That and then understand that how we can leverage GenAI to reshape not just our productivity, but also relationship with our work.How we can collaborate, lead, and protect our creative energy that makes this career better. So a question, how much of your week is spent on work that doesn't require your unique expertise? Well, not just busy work, but the work that follows a pattern that you have done before. That's something with a good runbook could do. For most of us, including me, that is a shocking amount. We got into this field to think, to design, to solve hard problems. But somewhere along the way, we got buried in execution, the same execution over and over again. Let me paint the picture.

These are the things I see eating time across engineering organizations every single day, and I bet you recognize most of them regardless of your role. If you are an engineer, it's writing the same boilerplate code across services, running the same migration 100 times, or triaging incidents by following a mental runbook. If you're a manager or SDM, it's chasing status updates, compiling the same reports, or manually tracking dependencies across teams. If you're a PM, it's writing the same templates, updating stakeholders with information that already exists somewhere, or reconciling road maps across tools. Well, the pattern is same no matter your role. Repetitive, predictable work that follows a known playbook, consuming time that could go towards your strategy, creativity, and high judgment decision. Studies show that over 60% of the knowledge work time goes into this kind of operational toil. And the cost isn't just lost productivity. It's deeply human.

Cognitive fatigue is something that we get when we are into this repetitive cycle. And what it means is that by the time you finish your repetitive stuff, you have no mental energy left for creative problem solving. Eighty three percent of the developers report burnout on average in an IT field. Top engineers leave work when it stops becoming meaningful. And the painful irony is that we have brilliant ideas for improving our systems, but we never get to build them because there's no time. We are just too busy keeping the lights on. So here is where it gets exciting. What if we could flip this? What if instead of you doing everything, thinking and executing, you could purely focus on the thinking, designing, and architecture part and let AI handle the repetitive execution?

That is a new model that I want to share with you. So this is a fundamental shift. In the old model, we do everything, the creative thinking and the tedious execution, the strategy and the status updates, the design, and the repetitive implementation. In the new model, you become the architect of your work. You think, you design, you make the judgment calls, the things that require expertise, your context, and your creativity. And you delegate AI agents, the execution part of it, the predictable part, and the repeatable parts. Now you can say that this is about losing control, but it really isn't. Leveraging AI to do the repetitive part for you is about gaining leverage. Whether you are writing code or managing a team or driving a product, you are not doing less work, but you are doing better work with AI, the the kind of work that actually needs human judgment.

And we can do most of this by developing AI agents for our work, for our teams, and for our organizations. And what exactly are AI agents? Well, you would think of them as a GenAI plus autonomy plus tools plus guardrails, where you you know that if you if you know what the input to your work is, what the output to your work is, you can write in natural language what you want the agent to do. They can use APIs. They can use command lines, file systems, databases, or any other service that you want your agent to talk to. So critically, they operate within the boundaries you define. Think of them as the new engineers who join your team, who never get bored of the repetitive task, work around the clock because they have so much energy to do the stuff and follow your guidance exactly and escalate when they think they hit something they can't handle. That is exactly what we want the AI agents to do today. You define their behavior. You define them how you want a a certain problem to be solved.

And if they hit something, a new problem, or if they hallucinate, they escalate it to you. They notify you either via Slack, via email, via text message, however you wanna configure it. Now that way, you have deli you it's like a delegator or assistant that is doing exactly what you are saying. So what are the things that AI agents can automate today? Almost everything across the roles, whether it's engineering, management, product, building, or whatever. For builders and developers, it could be generating boilerplate code from specs, migrating code across services, writing and maintaining test, producing documentation, handling dependency upgrades at scale. For operations and platforms team, it could be incident triaging, config propagation, runbook execution, environment provisioning.

And for managers and PMs, it could be drafting status reports from existing data, compiling metrics across tools, generating stakeholder updates, tracking action items from meeting notes, etcetera. Now notice the pattern here. Regardless of the volume regardless of the role or the job level you are in, these are all high volume, well defined, predictable task. The kind that will drain your energy without requiring your creativity or judgment. So here's how the AI agentic workflow actually looks like in practice. You, as a human, you identify the problem, you design the solution, you define the plan and constraints, you review everything, and then you approve the output. Now when you trigger an agent or invoke an agent, it breaks down the plan in two steps, executes each of the steps. Now they they could be running your code, reading your documentation, changing your config files, doing status updates, whatever you have defined it.

Validate it will validate all your work and then generate reports that it can send back to you for review. So it's like you you stay in the driver seats, and then AI is handling the distance for you. You are not abandoning responsibility. You are delegating execution while retaining ownership of the design and decisions. Now let me share what this looks like in practice with real numbers. Here's a real case study from my experience. We had a challenge in our work where hundreds of services needed the modernization change it changes. And then each change followed a similar pattern but in a different context. And what it needed was for engineers to move from one stack to a newer tech stack. And most of the work involved where engineers were copy pasting and adapting to a newer stack for months.

So what we did was we built a Gen AI powered agent that understood our older stack, that understood our new stack, understood the code pattern so that it was able to migrate that service and then the entire code logic for us. So agents generated the changes. We validated them and then submitted them for review. So engineers went from spending days on each service to spending minutes reviewing and approving. It's like a new engineer or a new teammate in your team has done the work you asked them to do. Now they come back to you. They ask you to review. You approve it. And then the changes are done. Imagine that every work that you do in your day to day life can be like that. And what it means is that we start finding the pattern in our work that can be automated and delegated to agents.

How that helps is that it reduces the engineering toil. In my own org, it reduced, like, 87% engineering toil. The work that would have taken months was completed in days. And most importantly, engineers were redirected to architecture and innovation work that they had been postponing. And here's the part that really matters. What happened with all that reclaimed time? Well, people started doing the work they had been putting off for quarters. Architects finally had space to redesign systems that had been held together with duct tape. Leaders invested in mentoring and growing their teams. PMs explored customer problems they had never had bandwidth to take into. Teams invested in learning new tools, new approaches, and new domains, and the energy shifted from internal plumbing to work that actually impacts users and customers.

But here's the real deal. This is a burnout connection. People reported higher job satisfaction and lower stress, not because the job got easier, but because the work started to feel meaningful again. When you remove a crushing repetitive stuff, people don't just perform better. They actually start enjoying it and showing up more. So how do you actually do this? Let me give you a practical framework for building your own AI agents. Like, think of an AI agent, like, delegating of your work to a really reliable teammate of yours. You need to give them four things, a clear task, a playbook to follow, the right tools to do the job, and the boundaries so that they don't go off script.

That's it. You tell the agent what to do, how to do it, what tools it can use, and where to stop and ask you. The better you set those up, the more you can trust it to run on its own. And the less you define guardrails, the more you need to babysit. It's the same way you'd onboard a new temp team member, just faster and more scalable. Here's a simple litmus test for identifying what to automate. Ask yourself four questions. Do I do this the same way every time? If yes, automate it. Could I write a book for this? If yes, then you can have a agent SOP for this. Does this require my judgment or just my time? If it just requires your time, absolutely delegate it to an agent. If it requires judgment, no. It's your work to do it.

And my favorite, would I be embarrassed if this is what I spend my week on? Well, yeah, if yes, then definitely automate it. The best candidates are high volume, low creativity, well defined task. Here's a step by step workflow for going from idea to working agent. Step one, you document the manual process. You write down exactly what you want, what what your work needs to do, what what is the thing you need to do to get this deliverable done. Then step two, you define your inputs. You define your outputs and success criteria. What does done right look like to you? Step three, build the agent with all these natural language guardrails we talked about in step two, and then have a way to invoke it.

Step four, test on low risk samples first. For example, you want to you wouldn't want to give it a word to go and change a production database for testing. Right? You would start with something that doesn't have that of a big blast radius. And even if the agent does something wrong, it's probably okay. And then see how the agent is performing. If it is doing good, start giving it more medium to complex task and more challenging task. And the way and the point at which it starts giving you inaccurate results means that something is wrong with your guardrails. You need to define or refine your SOP for agents, and you need to train it much better. So it's like a iterative cycle where the agent is actually performing better by actually running the test samples.

Every time it gives you inaccurate results, that's an opportunity for you to refine the SOP, to refine the guardrails, and make sure that if the same test is run again, the agent gives you a right answer. And keep repeating the step until and unless you are satisfied that the answer is right. Once you do that, all of this involves human review where now you are ready to actually use your agent for your work. You define a step where after the agent is done, it notifies you for human review and sorry. Give me a sec. Yeah. It it notifies you for human review. And once a human approves, only and only then it makes the changes in production or any of the work. So the confidence builds over time. So the key is starting small and expanding as trust grows. Now let's talk about what happens to team dynamics when you make the shift. The transformation is profound.

Stand up conversations shift from I'm still working on migrations to I designed a new caching strategy. The agent handled the rollout. Junior engineers, they had the potential to grow faster because AI agents handle the grunt work, and seniors finally have time to mentor. Equity improves because office housework can become more interesting. And those thankless repetitive tasks, they can automate instead of being assigned to the same people over and over. And the team identity evolves from we maintain the system to we design the solutions, and that's a powerful cultural shift according to me. Now this only works if you do it responsibly. The rule is simple. The human decides the AI only executes. Start simple. Pick boring, low risk task first. Always keep yourself in the loop for anything that matters and measure more than just speed And look at the outcome whether the results are accurate, and if they are, then you are deploying these agents in your team.

Are people happier? Are you shipping faster? Are you delivering more products? Those are the kinds of indicator that will tell that the AI agents can be responsibly adopted. Now there are two traps to avoid. First is, we need to shift from a mental model of AI can do it and turn into we need fewer people. The goal is better work, not fewer people. And the second, don't stop understanding the systems you have automated. We are still responsible for the work that is produced by our AI agents. So if we are not able to understand what the agents did, no one else will. So it is our responsibility to review and understand everything the AI agent has produced because at the end, we are the owners of our products. So here's your plan of action, something that you can start this week. List your top five most repetitive task.

Then next week, pick the most painful one and write a runbook of it. Then you build or configure an agent to handle it. And last, you review the outputs, refine, and measure the time saved with it. When you keep on doing this over time, you will eventually want to move to a next task and share with your team. And if you keep on iterating over it, you'll eventually reach a position where you have avoided or automated most of the repetitive work in your team. And the only work that remains is where you use human judgment, and that is where your team comes into picture. Remember the core principle. You plan, you design, and the AI executes. That's not less engineering. It's better engineering. So the key takeaways are human plus AI equals superpower. You bring creativity and judgment.

AI just brings trial and execution. And together, you are unstoppable. Second, agents are your new team members. Build them for repetitive work. Free yourself for your big ideas. And third, teams thrive when toil disappears. It leads to better morale, better mentorship, and faster innovation. And the meta key takeaway, start small, think big. One automated workflow today can transform your entire team tomorrow. So stop spending your brilliance on busy work. You are too talented, too creative, and too valuable to be doing copy paste work all day. Design the future. Let AI build the repetitive parts. Thank you so much for your time today. I would love to connect with all of you. You are free to find me on LinkedIn or come chat after the session.