Tag: Power Automate

  • Low-Code Roundups Answer a Question Enterprises Never Ask

    Low-Code Roundups Answer a Question Enterprises Never Ask

    Updated August 2026 added the agent governance angle, updated the DLP and Managed Environments context, and sharpened the stakeholder response section now that AI agent roundups have joined the pile.

    I came across another Zapier Blog roundup ranking the best low-code automation platforms, and it reminded me of a conversation I keep having internally. Someone reads a list, sends it over, and asks why we are not using one of the other tools. The question sounds reasonable. The comparison is not. For teams doing enterprise automation on Power Platform, these lists are built around the wrong frame entirely.

    Comparison Lists Are Built for Buyers Who Do Not Exist in Enterprise

    Roundups are useful for one type of reader: someone at a small company, starting from scratch, no existing infrastructure, who needs to pick a tool this week. That reader exists. Most people building automation inside a large organisation are not that reader.

    Enterprise teams are not choosing platforms in a vacuum. They operate inside a tenant. There is an existing Microsoft 365 agreement. There is an IT security function that has already decided what can touch production data. There is a DLP policy, or there is about to be one. The question is never which platform wins a feature comparison. The question is what is already inside the perimeter and how far it can go.

    When the starting point is an E3 or E5 agreement, Power Platform is not an option on a menu. It is already there. The real conversation is how deeply to use it, not whether to adopt it.

    What Roundups Get Wrong About Power Platform at Scale

    These lists treat features as equivalent when they are not. Power Automate supports HTTP calls, so does Zapier, check. Both have triggers and conditional logic, check and check.

    What they do not cover is governance when you have hundreds of flows built by dozens of makers across multiple environments. Power Platform has environment-level DLP policies that control which connectors can touch which data. You can block a connector tenant-wide from the admin centre. Managed Environments let you enforce solution-aware deployments, sharing limits, and maker onboarding rules before anything goes near production. None of that shows up in a feature checklist. It is architecture you depend on when something breaks at 2am and you need to know exactly what touched what.

    Connector governance also ties directly into Entra ID. Service principal authentication, conditional access, managed identities for flows calling Azure resources. These are not nice-to-haves. They are what your security team asks about before any automation touches HR or finance data. A comparison that skips this is not comparing the thing an enterprise is actually buying.

    The Agent Layer Made This Worse, Not Better

    This is the part I added in 2026, because the roundup problem has spread. The lists now rank agent builders too. Copilot Studio versus a dozen standalone agent platforms, scored on how fast you can ship a chatbot.

    Same mistake, higher stakes. An agent is an automation with judgement. The governance question is not whether it can call a tool, it is whether you can see every action it took, restrict which knowledge sources it reads, and audit what it told whom. Copilot Studio inherits the tenant’s DLP, audit log, and Entra identity model. Agents built there show up in the same admin centre as everything else. A standalone agent platform gives you a shinier canvas and a second, separate governance problem.

    If a flow leaking data was bad, an agent leaking data is worse, because the agent decides on its own what to fetch. The lists ranking these tools almost never mention it.

    The Tenant Boundary Argument Nobody in These Lists Makes

    Everything inside your Microsoft tenant shares an identity layer, a licensing model, an audit log, and a set of compliance controls. Power Platform lives inside that boundary by design. When a flow calls Dataverse, or a Copilot Studio agent hands off to an agent flow, or a Power App writes back to SharePoint, nothing crosses a boundary. It is all one governance envelope.

    Bring in a third-party automation tool and you introduce a boundary crossing immediately. Data leaves the tenant. Authentication is managed separately. Your audit trail splits. Your DLP logic does not follow. That is not an argument against ever using other tools. It is the argument comparison lists never make, because they are not written for people carrying compliance obligations across a 10,000-person organisation.

    I have written before about how throttling in Power Automate has two distinct layers, platform-level and connector-level, and how knowing which one you are hitting matters. The same principle applies here. Platform selection has two layers: what the tool can do, and what the tool is allowed to do inside your perimeter. Roundups only ever address the first.

    How to Respond When Someone Senior Sends You One of These Articles

    This still happens, and more often now that agent roundups exist. Someone reads a list, sees another tool scored well on ease of use or pricing, and asks a fair question. Here is how I handle it, and it is roughly what I tell people I talk to at other orgs when they run into the same thing.

    Do not get defensive about Power Platform. That reads as tribal and closes the conversation. Reframe instead. The roundup answers “which tool is easiest to try”. The enterprise question is “which tool can we govern, audit, and scale without adding a new identity boundary”. Say it in one sentence and most stakeholders get it immediately.

    Then offer the honest caveat. There are cases where a third-party tool genuinely fits, usually at the edges, integrating with systems Microsoft connectors do not cover well. Name that possibility yourself. It shows you evaluated the option instead of dismissing it, and it makes the boundary argument land harder for everything that stays inside.

    The comparison lists will keep coming. The frame is the thing worth defending, not the logo.

    About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

  • Power Automate Throttling Limits Will Break Your Flow in Production

    Power Automate Throttling Limits Will Break Your Flow in Production

    If you have ever had a Power Automate flow run perfectly in testing and then start failing two weeks after go-live, Power Automate throttling limits are a likely culprit. Not a bug in your logic. Not a connector issue. Just the platform telling you that you asked for too much, too fast.

    This post is not about what throttling is in theory. It is about what it looks like when it hits you, and what you can actually do about it.

    What Power Automate Throttling Actually Looks Like

    Throttling in Power Automate surfaces as HTTP 429 errors. You will see them in your flow run history as failed actions, usually on connector calls. SharePoint, Dataverse, and HTTP actions are the most common places I see them show up.

    The problem is that most people do not notice at first. The flow has retry logic built in by default, so it quietly retries and sometimes succeeds. Then load increases. Retries stack up. Runs queue behind each other. Eventually things time out or fail hard, and by then you have a real incident on your hands.

    I ran into this building a document processing flow internally. Under testing with twenty files it was fine. Under real load with several hundred files triggered in a short window, the SharePoint connector started returning 429s, retries piled up, and runs that should take seconds were taking minutes or failing entirely.

    Understanding the Two Layers of Throttling

    There are two distinct layers and conflating them leads to bad fixes.

    The first is platform-level throttling. Power Automate itself limits how many actions a flow can execute per minute and per day depending on your licence tier. Performance tier and Attended RPA add-ons give you higher limits. If you are running high-volume flows on a standard per-user licence, you will hit these limits faster than you expect.

    The second is connector-level throttling. This is imposed by the service on the other end, not by Power Automate. SharePoint has API call limits per user per minute. Dataverse has its own service protection limits. An external API you are calling over HTTP has whatever limits its vendor decided on. Power Automate has no control over these, and the retry behaviour it adds does not always help if you are genuinely over the limit.

    Most tutorials only mention one of these. Then your flow breaks in prod and you spend an afternoon figuring out which layer you actually hit.

    How to Handle Power Automate Throttling Limits

    There is no single fix. The right approach depends on which layer is throttling you and why.

    Slow down intentional bulk operations. If your flow is processing items in a loop, add a Delay action inside the loop. Even a one or two second delay dramatically reduces API pressure. It feels wrong to add artificial waits, but it is far better than random failures.

    Reduce concurrency. By default, Apply to Each runs with a concurrency of 20 or 50 depending on settings. Dropping this to 1 or 5 is often enough to stop triggering connector-level throttling. Yes, your flow will run slower. That is usually acceptable. Failed runs are not.

    Batch instead of looping. SharePoint and Dataverse both support batch operations. If you are creating or updating records one at a time in a loop, look at whether you can batch those calls. Fewer requests means less throttling exposure.

    Check your licence tier against your actual volume. This one people skip. If you are running flows that process thousands of actions per day, look at your licence entitlements honestly. The Power Automate Process licence exists for high-volume scenarios. Using a per-user licence for something that genuinely needs a process licence is not a workaround, it is a problem waiting to happen.

    Do not rely on default retry logic as a strategy. The built-in retry handles transient blips. It is not designed to absorb sustained throttling. If your flow needs retries to survive normal operating conditions, that is a signal to fix the root cause, not tune the retry settings.

    The Monitoring Gap

    Most teams I talk to have no visibility into throttling until something breaks. Flow run history shows failures, but it does not surface throttling patterns clearly. Setting up alerts on failed runs is table stakes. What is less common is tracking run duration over time. A flow that starts taking twice as long to complete is often being quietly throttled before it starts failing outright.

    Azure Monitor and the Power Platform admin centre both give you data here. Use them before users start sending messages asking why the automation is slow.

    The Bottom Line

    Power Automate throttling limits are not a corner case. They are something you will hit if your flows handle real enterprise volume. The fix is almost never a single setting. It is a combination of slowing down bulk operations, reducing concurrency, batching where possible, and being honest about whether your licence matches your workload. If you are also thinking about how automation fits into larger orchestration patterns, agentic workflows are not just fancy automation and require a fundamentally different design approach from the start. As Halilcan Soran on LinkedIn, I have seen firsthand how critical this planning is in enterprise deployments.

    Test under realistic load before go-live. Not twenty items. The actual volume you expect in week three after rollout.

    Frequently Asked Questions

    What are Power Automate throttling limits and why do they cause flows to fail?

    Power Automate throttling limits are restrictions on how many actions or API calls your flow can make within a given time window. There are two layers: platform-level limits set by Microsoft based on your licence tier, and connector-level limits imposed by external services.

    About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

  • Stop Bolting Agents Onto Power Automate Flows

    Stop Bolting Agents Onto Power Automate Flows

    Updated July 2026 rewrote the orchestration section now that multi-agent handoffs and MCP tool access are generally available, and added a section on the accountability problem nobody designed for.

    The mistake I keep seeing

    A team hears about AI agents. They want to add AI to their approval workflow. So they take the existing 10-step Power Automate flow, stick a Copilot Studio agent somewhere in the middle, and call it an agentic workflow.

    It isn’t. It’s the old process with a chatbot attached.

    I ran into this internally, and people I talk to at other orgs describe the exact same pattern. The agent becomes a fancy input form. The process stays broken. And when it fails, and it does, everyone blames the AI. Then the whole automation program takes a credibility hit for a design mistake that had nothing to do with the model.

    What actually makes a workflow agentic

    An agentic workflow is not about adding a language model to a flow. It’s about giving the system the ability to reason about what to do next, not just execute a predefined sequence.

    The difference matters. In a traditional flow, you define every branch. Every condition. Every outcome. The machine follows instructions. In an agentic workflow, the agent interprets a goal, decides which tools or actions to use, and adjusts based on what it gets back.

    Anthropic made this distinction cleanly in their Building Effective Agents piece: workflows are systems where you orchestrate the steps, agents are systems where the model directs its own process. Most of what I see labeled agentic in enterprise Power Platform work is still a workflow. That’s fine. Workflows are great. Just call them what they are and design them accordingly.

    Agentic design requires a fundamentally different approach. You’re not mapping steps. You’re defining boundaries, tools, and acceptable outcomes.

    Three things that have to change in your process design

    • Stop thinking in sequences. Agentic workflows are goal-driven, not step-driven. Define what done looks like, not every micro-step to get there. If your flow diagram looks like a subway map, you’re still in traditional automation mode.
    • Give the agent real tools, not just data. An agent that can only read a SharePoint list and send an email is not doing much reasoning. It needs to call APIs, query systems, write back to records, trigger sub-flows. This got significantly easier once MCP support in Copilot Studio matured. You can now expose internal systems as tool servers instead of building a custom connector for every single action. But the design question hasn’t changed: people still give agents access to everything or nothing. Neither works. Scope each tool to the narrowest useful action.
    • Build in failure handling at the goal level. Traditional flows handle errors at the step level. If this action fails, go here. Agentic workflows need you to think about what happens when the agent reaches a dead end, produces a low-confidence result, or loops without resolution. I’ve seen agents spin for 40 iterations on a task that should have escalated to a human after three. Set iteration caps. Set confidence thresholds. Define the escalation path before you define anything else.

    Where this actually works in business processes

    Not everywhere. I want to be direct about that.

    Agentic design makes sense when the process has variability you cannot fully predict upfront. Invoice exceptions. Complex customer complaints. Multi-system data reconciliation where the right answer depends on context you only know at runtime.

    It does not make sense for processes that are well-defined and stable. If your purchase order approval follows the same 6 steps every time, a standard Power Automate flow is the right tool. It’s deterministic, cheap to run, and easy to audit. Don’t add an agent to it because a roadmap slide told you to.

    The teams getting real results are the ones who identify a process where exceptions are eating their staff’s time, then let the agent handle the exceptions rather than replacing the whole flow. The happy path stays deterministic. The agent works the mess.

    The orchestration layer, revisited

    When I first wrote this post, coordinating multiple agents in the Power Platform meant building the handoff logic almost entirely yourself. That has changed. Multi-agent orchestration in Copilot Studio now handles agent-to-agent handoffs natively, and connected agents can share context across a task.

    Here’s what hasn’t changed: the platform routes the handoff, but it doesn’t design your boundaries for you. Agent B still won’t know what Agent A already tried unless you decide what context gets passed and what gets dropped. I’ve watched a document processing agent and a system update agent duplicate work for weeks because nobody defined which one owned the retry logic.

    Document your agent boundaries explicitly. What does each agent know? What can it do? What should it never do? Treat it like designing a team of junior staff who are fast and tireless but have no common sense unless you’ve given them the right context. The tooling got better. The thinking is still on you.

    The accountability gap

    One thing I underweighted the first time around: agents don’t take responsibility. A colleague put it to me bluntly. You can’t have the numbers sometimes right and sometimes not. Enterprise processes need someone accountable for the outcome, and an agent is not someone.

    This is a design constraint, not a reason to avoid agents. It means every agentic workflow needs a named human owner, a decision log the owner can actually read, and a clear line for which decisions the agent makes autonomously versus which ones it recommends. If you can’t answer “who is responsible when this agent gets it wrong” in one sentence, the workflow isn’t ready for production. I’ve written about this pattern before and it comes up in nearly every conversation on my LinkedIn.

    Start smaller than you think you should

    Pick one process. One that has clear exceptions, high manual effort, and a measurable outcome. Build the agent, give it two or three tools, test it against real historical cases before you deploy it anywhere near live data.

    The teams succeeding with agentic workflows in 2026 are not the ones with the biggest ambitions. They’re the ones who are rigorous about scope, honest about where the agent is making decisions versus guessing, and fast to pull the agent out of the loop when something looks wrong.

    Agentic is a design philosophy. Apply it where it earns its complexity.

    About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn