AI Coding Summit

AI Coding Summit NYC

AI coding agents work impressively when the task fits inside a few files. Enterprise software rarely does. A seemingly simple requirement can cross hundreds of files, shared libraries, APIs, data models, tests, infrastructure, and years of architectural decisions. At that scale, putting more source code into an LLM context window is not the same as giving an agent an understanding of the system. This talk shares lessons from building AI-augmented software development for large enterprise codebases. We’ll examine why file-level retrieval and ever-larger context windows break down, how coding agents can construct a task-specific model of a codebase using structural relationships and iterative exploration, and why planning and verification become more important than code generation as agent autonomy increases. The goal is to move the conversation from “How much code can the model see?” to “How does the agent determine what actually matters for this change?”

Kranthi Manchikanti