Speaker 1: If you've ever opened a solution and instantly felt overwhelmed
Speaker 1: by the web of files, references, and bugs waiting to
Speaker 1: ambush you, you're not alone. Most developers work reactively patching here,
Speaker 1: debugging there. Get ub Copilot's agent mode aims to hold
Speaker 1: broader contexts and coordinate changes across files. In this video,
Speaker 1: we'll demonstrate how that workflow looks inside a real podcast
Speaker 1: Net and Azure project. We'll walk through a live solution
Speaker 1: and show before and after agent changes. You'll see how
Speaker 1: to generate multi file code with less overhead, resolve crossfile
Speaker 1: errors faster, and even use a plain language speck to
Speaker 1: scaffold features. And before we get there, let's start with
Speaker 1: the hidden cost of the debugging loop so many of
Speaker 1: us live in every day, The hidden cost of patchwork debugging.
Speaker 1: You sit down to fix an error that looks simple enough.
Speaker 1: The application won't build, and the console flags a line
Speaker 1: in your main project file. You tweak the method, recompile,
Speaker 1: and think you've solved it until the same message reappears
Speaker 1: in a slightly different form. Another half hour slips by
Speaker 1: before you spot the real issue, a missing dependency tugged
Speaker 1: away in another project folder by the time, the reference
Speaker 1: is corrected and you redeploy. Most if your afternoon has
Speaker 1: dissolved into patchwork, the feature work you planned, it's pushed
Speaker 1: to tomorrow. This pattern is so common it feels normal
Speaker 1: on the surface, you're moving forward because each bug you
Speaker 1: squash feels like a win. In practice, you're running in circles.
Speaker 1: The loop is code compile, error, fix, repeat, hours vanish
Speaker 1: into chasing a trail of cause and effect, and the
Speaker 1: net result is reactive progress rather than meaningful improvements. How
Speaker 1: many of you have lost an afternoon to this exact loop?
Speaker 1: Drop a one line comment, I'll read through the top replies.
Speaker 1: What makes this cycle exhausting is that the tools around
Speaker 1: us keep advancing while the pattern doesn't. Editors add new features,
Speaker 1: frameworks evolve, and integrations grow deeper, but debugging still demands
Speaker 1: a reactionary approach. It's like trying to hold back a
Speaker 1: growing fire with a bucket of water. Each flare up
Speaker 1: gets handled in the moment, but the underlying conditions that
Speaker 1: sparked it remain almost guaranteeing the next place, And with
Speaker 1: every tap swhich the hidden cost rises. You move from
Speaker 1: a service class into a configuration file, then jump across
Speaker 1: to a dependency graph. Each shift pulls you out of
Speaker 1: whatever thread of logic you are holding, forcing a mental reset.
Speaker 1: It's not just the set can spend flipping windows, it's
Speaker 1: the mental tax of reconstructing context again and again over
Speaker 1: a day. Those small resets pile up into something heavy.
Speaker 1: For individual developers, the fatigue shows up as frustration and
Speaker 1: wasted time. For teams working on enterprise projects, the impact multiplies.
Speaker 1: Debugging loops, drag sprint goals off track, delay feature launches,
Speaker 1: and open up a backlog that grows faster than it shrinks.
Speaker 1: The strengthening of technical debt is just another side effect
Speaker 1: of hours lost to firefighting. Many teams report that a
Speaker 1: large share of their development time gets siphoned into reactive debugging.
Speaker 1: It's not the exciting part of engineering. No one plans
Speaker 1: a roadmap around chasing the same dependency mismatch five times.
Speaker 1: Yet this is where bandwidth goes week after week, When
Speaker 1: fixing errors becomes the definition of progress, building new features
Speaker 1: becomes secondary, and the architecture suffers quietly in the background.
Speaker 1: The uncomfortable truth is that patchwork debugging doesn't just slow
Speaker 1: things down, It reinforces a culture of reaction instead of design.
Speaker 1: You're spending time dowsing flames, not constructing systems. That may
Speaker 1: keep the product alive in the short term, but it
Speaker 1: limits how far a team can scale and how confidently
Speaker 1: they can ship. So let's pause on that image fire fighting,
Speaker 1: Dash to the hotspot, dump water, move on. The trouble
Speaker 1: isn't that developers aren't good at it. They are. The
Speaker 1: trouble is that the flames never really stop. They just
Speaker 1: move around, flaring up in new files, new projects, new configurations,
Speaker 1: keeping everyone in response mode instead of creation mode. That
Speaker 1: raises the question, what happens if the cycle doesn't rest
Speaker 1: on you alone? What if the repetitive parts, the loop
Speaker 1: of tracing, switching, and patching, could be managed differently while
Speaker 1: you stayed focused on building. Because while the strain of
Speaker 1: firefighting is obvious, there's another pressure point we haven't touched yet.
Speaker 1: The real weight comes when your project isn't just one
Speaker 1: file or one module. It's when the fix you need
Speaker 1: spans multiple layers at once, pictures sitting down to open
Speaker 1: a solution where the logic sprawls across different projects, services,
Speaker 1: and libraries. The part you need lives in three places
Speaker 1: at once, and keeping it straight in your head is
Speaker 1: its own battle multi file chaos versus AI context control.
Speaker 1: When projects spa when multiple layers, the real challenge isn't
Speaker 1: writing code, it's holding all the moving parts together. This
Speaker 1: is where the tension between multi file chaos and AI
Speaker 1: driven context control shows up most clearly. Take a large
Speaker 1: net solution with a dozen or more projects. Any new
Speaker 1: feature usually touches different layers at once, a controller in
Speaker 1: one place, a service in another, and a set of
Speaker 1: configuration files that live elsewhere. Before you write a single line,
Speaker 1: you spend time tracing references and checking dependencies, hoping a
Speaker 1: small change doesn't ripple into unexpected breaks further down the chain.
Speaker 1: That workflow isn't an exception. It's normal in enterprise applications,
Speaker 1: especially once as your services and integrations enter the picture.
Speaker 1: The structure of these systems isn't flat. Interfaces, dependency injection, mappings,
Speaker 1: and cross project references all play a role with azure
Speaker 1: in the mix. Some dependencies step completely outside the solution
Speaker 1: folder function apps, service bus bindings, resource settings, storage connections.
Speaker 1: You're coordinating between code and your IDE config files on
Speaker 1: disc and services defined in the cloud. None of them
Speaker 1: care that you you'd like fewer clicks, and every time
Speaker 1: you switch context, you burn energy reconstructing the bigger picture.
Speaker 1: Most of us try to juggle that context in working memory.
Speaker 1: At first it's manageable, but as the project grows, mistakes
Speaker 1: slip in. You add a new method in a service
Speaker 1: but forget its ye registration. You code up an asure
Speaker 1: function and only later realize the binding never got added
Speaker 1: to host Jason or the deployment template. Nothing alerts you
Speaker 1: until runtime, when you're debugging instead of building the code itself.
Speaker 1: Isn't the hard part. It's the cross file coordination. Everyone
Speaker 1: knows the feeling of bouncing through tabs from a controller
Speaker 1: into a service, then over to a model, then into
Speaker 1: configuration files, then back again, only to lose track of
Speaker 1: why you opened that file at all. It's a small disruption,
Speaker 1: repeated dozens of times a day. Those interruptions pile up,
Speaker 1: creating friction that drags down real progress. The result is
Speaker 1: slower delivery, not because writing is slow, but because keeping
Speaker 1: everything in sync steals focus. This overhead grows in cloud
Speaker 1: first projects as you pushes key settings into multiple places,
Speaker 1: local config files, environment variables, arm or BICEP templates, ICD pipelines.
Speaker 1: What looks like a single feature request often spreads across
Speaker 1: four layers of abstraction. The complexity isn't optional, it's built
Speaker 1: into the way the ecosystem works. Now here's where agent
Speaker 1: mode enters as a potential shift. Instead of leaving all
Speaker 1: that orchestration to you, it's designed to hold broader context
Speaker 1: across multiple files. That means when you ask for a
Speaker 1: change in one layer, it doesn't ignore the others. In
Speaker 1: the demo, I'll create a new Azure function and show
Speaker 1: how the agent helps by generating the method body, producing
Speaker 1: the binding config, updating host Jason, and even suggesting the
Speaker 1: right DII registration. That's usually a multi step process scattered
Speaker 1: across different files. An agent can streamline it into one flow.
Speaker 1: This is not about replacing your judgment. It's about removing
Speaker 1: the repetitive bookkeeping so you can focus on the actual
Speaker 1: design choices. Humans can keep a rough outline in their
Speaker 1: heads or sketched on a whiteboard. An AI can track
Speaker 1: the details file by file without losing the thread. What
Speaker 1: feels like a huge cognitive load for us is just
Speaker 1: baseline context for the agent. The difference in practice is
Speaker 1: moving from fractured tab juggling to orchestrated changes that stay
Speaker 1: in sync. I'll also pull up the agent created pull
Speaker 1: request or diff during the demo so you can see
Speaker 1: exactly what edits were made. That visibility matters. You get
Speaker 1: full control to review and approve while the legwork of
Speaker 1: updating multiple files happens for use. So instead of spending
Speaker 1: an afternoon stitching fragments together, you direct the change once,
Speaker 1: confirm the generated updates, and move on to higher level design.
Speaker 1: The relief isn't just in saved clicks or keystrokes, it's
Speaker 1: in staining focused on solving actual problems rather than retracing
Speaker 1: how a dozen files connect. This advantage shows most clearly
Speaker 1: when things break, because even with stronger contacts handling systems fail,
Speaker 1: Confixed drift and mismatched references creep back in, and that's
Speaker 1: where the next test begins. How errors get tracked down
Speaker 1: and resolved once they surface, from error hunts to autonomous fixes.
Speaker 1: Think about how often you hit an error message that
Speaker 1: points straight at a single file. You follow the stack
Speaker 1: trace to the method it names, make a small adjustment,
Speaker 1: and hit rebuild. It feels like the obvious solution until
Speaker 1: the same error appears again. Just in a slightly different form.
Speaker 1: That's when you realize the stack trays showed the symptom.
Speaker 1: The real issue lives somewhere else entirely, maybe in a
Speaker 1: supporting class or hidden inside a confiict file you haven't
Speaker 1: opened all week. Every developer has faced that kind of misdirection.
Speaker 1: What looks like the problem isn't actually where the fix belongs.
Speaker 1: This eats uptime fast. You adjust one thing, rebuild weight,
Speaker 1: then a fresh error greets you, leading to another file,
Speaker 1: another tweak, and another rebuild. The loop looks productive because
Speaker 1: you're moving, typing, recompiling, but under the surface its trial
Speaker 1: and error more than actual resolution. That cycle can swallow hours,
Speaker 1: leaving you with tiny surface fixes but no real forward
Speaker 1: progress on the feature you started with. The real cost
Speaker 1: here is opportunity. While you're caught in the rebuild and
Speaker 1: retry rhythm, you're not solving business problems or shipping the
Speaker 1: functionality your users are waiting on. Momentum goes into guesswork
Speaker 1: instead of design. It feels active in the moment, but
Speaker 1: those hours don't add up to much beyond keeping the
Speaker 1: system from being broken. Across a team, this shallow motion
Speaker 1: slows everything down and creates a backlog of features that
Speaker 1: keeps sliding forward. Here's where an agent workflow begins to
Speaker 1: look different. The idea isn't stopping at the one line
Speaker 1: your stack trace highlights. Instead, it's designed to hold system
Speaker 1: level context, asking not just what should this file do,
Speaker 1: but what sequence of changes is needed across connected pieces
Speaker 1: to restore consistency. In practice, that means you may see
Speaker 1: it propose edits that span multiple files. For example, you'll
Speaker 1: see in the demo that when a method requires changes,
Speaker 1: it can suggest matching edits in related CONFIGX or deployment
Speaker 1: templates instead of leaving you to hunt them down. That's
Speaker 1: the jump from autocomplete to something broader. Autocomplete finishes lines
Speaker 1: and agent coordinates across files, and that coordination matters most
Speaker 1: when errors don't live neatly in one place. Take a
Speaker 1: common Azure scenario. You build a new function app, but
Speaker 1: once deployed, the Q trigger fails because the binding doesn't
Speaker 1: match the method signature. Normally, you dig through logs, figure
Speaker 1: out which binding is off, adjust the function jjasent by hand,
Speaker 1: maybe even alter your infrastructure template if a values mismatched
Speaker 1: there too. Every step is a separate chase and every
Speaker 1: fix triggers another test. Run. With agent mode, the workflow
Speaker 1: is different. It can propose the code chain, change, generate.
Speaker 1: The proper function is in binding and surface edits for
Speaker 1: deployment scripts. If they're misaligned, you review, confirm, and move
Speaker 1: forward without spending hours piecing each layer together. Then trust
Speaker 1: is the key here. Nobody should feel like invisible edits
Speaker 1: are happening in the background. That's why the review flow matters.
Speaker 1: In this demo, we'll walk through it together. The agent
Speaker 1: suggest the coordinated changes will open the diff to inspect
Speaker 1: exactly what it generated. Run our unit tests, validate the
Speaker 1: build locally, and only then choose whether to accept or
Speaker 1: reject the edits. That validation loop keeps you in full
Speaker 1: control while removing the grunt work. It's worth stressing agents
Speaker 1: can help move faster, but they don't replace good engineering practices.
Speaker 1: You should still treat code reviews and CI as non
Speaker 1: negotiable gatekeepers. Let the AI reduce the time you spend
Speaker 1: on detective work, but keep automated checks and human review
Speaker 1: as the safety net. That balance solves the trust problem
Speaker 1: and ensures the speed gain doesn't undermine stability. The speed
Speaker 1: difference is not theoretical. Where error chasing and manual patching
Speaker 1: made you up half a day, coordinated suggestions can narrow
Speaker 1: it to minutes, and the reclaimed time flows back into
Speaker 1: the work you actually want to spend energy on the
Speaker 1: features your users. Notice the architecture decisions that improve your
Speaker 1: code base. Instead of firefighting at runtime, you get to
Speaker 1: design with confidence upfront, so the debugging loop no longer
Speaker 1: has to define your day With an agent suggesting cross
Speaker 1: file updates. You shift from scattershot searching to a review
Speaker 1: and approve rhythm. You stop wandering through errors in circles
Speaker 1: and start treating debugging as a structured, almost automated step
Speaker 1: in your workflow. That shift frees up cognitive space and
Speaker 1: calendar hours for building features, not just patching flaws, And
Speaker 1: once fixing errors becomes less about chasing symptoms, it opens
Speaker 1: the door to something bigger. How you might start whole
Speaker 1: features in the same structured way. Imagine if the same
Speaker 1: workflow that proposes coordinated fixes could also take a plane
Speaker 1: language specification and shape a working structure around it. That's
Speaker 1: where the next stage of development begins, spec driven development
Speaker 1: without the overhead. One of the most interesting shifts comes
Speaker 1: when you stop thinking only in terms of files and
Speaker 1: methods and start framing work in plane language instead. That's
Speaker 1: where spec driven development without the over head comes in picture.
Speaker 1: Writing out a simple feature request, add a reporting workflow
Speaker 1: that generates monthly summaries, stores them, and makes them available
Speaker 1: through an admin page. Instead of just getting a few
Speaker 1: isolated snippets, you return with a working structure already mapped
Speaker 1: across your app, controllers stubbed in, models, created, services registered,
Speaker 1: and configuration wired. That move from describing intent to seeing
Speaker 1: a concrete scaffold appear in your project is where this
Speaker 1: approach finally feels practical instead of theoretical in traditional setups.
Speaker 1: Spec first development has a heavy reputation in large organizations.
Speaker 1: It usually means long requirement dogs, multi page design sheets,
Speaker 1: and rigid diagrams that slow everyone down. They make sense
Speaker 1: in regulated industries or globally distributed teams, but for everyday
Speaker 1: coding most developers skip them. Writing and maintaining detailed specs
Speaker 1: ads cost. Nobody has the patience or time for it's
Speaker 1: extra work stacked on top of shipping features, and as
Speaker 1: deadlines press closer, those extra cycles are usually sacrificed. The
Speaker 1: irony is clear. Developers actually like thinking in broader strokes.
Speaker 1: Knowing the structure ahead of time is reassuring. The problem
Speaker 1: isn't the intent, it's the upkeep. Once the spec starts
Speaker 1: slipping out of sync with reality, the maintenance becomes a burden.
Speaker 1: That's why so much of real life work drifts toward
Speaker 1: improvisation rather than complete design, even in shops that technically
Speaker 1: endorse heavy planning. An agent workflow offers an alternative instead
Speaker 1: of demanding a polished design doc, it can help translate
Speaker 1: a plain language spec into a scaffold you can refine.
Speaker 1: You don't need UML diagrams or handwritten interface maps. You
Speaker 1: can simply say, create a reporting module with a new
Speaker 1: API endpoint, link it to storage, and secure it with
Speaker 1: role based access, and the system generates a baseline across
Speaker 1: files in your a net solution. In the demo, I'll
Speaker 1: read a plain language spec aloud, then show the files
Speaker 1: the agent produced controllers, models and service registration, and point
Speaker 1: out the spots that still needed manual refinement. That way,
Speaker 1: you can judge the quality for yourself. This sets up
Speaker 1: a middle ground between two developer modes. On one side,
Speaker 1: there's vibe coding fast freeform but fragile for long term systems.
Speaker 1: On the other side, there's spec driven design, reliable but painfully.
Speaker 1: With agent support, you outline the idea in natural words,
Speaker 1: the scaffold shows up and you can iterate almost as
Speaker 1: quickly as vibe coding, while keeping the benefit of an
Speaker 1: organized structure. Take that reporting workflow again. Usually you'd have
Speaker 1: to create the model, build the data service, wire up
Speaker 1: the controller, configure security, and connect everything in startup. That
Speaker 1: means bouncing through multiple files and hoping consistency holds. With
Speaker 1: this approach, the scaffolding lands in place at once. The
Speaker 1: real savings come less from typing fewer lines and more
Speaker 1: from avoiding cross file slips, like forgetting to register a
Speaker 1: new service after you've already built it. Of course, there's
Speaker 1: always the question of style. Will an AI force boiler
Speaker 1: plate or overwrite conventions. In practice, agents often mirror existing
Speaker 1: project patterns in their suggestions. In the demo, you'll see
Speaker 1: both sides. Places where it matched our repost controller structure
Speaker 1: perfectly and places where its gases slipped out of alignment.
Speaker 1: That mix is important. You'll know what you can trust
Speaker 1: and what you still need to adjust. The end result
Speaker 1: isn't cookie cutter code. It still feels like your project,
Speaker 1: only with less manual scaffolding work. You get to keep
Speaker 1: speed and s still rely on a foundation that you
Speaker 1: can refine for future growth. Instead of spec work being
Speaker 1: limited to architects. With the patients for diagrams, it becomes
Speaker 1: a tool for any developer who wants to experiment quickly
Speaker 1: without sacrificing order. And when this becomes part of your
Speaker 1: daily flow, specification stops feeling like a formal enterprise step
Speaker 1: and more like a lightweight shorthand. You describe your intent,
Speaker 1: the system lays the groundwork, and you focus on shaping
Speaker 1: the details. The bigger impact comes not just from the
Speaker 1: code you generate, but from what the time savings mean
Speaker 1: week after week. More hours for features, less grind and
Speaker 1: set up. That brings us directly to the question every
Speaker 1: developer cares about most, what these changes actually add up
Speaker 1: to in terms of productivity, the productivity payoff. The real
Speaker 1: question is not whether you save a few minutes here
Speaker 1: and there, but how those small shifts accumulate into meaningful
Speaker 1: hours across a week. This is the productivity payoff, regaining
Speaker 1: time that usually slips unnoticed through context switching, repeated build cycles,
Speaker 1: and manual patching. Most developers accept those small losses as
Speaker 1: just part of the word. Ten minutes here, chasing a
Speaker 1: config value another fifteen rerunning after a missed reference, or
Speaker 1: bouncing between files to check connections. On their own, they
Speaker 1: feel minor, but added together across a full sprint, they
Speaker 1: shape how much actual value gets delivered. What looks like
Speaker 1: routine motion hides a real drag on delivery timelines. The
Speaker 1: hidden weight isn't in compile times or typing speed. It's
Speaker 1: in the stop start rhythm imposed on your concentration. Every
Speaker 1: time you move from one file to another, your brain resets.
Speaker 1: That reset has a cost Offloading repetitive corrections to an
Speaker 1: agent lightens that burden. Instead of constantly reconstructing context, you
Speaker 1: spend focus where it matters, solving the bigger design problems.
Speaker 1: Many teams don't track these micro costs because they don't
Speaker 1: appear in gyra tickets or get history. Work that goes
Speaker 1: nowhere isn't logged, but it still consumes energy, and as
Speaker 1: code bases scale, this friction doesn't just add linearly. More
Speaker 1: layers mean more dependencies to hold in memory and more
Speaker 1: chances to lose time simply aligning structure before progress can continue.
Speaker 1: Agent workflows change the mass by targeting these drains directly.
Speaker 1: They don't just fill methods stopsquicker. They reduce the loops
Speaker 1: of searching, patching, and rerunning that eat afternoons. Teams using
Speaker 1: agent workflows report shorter time to unblock in many cases.
Speaker 1: In this video will demonstrate one before after task so
Speaker 1: you can judge the impact. I'll record the time it
Speaker 1: takes to implement the same asue of feature manually and
Speaker 1: then again with the agent, so you can see the
Speaker 1: productivity gain in concrete terms from a business lens. The
Speaker 1: value shows up in project velocity. Faster cycles aren't only
Speaker 1: about developer satisfaction. They decide whether features ship in this
Speaker 1: release or slip quarters forward. They influence technical debt. Since
Speaker 1: fewer hacks and regressions lean into the backlog, a smoother
Speaker 1: flow lets teams move ahead cleanly instead of revisiting broken
Speaker 1: work from the sprint before. That consistency compounds into less
Speaker 1: fire fighting and stronger delivery over time. There's also the
Speaker 1: human factor. A day loss to error chasing leaves any
Speaker 1: engineer drained. Once you're fatigued. Clean design work gets harder
Speaker 1: detailed slips and mistakes creep in. By shifting mechanical fixes
Speaker 1: to an agent, developers stay alert for longer stretches. That
Speaker 1: extra focus sharpens both productivity and quality. When you're not
Speaker 1: grounded down by repeated friction, you're free to think broadly
Speaker 1: and plan more effectively. The difference in practice looks like this.
Speaker 1: A new as your function with multiple bindings and service
Speaker 1: integrations might stretch into two full days, unaided between configuration
Speaker 1: testing and backtracking from mismatched references. The task drags on.
Speaker 1: With an agent helping, the same function can emerge in
Speaker 1: half a day, not because corners get cut, but because
Speaker 1: the cross files set up and orchestration land consistently the
Speaker 1: first time. The timeline shrinks by removing false starts and
Speaker 1: redundant effort. Some developers voice of fair concern. Does relying
Speaker 1: on an AI to handle maintenance dull your own skills.
Speaker 1: The perspective often misses what's really shifting. Offloading repetitive setup
Speaker 1: doesn't weaken your expertise, it preserves it for design and architecture,
Speaker 1: where judgment creates leverage. In reality, you gain room to
Speaker 1: practice higher order problem solving instead of wasting energy on
Speaker 1: road corrections. If you try this out, comment with how
Speaker 1: many hours you've reclaimed in a sprint. I'll pull interesting
Speaker 1: responses into future videos. Hearing how other teams experience the
Speaker 1: shift gives everyone a better picture of the real value
Speaker 1: beyond demos and examples. A note of caution, though automation helps,
Speaker 1: but safety still matters. Always run your tests and use
Speaker 1: code review to validate agent created changes. Trust that the
Speaker 1: busy work gets reduced, but keep the same guardrails in place.
Speaker 1: The speed difference only pays off if the quality holds steady.
Speaker 1: Strong tests and smart pr review protect your release pipeline
Speaker 1: from fragile automation. The payoff is less about cranking out
Speaker 1: lines of code quickly and more about freeing space to
Speaker 1: produce stronger solutions with less fatigue and fewer distractions. Teams
Speaker 1: move past patching to actual building. That shift opens the
Speaker 1: door to a broader mindset where projects are less about
Speaker 1: reacting to fires and more about shaping clear, organized systems
Speaker 1: from the start, and that brings us to an even
Speaker 1: larger point. When you stop spending your energy in piecemeal loops,
Speaker 1: the nature of building software begins to look very different.
Speaker 1: Coding workflows don't have to stay reactive. The point of
Speaker 1: this walkthrough was to show how agent support changes where
Speaker 1: your time goes. You move from fire fighting to building
Speaker 1: features with fewer interruptions while still keeping full control over
Speaker 1: what ships. Here are the three takeaways. One, agent workflows
Speaker 1: reduce cross file friction. Two you can use plain language
Speaker 1: specks to scaffold features, and three always review difts and
Speaker 1: run CI before merging. Try Copilot's agent flow on a
Speaker 1: real problem this week. Pick the task that usually eats
Speaker 1: in afternoon time it and compare. Drop the outcome in
Speaker 1: the comments. The agent suggests changes. You still review, test
Speaker 1: and decide what to merge. If you found this useful,
Speaker 1: like and subscribe for more hands on a I plus
Speaker 1: as your tooling walk throughs