Halilcan Soran

Category: Artificial Intelligence in Business

  • Copilot Studio Is Not Always the Answer

    Copilot Studio Is Not Always the Answer

    I keep seeing this on LinkedIn and in community forums. Someone describes an internal use case, and the first five replies are all “have you tried Copilot Studio?” The tool has gotten good enough that it has become the reflexive answer to any question involving automation, conversation, or AI. That reflex is causing real problems. Knowing when Copilot Studio is the wrong tool is as important as knowing how to build with it well.

    When Copilot Studio Is the Wrong Tool for the Job

    Most misuse I see falls into one of three situations. The use case is purely transactional. The interaction model is not conversational. Or the team wants a workflow, not an agent.

    If someone needs to submit a form, approve a request, or trigger a process on a schedule, that is Power Automate territory. Putting a conversational interface in front of a single-action task does not make it better. It makes it slower, harder to test, and harder to maintain. Users do not want to type a sentence to do something they could do in two clicks.

    The second situation is harder to spot. Some interactions look conversational but are not. A knowledge base search, a document lookup, a status check. These are point-in-time queries with no real back-and-forth. You could build them in Copilot Studio. You could also build them as a Power Apps canvas app with a simple search interface and ship it in a day with less moving parts and a much more predictable failure surface.

    The Agent Complexity Problem

    There is also a complexity ceiling that teams hit faster than expected. Copilot Studio agents work well when the conversation scope is tight. One domain. A few topics. Defined intents. When someone tries to build a single agent that handles HR queries, IT requests, and finance approvals inside the same session, topic routing starts failing at the edges. I wrote about this in Your Copilot Studio Agent Passed Every Test and Still Failed in Production. When a user’s phrasing sits between two topics, the agent picks one confidently and gets it wrong. The more topics you add, the more edge cases you create, and the harder they are to test systematically.

    The instinct to build one agent that does everything is understandable. It feels cleaner. In practice it produces an agent that does everything poorly and fails in ways that are genuinely difficult to diagnose.

    Where the Wrong Choice Usually Starts

    It usually starts with the framing of the requirement. Someone says “we want a chatbot” and that phrase triggers Copilot Studio before anyone has defined what the interaction actually needs to do. I have seen teams spend weeks building agent topics, writing generative AI prompts, and wiring up Power Automate actions, when what the users actually wanted was a better SharePoint search and a weekly digest email.

    The honest question to ask before opening Copilot Studio is this: does this use case genuinely require back-and-forth conversation, or does it just need to surface information or move data? If the answer is the second one, there is almost always a simpler path.

    This is not a knock on Copilot Studio. The tool is genuinely capable when it fits the problem. Handling multi-turn conversations, routing across complex intent patterns, integrating generative answers with structured actions, those are things it does well. But that capability comes with a real operational cost. There is a topic structure to maintain, system prompts that drift when production data introduces edge cases, Power Automate actions that can fail silently inside a topic and return a confident-sounding response for work that was never done.

    What to Reach for Instead

    Power Apps for anything with a fixed interaction model. Canvas apps are underrated for internal tooling. They give you a defined UI, predictable state, and a clear place to debug when something breaks.

    Power Automate for anything triggered, scheduled, or event-driven. If there is no user in the loop having a conversation, there is no reason for Copilot Studio to be involved. Keep in mind that even straightforward flows can run into issues at scale, as Power Automate throttling limits will break your flow in production under real load if you have not accounted for them.

    SharePoint or Dataverse with a search interface for knowledge retrieval. If users are looking something up, build a search experience, not a conversational one.

    In enterprise environments, the governance overhead of Copilot Studio also matters. You are managing an agent that generates natural language responses. That response quality needs to be reviewed, monitored, and occasionally corrected. Most teams I talk to underestimate this cost until they are three months into production and someone in legal asks why the agent said something it should not have.

    The Right Question Before You Build

    Before any Copilot Studio project starts, the question worth asking is not “how do we build this agent” but “does this use case actually need an agent.” If the answer requires you to stretch the definition of conversation to make it fit, that is a sign to stop and pick the simpler tool.

    Copilot Studio is a good tool. It is not a default. Using it where it fits produces something worth building. Using it where it does not produces something you will be maintaining and explaining for a long time.

    Frequently Asked Questions

    When should I use Copilot Studio instead of another tool?

    Copilot Studio works best when the interaction is genuinely conversational, scoped to a single domain, and involves a defined set of intents. If the task is transactional, point-in-time, or better served by a simple form or search interface, tools like Power Automate or Power Apps are likely a faster and more maintainable choice.

    What is the difference between Copilot Studio and Power Automate?

    Power Automate is built for workflow and process automation, such as form submissions, approvals, and scheduled triggers. Copilot Studio is designed for conversational agent experiences. Using Copilot Studio for single-action tasks adds unnecessary complexity without improving the user experience.

    Why does my Copilot Studio agent keep routing users to the wrong topic?

    Topic routing breaks down when an agent is built to handle too many domains or intents within a single session. When a user’s phrasing falls between two topics, the agent will confidently pick one and get it wrong. Keeping each agent focused on a narrow scope reduces these edge cases and makes failures easier to diagnose.

    How do I know if my use case actually needs a chatbot?

    Start by defining what the interaction needs to do before choosing a tool. If users need a back-and-forth conversation to complete a task, a conversational agent may be appropriate. If they need a search result, a status update, or a simple action, a canvas app or improved search interface will often deliver a better outcome in less time.

  • Low-Code Platform Comparisons Miss the Point for Enterprise Power Platform Teams

    Low-Code Platform Comparisons Miss the Point for Enterprise Power Platform Teams

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

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

    Roundups like this are useful for one type of reader: someone at a small company, starting from scratch, with 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 between platforms in a vacuum. They are operating inside a tenant. They have an existing Microsoft 365 agreement. They have an IT security function that has already decided what can touch production data. They have a DLP policy, or they are about to have one. The question is never which platform wins a feature comparison. The question is what is already inside the perimeter and how far can it go.

    When the starting point is a Microsoft 365 E3 or E5 agreement, Power Platform is not an option on a menu. It is largely already there. The conversation is about how deeply to use it, not whether to adopt it at all.

    What These Roundups Get Wrong About How Power Platform Actually Works at Scale

    The comparisons that show up in these lists treat features as equivalent when they are not. They will note that Power Automate supports HTTP connectors, and so does Zapier, so check. They will note that both have flow triggers and conditional logic. Check and check.

    What they do not cover is how governance works when you have hundreds of flows built by dozens of makers across multiple environments. Power Platform has environment-level DLP policies that enforce which connectors can interact with which data classifications. You can block a connector tenant-wide from the admin centre. You can require solution-aware flows before anything goes near a production environment. None of that is a feature you evaluate in a roundup. It is architecture you depend on when something goes wrong at 2am and you need to know exactly what touched what.

    Connector-level governance also ties directly into Entra ID. Service principal authentication, conditional access policies, managed identities for flows that call Azure resources. These are not nice-to-haves. They are what your security team will ask about before any automation touches HR data or finance systems. A platform comparison that does not address this is not comparing the same thing your enterprise is actually buying.

    The Governance and Tenant Boundary Argument Nobody in These Lists Makes

    The argument that actually matters for enterprise teams is about the boundary. 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 Power Automate flow calls Dataverse, or a Copilot Studio agent hands off to an AI Builder model, or a Power App writes back to SharePoint, none of that crosses a boundary. It is all inside the same governance envelope.

    When you bring in a third-party automation tool, you immediately introduce a boundary crossing. Data leaves the tenant. Authentication has to be managed separately. Your audit trail splits. Your DLP logic does not follow. That is not an argument against ever using other tools. But it is the argument that platform comparison lists never make, because they are not written for people managing 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 understanding which one you are hitting matters. The same principle applies here. There are two distinct layers to platform selection: what the tool can do, and what the tool is allowed to do inside your security perimeter. Most comparison articles only address the first layer.

    How to Respond When a Stakeholder Sends You One of These Articles

    This happens. Someone senior reads a roundup, sees that another tool scored well on ease of use or pricing, and asks a reasonable question. Here is how I handle it.

    First, do not get defensive about Power Platform. That reads as tribal and closes the conversation. Instead, reframe the question. The roundup is answering “which tool is easiest to try”. The enterprise question is “which tool can we govern, audit, and scale without introducing a new identity boundary or violating our data residency requirements”.

    Second, be specific about what already exists. If you have 200 flows in production, connectors pre-approved by security, an admin centre your IT team actually monitors, and makers who already know the platform, the switching cost is not zero. It is very large. That context belongs in the conversation.

    Third, acknowledge what the other tools do well. Zapier is genuinely easier to set up for a simple two-step integration. Make has a visual canvas that some people find clearer than Power Automate’s. Agreeing on the narrow case where another tool wins builds credibility for the broader argument about why it does not win at enterprise scale. The same logic applies when teams start layering AI into their automations: as I explored in Agentic Workflows Are Not Just Fancy Automation, adding an AI layer does not transform a poorly governed process into a reliable one, regardless of which platform you are on.

    The roundup is not wrong. It is just answering a different question. Once you say that clearly, the conversation usually moves to something more useful than defending a platform choice that was effectively made the day the Microsoft agreement was signed.

    Frequently Asked Questions

    Why should enterprises use Power Platform for enterprise automation instead of other low-code tools?

    For most large organisations, Power Platform is already included in their Microsoft 365 agreement, so the decision is less about choosing a tool and more about how deeply to use one that is already available. It also integrates directly with existing Microsoft security infrastructure, including Entra ID, conditional access policies, and tenant-level governance controls that other platforms simply cannot replicate in that environment.

    How do I govern Power Automate flows across a large organisation?

    Power Platform allows admins to apply environment-level DLP policies that control which connectors can access which types of data, and connectors can be blocked tenant-wide from the admin centre. Requiring solution-aware flows before anything reaches a production environment adds another layer of control, giving teams a clear audit trail when something needs investigating.

    What is a DLP policy in Power Platform and why does it matter for enterprise teams?

    A DLP (Data Loss Prevention) policy in Power Platform defines which connectors can interact with business or sensitive data within a given environment. For enterprise teams handling HR or finance data, these policies are a security requirement rather than an optional feature, and they are enforced at the tenant level rather than left to individual flow builders.

    When should I question a low-code platform comparison for enterprise use?

    Most platform comparison lists are designed for small teams starting from scratch with no existing infrastructure, which is a very different situation from a large organisation with an established Microsoft 365 tenancy and security requirements already in place. If a comparison does not address governance at scale, service principal authentication, or tenant boundary controls, it is not evaluating the same things your enterprise actually needs.

    This post was inspired by The 7 best low-code automation platforms in 2026 via Zapier Blog.

  • Your Copilot Studio Agent Passed Every Test and Still Failed in Production

    Your Copilot Studio Agent Passed Every Test and Still Failed in Production

    I came across a post from Zapier Blog about AI agent evaluation, and it described something I keep seeing inside large organisations: an agent that looks perfect in a demo, gets signed off, goes live, and then immediately starts doing things nobody expected. Wrong tool calls. Conversation loops that never resolve. Outputs that look confident and are completely wrong. The post frames this well as a sandbox problem. But the fix it describes, better test coverage and smarter metrics, only gets you partway there. The deeper issue with Copilot Studio agent testing is not the quantity of your tests. It is what you are actually testing for.

    Why Demo-Passing Agents Break in Real Workflows

    When a team builds an agent in Copilot Studio, they test it against the happy path. A user asks a clean question. The agent triggers the right topic or action. The response looks good. Someone in the review meeting says it works great. The agent gets promoted to production.

    The problem is that real users do not ask clean questions. They ask incomplete ones. They switch intent halfway through a conversation. They paste in text that includes formatting your prompt never anticipated. They use your agent for things it was never designed to do, because nothing in the interface tells them not to.

    None of that shows up in a demo. It shows up three days after go-live when someone forwards you a conversation log that reads like a stress test you forgot to run.

    The Three Failure Modes I Keep Seeing in Copilot Studio Agents

    After building and reviewing a number of agents internally, the failures cluster into three patterns.

    Topic misrouting at the edges. Your agent routes correctly when the user says exactly what you expected. But natural language is messy. When a user’s phrasing sits between two topics, the agent picks one confidently and gets it wrong. You only discover this when someone captures a failed session and traces it back. By then, a dozen other users have hit the same wall and just stopped using the agent.

    Action failures that degrade silently. A Power Automate flow or a connector action fails in the background and the agent carries on as if nothing happened. No error surfaced. No fallback triggered. The user gets a response that implies the task completed. It did not. This is the agent equivalent of a flow that retries quietly and masks the problem until the load goes up. I wrote about that pattern in the context of Power Automate throttling limits breaking flows under real load. The same logic applies here: silent success is not success.

    Prompt instruction drift under real data. Your system prompt was written against clean test data. Production data is not clean. It has unexpected characters, long strings, mixed languages, or values that push the model toward an interpretation you did not intend. The agent’s behaviour drifts. Not catastrophically. Just enough to become unreliable in ways that are hard to reproduce and harder to explain to stakeholders.

    How to Build a Behavioral Test Suite Instead of an Output Checklist

    Most teams build an output checklist. Did the agent return the right answer for these ten questions? That tells you almost nothing about production behaviour.

    What you actually need is a behavioral test suite. The difference is this: output testing checks what the agent said. Behavioral testing checks how the agent handled the situation.

    Here is how I approach it inside Copilot Studio before promoting anything to production.

    Build adversarial input sets, not just representative ones. For every topic your agent handles, write three versions of the trigger: the clean version, an ambiguous version that could belong to two topics, and a broken version with incomplete or oddly formatted input. If the agent routes all three correctly, you have something worth shipping. If it fails on the ambiguous case, you have a routing gap that will hit real users constantly.

    Test conversation state, not just single turns. Copilot Studio agents hold context across a conversation. Test what happens when a user changes their mind on turn three. Test what happens when they ask a follow-up that assumes context the agent should have retained but might not. Single-turn testing misses an entire class of failure that only appears in multi-turn sessions. This is also why agentic workflows require a fundamentally different design approach, not just an AI layer placed on top of existing processes.

    Inject real data samples into action inputs. Pull a sample of actual data from your environment and run it through the actions your agent calls. Do not use synthetic test data if you can avoid it. Real data has edge cases your synthetic data will never cover. If your agent calls a flow that queries a SharePoint list, run the query against the actual list with actual entries, including the ones with blank fields and formatting you did not anticipate.

    Define explicit fallback behaviour and test it deliberately. Every agent should have a defined behaviour for when it cannot complete a task. Most teams add a fallback topic and assume it works. Test it by constructing inputs that should trigger it. If the fallback does not fire, or fires on the wrong inputs, fix it before go-live. A graceful failure is far better than a confident wrong answer.

    What to Monitor After Go-Live and When to Pull an Agent Back

    Testing before launch is necessary but not sufficient. Agent behaviour shifts as the inputs it receives in production diverge from what you tested against. You need monitoring in place from day one.

    Track escalation rate and abandon rate per topic. If a topic is seeing significantly higher escalations than others, that is a signal of routing or response quality problems, not user error. Track action failure rates separately from conversation outcomes. An agent can complete a conversation and still have failed to do the thing the user needed.

    Set a threshold before launch. If escalation rate exceeds a number you agree on in advance, or if a specific action is failing more than a defined percentage of the time, you pull the agent back or disable the affected topic. The threshold is arbitrary. Having no threshold at all is not.

    The agents I have seen hold up in production are not the ones with the most sophisticated prompts. They are the ones where someone spent real time on the failure cases before launch and built actual monitoring into the plan from the start.

    If you are still signing off agents based on demo performance, you are not testing. You are hoping.

    Frequently Asked Questions

    Why does my Copilot Studio agent testing pass in demos but fail in production?

    Most Copilot Studio agent testing is built around ideal user inputs and predictable conversation paths, which do not reflect how real users actually behave. In production, users ask incomplete questions, switch intent mid-conversation, and use the agent in unintended ways that no demo ever surfaces. Testing needs to go beyond the happy path to catch these edge cases before go-live.

    What are the most common failure modes in Copilot Studio agents?

    The three patterns that appear most often are topic misrouting when user phrasing falls between two intents, action failures that complete silently without triggering any error or fallback, and prompt instructions that break down when they encounter messy real-world data. Each of these can go undetected in testing because they only emerge under realistic conditions.

    How do I know if a Power Automate action failed inside my Copilot Studio agent?

    Silent action failures are a serious risk because the agent can continue the conversation and imply a task completed when it did not. You need explicit error handling and fallback logic in your flows so that failures surface to the user rather than being masked by a confident-sounding response.

    When should I test my Copilot Studio agent against real production data?

    You should test against realistic data before promotion to production, not after. System prompts written against clean test data can behave unpredictably when they encounter unexpected characters, mixed languages, or long strings that only appear in live environments. Incorporating a sample of real or representative data into your test suite is a necessary step before sign-off.

    This post was inspired by AI agent evaluation: How to test and improve your AI agents via Zapier Blog.

  • 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.

  • Agentic Workflows Are Not Just Fancy Automation

    Agentic Workflows Are Not Just Fancy Automation

    The mistake I keep seeing

    A client comes in. They’ve heard 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 just the old process with a chatbot attached.

    This is the most common mistake I see right now, and it’s costing teams time and credibility. The agent becomes a fancy input form. The process stays broken. And when it fails — and it does — everyone blames the AI.

    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.

    That requires a fundamentally different design 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. Tool design is where most implementations fall apart — people give agents access to everything or nothing. Neither works.
    • 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.

    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 that 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. Don’t add an agent to it just because you can.

    The teams that get the most out of agentic workflows 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 orchestration layer nobody talks about

    When you start running multiple agents — one for document processing, one for customer communication, one for system updates — you need something coordinating them. This is where I see projects go sideways fast.

    In Copilot Studio and Power Platform, you can build orchestrating agents that hand off to specialist agents. But the handoff logic, context passing, and failure recovery across agents is not something the platform handles automatically. You have to design it. Most tutorials skip this. Then your multi-agent setup breaks in production because Agent B has no idea what Agent A already tried.

    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.

    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 that succeed 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.