AI is taking over! Or is it? You’ve probably heard the hype, the hate, and the fear. Some say AI will steal jobs, others say it’s useless. The reality? It’s neither. AI isn’t magic, and it’s not a doomsday machine – it’s a tool. A powerful one. The real question is, how can we use it effectively?
Every new technology comes with skepticism. The internet was once ridiculed as a fad. People thought social media wouldn’t last. Yet, here we are. AI-agents are on a similar trajectory, and the sooner we understand their role, the better we can leverage them.
But first, what exactly are AI-agents? Why are they different from traditional AI? And most importantly, why should you as a business owner care?
Hello, my name is boogieman aka Roman Gorbunov and I have hands-on experience building and integrating AI-based solutions into software products or business processes. And today in this video I will share my vision of how AI-agents will change businesses based on real use cases from my professional experience. Buckle up, it is going to be exciting.
Before we jump, make sure you are subscribed to the channel. I release videos sometime a while, but they all contain very important in-depth insights about modern technological trends and how they transform our society so you can apply this knowledge for your career or your business and get ahead of the crowd. Like and comment if you like this or comment if you have any comment or questions.
The rise of AI and AI-agents has not only sparked excitement but also a wave of exaggerated claims, especially on YouTube. Many AI software development YouTubers exuberantly promote the abilities of AI with clickbait titles like ‘Build a Fully Fledged Application in 15 Minutes!’ or ‘AI Will Replace Developers Completely!’ — claims that are often misleading and far from reality. While AI can significantly enhance and accelerate software development, it is not a magic wand. Building robust applications still requires a solid technical background, strong problem-solving skills, and a deep understanding of software development processes. These so-called ‘AI gurus’ often oversimplify the complexities of development, creating false expectations for viewers. This video is my response to those misleading narratives — a guide for anyone seeking real, actionable advice on how to leverage AI in software development without the hype or false promises. No clickbait, no sales pitch — just honest insights into how AI can truly transform your workflows when used responsibly and with the right expertise.
Every industrial revolution has fundamentally reshaped the way we work and live. The first industrial revolution introduced steam power, enabling mechanized production and transforming industries like textiles and transportation. The second brought electricity and mass production, revolutionizing manufacturing with assembly lines and making goods more affordable and accessible. The third gave us computers and digital systems, automating calculations, data processing, and communication on a scale never seen before. Now, we’re in the midst of the fourth industrial revolution—driven by automation, artificial intelligence, and interconnected systems.
But AI-agents aren’t just another incremental software upgrade. They represent the next generation of tools in the ongoing quest for increased productivity. To understand their significance, let’s look at history. In manufacturing, the introduction of machinery replaced manual labor for repetitive tasks, allowing workers to focus on more skilled roles. Later, robotic arms on production lines automated precision tasks like welding and assembly, improving efficiency and reducing errors. These innovations didn’t eliminate the need for human workers – they enhanced their capabilities and allowed them to focus on higher-value activities. AI-agents are the digital equivalent of this evolution.
Whether you’re a developer, a business owner, or just curious about the future of AI, this video will help you understand why AI-agents are a game-changer. And no, you don’t need a PhD in machine learning to get started.
As an independent consultant with extensive hands-on experience in developing and integrating AI-driven solutions for businesses, I know firsthand that AI development and integration is a knowledge-intensive process. It’s not a one-size-fits-all solution, nor is it something that can be effectively handled by inexperienced individuals or so-called ‘AI gurus’ who lack the necessary technical depth and real-world expertise. Successfully implementing AI requires a senior level of experience, a deep understanding of business processes, and the ability to navigate the complexities of AI systems.
Business owners must be cautious when choosing who to trust with their AI initiatives. Working with unqualified professionals can lead to poorly designed solutions, wasted resources, and even harm to your business or idea. That’s why I focus on delivering tailored, high-quality solutions that align with your unique needs and goals. From strategic planning to deployment and optimization. Don’t leave your business in the hands of amateurs – partner with a professional who understands the stakes. Visit my website to learn more and start your journey toward AI success.
In 2017, the seminal paper “Attention is All You Need” introduced the transformer model by Vaswani et al., which revolutionized the field of NLP by enabling models to understand and generate language with unprecedented efficiency. This breakthrough laid the groundwork for subsequent models capable of translating human language into executable code.
Building on this foundation, OpenAI released GPT-3 in 2020, which demonstrated remarkable proficiency in generating human-like text and performing a variety of language tasks. This development paved the way for the birth of OpenAI Codex in August 2021, a model specifically tailored for code generation. Trained on code from over 54 million GitHub repositories, Codex was designed to parse natural language instructions and convert them into programming code across multiple languages. This capability was further popularized by its integration into GitHub Copilot, which provided real-time code autocompletion and assistance to developers, significantly enhancing productivity in software development.
These tools have revolutionized the Software Development Life Cycle (SDLC), enabling developers to describe tasks in plain language and have AI generate, debug, and optimize code. However, while these tools enhance productivity, they still require technical expertise to use effectively, emphasizing the need for skilled professionals in leveraging AI for software development and they do not present a complete, end-to-end solution allowing to deliver production ready software products. However, with the introduction of AI-agents, enhanced LLM, Software Development Life Cycle, as many other business use cases, has received a tremendous opportunity for development teams’ productivity and significant reduction in technical & security risks.
Artificial Intelligence, or AI, refers to systems that mimic human intelligence, often designed for specific tasks like fraud detection or image recognition. However, AI-agents take this a step further, they’re autonomous systems that don’t just follow predefined rules but analyze, learn, and act based on goals. Unlike traditional AI, which is static and task-specific, AI-agents are dynamic and adaptive, capable of handling complex workflows and making decisions independently. A key technology behind modern AI-agents is Large Language Models, or LLMs, like OpenAI’s GPT. These models are trained on massive datasets, enabling them to understand and generate human-like language. For example, tools like ChatGPT can simulate conversations, while AutoGPT can break down complex goals into smaller tasks and execute them autonomously. Imagine a customer service AI-agent: it doesn’t just answer FAQs but learns from interactions, improves over time, and escalates issues intelligently when needed. This is a huge leap from traditional chatbots, which rely on static scripts. By combining the adaptability of AI-agents with the language understanding of LLMs, businesses can automate entire functions, improve decision-making, and achieve greater efficiency. However, these systems still require proper expertise and oversight to unlock their full potential.
AI-agents are transforming industries by reshaping workflows and enabling unprecedented efficiency in many areas. Here are some key applications:
AI’s potential goes beyond isolated tasks, it transforms entire workflows. And this is especially evident in software development, where AI is revolutionizing the entire process, from planning to deployment.
The Software Development Life Cycle (SDLC) is a structured framework for building software, consisting of several key phases: planning, design, coding, testing, deployment, and maintenance. Of course, with Agile practices applied you iterate all the phases delivering small product increments, so you repeat this circle steps each sprint until a final result is reached. Each phase requires careful coordination to ensure the final product meets user requirements and business goals. Traditionally, these processes have been manual, time-consuming, and prone to human error, especially in complex projects. The biggest cost in human communication is brought by knowledge transaction cost and the smaller this cost the faster and the better product your development team delivers. However, the introduction of AI-agents is transforming how these phases are executed, making the process faster, more efficient, and less error-prone.
As we already understood, The Software Development Life Cycle (SDLC) has always been a complex process which included multiple phases where various specialists must be involved. Over time, various tools and methodologies have been introduced to optimize it.
In the 1940s-1960s, programming was manual, with developers writing machine-level instructions. The introduction of high-level languages like Fortran, along with compilers, simplified coding. The 1970s Waterfall model brought structure with a linear, phase-based approach, but its rigidity made adapting to changes difficult. Tools like flowcharts and Gantt charts supported this model.
The 1980s introduced iterative models, enabling feedback loops and partial implementation. CASE tools automated parts of the process, such as code generation. The Agile revolution in 2001 emphasized flexibility and iterative development, with tools like Jira and Trello improving collaboration.
In the 2000s-2010s, DevOps and CI/CD practices automated building, testing, and deployment, using tools like Jenkins and Docker to speed up releases. Low-code/no-code platforms like OutSystems further simplified development, allowing non-technical users to create applications.
While these advancements improved efficiency, SDLC still required significant manual effort, paving the way for AI and LLMs to automate complex tasks and streamline workflows.
Integrating AI into the Software Development Life Cycle (SDLC) requires a detailed understanding of each phase. By breaking down and documenting workflows, we identify opportunities for AI to add value and ensure each phase provides the necessary inputs for the next step in the integration process. This structured approach not only enhances efficiency but also maximizes the impact of AI tools on the development process. A great example of this is the AI-enhanced software delivery process illustrated in the chart provided, which breaks down the journey from idea to development into three key phases: Ballpark Estimation, Detailed Estimation, and Project Infrastructure Setup. Let’s explore how this process aligns with the SDLC phases and how AI integration transforms each step. I want to highlight that this scheme is designed with a very simple software product to be developed in mind, thus estimation and initialization of a more complex product will require more time and effort respectively.
The planning phase is where project goals, timelines, and costs are defined. This phase provides the foundation for AI-driven estimation and prioritization. In the Ballpark Estimation Phase (as shown in the chart), AI tools analyze initial project inputs and historical data to generate quick estimates within 30 minutes. And, of course, it will be crazy to fully rely on those estimations, but as my experience proves with a properly written prompt you’ll get a pretty accurate numbers range which is enough to get going.
Comment prompt from a verified account and I will be happy to share my unique software development estimation prompt to share with you in direct messages.
This rapid estimation process allows stakeholders to make informed decisions early, saving time and effort compared to traditional manual methods. Documenting these high-level requirements with AI-tools as well ensures that the next phase has a clear starting point with pre-generated information architecture diagram, backlog draft and high-level user stories documented, technical stack recommendations outlined.
The design phase focuses on creating detailed workflows, system architecture, and user experience designs. In the Detailed Estimation Phase, business analysts and solution architects use AI tools and diagramming platforms enhanced by AI to refine requirements and create system diagrams, wireframes, and user journey maps. Inputs such as user behavior data and industry best practices guide AI in suggesting improvements and ensuring alignment with business goals. This phase provides the technical and functional clarity needed for the coding and infrastructure setup phases which also serve already both as a production requirement and project charter artefacts to share with development teams and streamline their onboarding.
Coding: Setting Development Frameworks
The coding phase involves setting up the project infrastructure and writing the initial codebase. In the Project Infrastructure Setup Phase, AI tools automate the configuration of cloud environments, CI/CD pipelines, and other infrastructure components. By documenting the development environment, reusable code libraries, and coding standards, we ensure that AI can assist in code generation, optimization, and infrastructure setup. This automation reduces setup time, ensures consistency, and minimizes human error, allowing developers to focus on high-value tasks. And again, I know there are already platforms and tools allowing developers to launch projects on their infrastructure deploying right away to production even without any functionality ready. In our case I speak about a project requiring a more complex infrastructure, something like a web and a mobile client communicating with multiple backend servers and third-party applications.
Testing: Establishing QA Parameters
The testing phase ensures that the software meets performance, reliability, and security standards. I bet you might have seen memes about startups hiring one vibe coder at one price and then hiring a couple more senior developers at a double higher rate to fix the bugs generated by the vibe coder. And to be honest I do not think we will see a 100% accurate result generated by AI in the near future. Yes, the context window is growing, but just from empirical experience human review and user acceptance tests will now go away any soon. That is why a normal practice will be to outline test cases, scenarios, and metrics for software quality. Historical bug data and user feedback are critical inputs that allow AI to simulate user behavior, identify edge cases, and detect vulnerabilities. This automation not only accelerates the testing process but also improves accuracy and reduces the risk of errors.
Deployment: Outlining Release Strategies
The deployment phase focuses on releasing the software to testing and further to production. Deployment workflows, performance metrics, and monitoring requirements are documented to help AI automate and optimize releases. AI-agents tremendously increase a lot of manual setup work, however their ability to monitor performance metrics during deployment, identify potential bottlenecks, and ensure smooth rollouts is a new potential a few developers yet leverage. This proactive approach minimizes downtime and ensures a seamless user experience.
Maintenance: Monitoring and Feedback
The maintenance phase involves monitoring system performance, analyzing user feedback, and implementing updates. AI tools use real-time performance data and feedback to recommend optimizations, predict system failures, and prevent downtime. By documenting maintenance schedules, update plans, and anomaly patterns, we ensure that AI has the necessary inputs to support long-term system stability and improvement.
The AI-enhanced software delivery process chart exemplifies how breaking down and documenting the SDLC at a granular level enables effective AI integration. Each phase is clearly defined, with inputs and outputs that guide the next step. For example:
By thoroughly understanding and documenting each phase, we create a roadmap for AI integration that ensures tools are applied efficiently and effectively. This structured approach not only enhances productivity but also reduces costs, shortens development cycles, and maximizes the value AI brings to the software development process.
So once you documented the whole business process workflow or part of it as in our example. The next step is to decide what tools to integrate. Choosing the right AI tools is a critical step in successfully integrating AI into your workflows. With the rapid advancement of AI technologies, a variety of platforms are available to suit different needs, from advanced developers to non-technical users. For non-technical users, no-code/low-code platforms also democratize AI integration, however in more complex use cases it still requires a solid technical background to realize certain automation.
The accessibility of these platforms ensures that businesses of all sizes, not just large enterprises with big budgets, can leverage AI to enhance productivity and efficiency. Whether you’re a developer looking for advanced tools or a business owner seeking simple, cost-effective solutions, the best rule to select the right solution is to estimate efforts needed and compare it against future benefits brought by the improvement. Thus we came to understanding the investment potential of AI-agent integration.
Integrating AI into your business is about creating real value, not just automating for the sake of it. Unfortunately, the rise of fake gurus and overhyped promises has led to misguided implementations that fail to deliver meaningful results. Successful AI integration requires a clear understanding of your business processes, goals, and the potential return on investment (ROI).
Some examples of poorly thought-out AI implementations highlight the importance of understanding the why behind automation. For instance, businesses using AI-agents to comment on social media under fake avatars may think they’re boosting engagement. However, when people discover that an AI is behind the comments, especially on serious or sensitive topics, it often upsets users and damages trust. Similarly, using AI to sort and reply to massive volumes of emails without understanding the purpose of the communication can lead to irrelevant responses, wasting resources and frustrating recipients. Another example is mass-producing AI-generated blog content without considering quality or relevance, which can harm a brand’s credibility.
To avoid these pitfalls, businesses must focus on meaningful automation that solves real problems. Our AI-Agent ROI Calculator helps you do just that by analyzing key factors such as:
For example, automating a customer service process with AI can reduce response times, lower staffing costs, and improve customer satisfaction. Since we integrated AI-powered tools to our SDLC we’ve seen a 30-50% increase in development speed and fewer errors, leading to faster delivery and lower costs. Don’t fall for empty promises or waste resources on misguided implementations. Use our AI-Agent ROI Calculator to make informed, data-driven decisions about AI integration and see how automation can transform your business with real, measurable results.
In upcoming videos, we’ll showcase real businesses that have successfully integrated custom AI-agents into their workflows. And one standout use case is an integrated functionality generating PRDs (product requirements artefacts), an AI-agent that transforms how businesses move from idea to execution. The PRD includes key features, user roles, acceptance criteria, and more, giving businesses a clear roadmap for development. From there, users can request a quote for AI-powered app development, delivered at a fraction of the cost and time of traditional methods.
Make sure you are subscribed to the channel or blog as we dive deeper into this already realized real-world use cases in the upcoming videos.
We’ve covered a lot today, from debunking AI hype and exposing misguided implementations to showcasing real-world applications and how AI-agents are transforming the Software Development Life Cycle (SDLC). We also teased some exciting use cases with AI-agent streamlining Product Requirements Documenting and shared insights into how businesses can achieve measurable results with AI integration.
In our next video, we’ll explore the evolution of AI agents and how AI-agents have evolved from simple chatbots to powerful tools that drive innovation and efficiency across industries.
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Thanks for watching, and I’ll see you in the next video!