AI is reshaping software development, and the focus is shifting from writing code to understanding it. In this episode of the Analytics Insight podcast, host Priya Diyalani speaks with Amar Goel, Co-founder and CEO of Bito, about how AI is reshaping software development. The conversation explores the rise of context engineering, the shift from writing code to understanding systems, and how AI is evolving into an architectural partner.
A: Bito builds AI agents and tools for development teams, with a focus on context engineering. While model quality matters, the real differentiator is context, how well AI understands a codebase, architecture, and system behavior. It’s not just about prompt inputs, but whether the AI grasps dependencies, design patterns, and real-world usage. Without that depth, output quality suffers significantly.
A: Writing code has always been just the output. The real value lies in designing scalable, resilient systems. AI is now accelerating code generation, but that shifts the bottleneck upstream toward system design and comprehension. Developers increasingly need to review and work with AI-generated code, which requires deeper understanding. If you don’t fully grasp what’s being built, you risk introducing errors at scale.
We’re moving toward agentic development, where AI can take a task, from a ticket to a pull request with minimal human input. While still evolving, this is already happening for smaller features. Humans remain critical in planning, validating, and refining outputs. AI can dramatically reduce time spent on analysis and implementation, but human oversight ensures accuracy and relevance.
A: Their role becomes even more important. They define system architecture, feasibility, and long-term scalability. AI can assist by providing a first-pass analysis, reducing work from days to hours, but human expertise is essential to guide decisions. Poor upfront design leads to costly rework later, so this stage remains critical despite automation.
A: It analyzes entire codebases across repositories, identifying services, APIs, and design patterns. It builds a graph of how components interact, tracking dependencies, data flows, and system behavior. This enables teams to assess impact, debug issues, and understand how changes affect the broader system. Essentially, it provides a system-level view rather than isolated code insights.
A: Knowledge graphs help organize fragmented data into connected insights. In large systems with thousands of services, developers often lack visibility into existing functionality. This leads to duplication and inefficiency. A structured, evolving understanding of the system ensures reuse, consistency, and better collaboration, reducing technical debt over time.
A: There’s a risk of cognitive offloading, where developers rely too heavily on AI and engage less deeply with the code. However, AI also makes intelligence more accessible and affordable. The key shift is toward judgment, deciding what to build, how to build it, and ensuring it aligns with business goals. That’s where human expertise remains indispensable.
A: Developers will move from execution to decision-making. AI can generate code, but humans define problems, validate solutions, and ensure real-world relevance. Skills like system thinking, architectural design, and product intuition will become more valuable than pure coding ability.
A: Software development will see a massive increase in output, but the real transformation lies in how humans and AI collaborate to design, understand, and build systems at scale.