I went from a team of over two dozen engineers to an AI-powered team of 6. Here's my advice for engineers told to embrace AI.

In today's fast-paced tech landscape, adaptability is essential, especially when transitioning from a large team to a compact, AI-driven group. Shivam Sagar, a senior software engineer at Aragon AI, shares his journey from a 25-member engineering team to a nimble crew of just six. Sagar emphasizes the importance of flexibility over perfection in this new environment. The dynamics have shifted dramatically; what once felt structured in a larger organization now demands a more fluid approach. "I underestimated how mentally exhausting the individual responsibility would be," he reflects, adding that this newfound ownership has also proven liberating. Working within a small team allows for an integrated approach to problem-solving, where engineering, product design, and user experience converge. This collaboration fosters creativity and accelerates decision-making, as there are fewer barriers to getting things done. "In a smaller setup, if I spot a task that needs attention, I can tackle it without waiting for approval from multiple layers of management," Sagar notes. One of the key lessons he's learned is the value of staying close to users. In smaller teams, each interaction with customers can significantly influence the product roadmap, often more effectively than extensive research. Sagar recounts how, in his first six months, he immersed himself in coding, often feeling overwhelmed. However, as he gained a deeper understanding of the product, the workload became more manageable, enabling him to make more intentional decisions about his time and focus. The shift to a smaller team has also altered Sagar's work-life balance. While it can be busier, he appreciates the clarity that comes with having better context and control over his tasks. The absence of a large support system means he must be proactive in learning the codebase, developing features, and conducting quality checks independently. In a rapidly evolving landscape driven by AI, Sagar notes that priorities and tools can change swiftly. This requires a commitment to open communication and regular knowledge sharing to avoid the pitfalls of isolation that can occur in smaller setups. Successful teams, he believes, are those that embrace experimentation and learn quickly from their failures, rather than striving for perfection from the outset. The insights shared by Sagar highlight the unique challenges and rewards of working in a tiny team, especially in the AI sector. Those interested in sharing their experiences in similar environments are encouraged to reach out and contribute to this growing narrative.