If you’ve ever opened a solution and instantly felt overwhelmed by the web of files, references, and bugs waiting to ambush you, you’re not alone. Most developers work reactively—patching here, debugging there. GitHub Copilot’s agent mode aims to hold broader context and coordinate changes across files. In this video, we’ll demonstrate how that workflow looks inside a real .NET and Azure project. We’ll walk through a live solution and show before-and-after agent changes. You’ll see how to generate multi-file code with less overhead, resolve cross-file errors faster, and even use a plain-language spec to scaffold features. And before we get there, let’s start with the hidden cost of the debugging loop so many of us live in every day.The Hidden Cost of Patchwork DebuggingYou sit down to fix an error that looks simple enough. The application won’t build, and the console flags a line in your main project file. You tweak the method, recompile, and think you’ve solved it—until the same message reappears in a slightly different form. Another half hour slips by before you spot the real issue: a missing dependency tucked away in another project folder. By the time the reference is corrected and you redeploy, most of your afternoon has dissolved into patchwork. The feature work you planned? It’s pushed to tomorrow. This pattern is so common it feels normal. On the surface, you’re moving forward because each bug you squash feels like a win. In practice, you’re running in circles. The loop is code, compile, error, fix, repeat. Hours vanish into chasing a trail of cause and effect, and the net result is reactive progress rather than meaningful improvements. How many of you have lost an afternoon to this exact loop? Drop a one-line comment—I’ll read through the top replies. What makes this cycle exhausting is that the tools around us keep advancing while the pattern doesn’t. Editors add new features, frameworks evolve, and integrations grow deeper—but debugging still demands a reactionary approach. It’s like trying to hold back a growing fire with a bucket of water. Each flare-up gets handled in the moment, but the underlying conditions that sparked it remain, almost guaranteeing the next blaze. And with every tab switch, the hidden cost rises. You move from a service class into a configuration file, then jump across to a dependency graph. Each shift pulls you out of whatever thread of logic you were holding, forcing a mental reset. It’s not just the seconds spent flipping windows; it’s the mental tax of reconstructing context again and again. Over a day, those small resets pile up into something heavy. For individual developers, the fatigue shows up as frustration and wasted time. For teams working on enterprise projects, the impact multiplies. Debugging loops drag sprint goals off track, delay feature launches, and open up a backlog that grows faster than it shrinks. The strengthening of technical debt is just another side effect of hours lost to firefighting. Many teams report that a large share of their development time gets siphoned into reactive debugging. It’s not the exciting part of engineering—no one plans a roadmap around chasing the same dependency mismatch five times. Yet this is where bandwidth goes, week after week. When fixing errors becomes the definition of progress, building new features becomes secondary and the architecture suffers quietly in the background. The uncomfortable truth is that patchwork debugging doesn’t just slow things down. It reinforces a culture of reaction instead of design. You’re spending time dousing flames, not constructing systems. That may keep the product alive in the short term, but it limits how far a team can scale and how confidently they can ship. So let’s pause on that image: firefighting. Dash to the hot spot, dump water, move on. The trouble isn’t that developers aren’t good at it—they are. The trouble is that the flames never really stop. They just move around, flaring up in new files, new projects, new configurations, keeping everyone in response mode instead of creation mode. That raises the question: what happens if this cycle doesn’t rest on you alone? What if the repetitive parts—the loop of tracing, switching, and patching—could be managed differently, while you stayed focused on building? Because while the strain of firefighting is obvious, there’s another pressure point we haven’t touched yet. The real weight comes when your project isn’t just one file or one module. It’s when the fix you need spans multiple layers at once. Picture sitting down to open a solution where the logic sprawls across different projects, services, and libraries—the part you need lives in three places at once, and keeping it straight in your head is its own battle.Multi-File Chaos vs. AI Context ControlWhen projects span multiple layers, the real challenge isn’t writing code—it’s holding all the moving parts together. This is where the tension between multi-file chaos and AI-driven context control shows up most clearly. Take a large .NET solution with a dozen or more projects. Any new feature usually touches different layers at once: a controller in one place, a service in another, and a set of configuration files that live elsewhere. Before you write a single line, you spend time tracing references and checking dependencies, hoping a small change doesn’t ripple into unexpected breaks further down the chain. That workflow isn’t an exception—it’s normal in enterprise applications, especially once Azure services and integrations enter the picture. The structure of these systems isn’t flat. Interfaces, dependency injection mappings, and cross-project references all play a role. With Azure in the mix, some dependencies step completely outside the solution folder—Function Apps, Service Bus bindings, resource settings, storage connections. You’re coordinating between code in your IDE, config files on disk, and services defined in the cloud. None of them care that you’d like fewer clicks. Every time you switch context, you burn energy reconstructing the bigger picture. Most of us try to juggle that context in working memory. At first it’s manageable, but as the project grows, mistakes slip in. You add a new method in a service but forget its DI registration. You code up an Azure Function and only later realize the binding never got added to host.json or the deployment template. Nothing alerts you until runtime, when you’re debugging instead of building. The code itself isn’t the hard part—it’s the cross-file coordination. Everyone knows the feeling of bouncing through tabs: from a controller into a service, then over to a model, then into configuration files, then back again—only to lose track of why you opened that file at all. It’s a small disruption repeated dozens of times a day. Those interruptions pile up, creating friction that drags down real progress. The result is slower delivery, not because writing is slow, but because keeping everything in sync steals focus. This overhead grows in cloud-first projects. Azure pushes key settings into multiple places: local config files, environment variables, ARM or Bicep templates, CI/CD pipelines. What looks like a single feature request often spreads across four layers of abstraction. The complexity isn’t optional—it’s built into the way the ecosystem works. Now, here’s where agent mode enters as a potential shift. Instead of leaving all that orchestration to you, it’s designed to hold broader context across multiple files. That means when you ask for a change in one layer, it doesn’t ignore the others. In the demo, I’ll create a new Azure Function and show how the agent helps by generating the method body, producing the binding config, updating host.json, and even suggesting the right DI registration. That’s usually a multi-step process scattered across different files. An agent can streamline it into one flow. This is not about replacing your judgment. It’s about removing the repetitive bookkeeping so you can focus on the actual design choices. Humans can keep a rough outline in their heads or sketched on a whiteboard. An AI can track the details file by file without losing the thread. What feels like a huge cognitive load for us is just baseline context for the agent. The difference, in practice, is moving from fractured tab-juggling to orchestrated changes that stay in sync. I’ll also pull up the agent-created pull request or diff during the demo so you can see exactly what edits were made. That visibility matters—you get full control to review and approve, while the legwork of updating multiple files happens for you. So instead of spending an afternoon stitching fragments together, you direct the change once, confirm the generated updates, and move on to higher-level design. The relief isn’t just in saved clicks or keystrokes; it’s in staying focused on solving actual problems rather than retracing how a dozen files connect. This advantage shows most clearly when things break. Because even with stronger context handling, systems fail, configs drift, and mismatched references creep back in. And that’s where the next test begins—how errors get tracked down and resolved once they surface.From Error Hunts to Autonomous FixesThink about how often you hit an error message that points straight at a single file. You follow the stack trace to the method it names, make a small adjustment, and hit rebuild. It feels like the obvious solution—until the same error appears again, just in a slightly different form. That’s when you realize the stack trace only showed the symptom. The real issue lives somewhere else entirely, maybe in a supporting class or hidden inside a config file you haven’t opened all week. Every developer has faced that kind of misdirection: what looks like the problem isn’t actually where the fix belongs. This eats up time fast. You adjust one thing, rebuild, wait. Then a fresh error greets you, leading to another file, another tweaBecome a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.Follow us on:LInkedInSubstack

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