and could use some guidance from the community. When attempting to commit the activator to my repo, I received the following error message: "We weren't able to commit the following item: Please check the technical details for more information " I'm not entirely sure what this error means or how to troubleshoot it. If anyone has come across a similar issue with type activators in Fabric or has any insights into what might be causing this, I'd really appreciate your help. Thanks in Advance
