Programming Tutorials

  You are three hours into a critical architectural refactor. You’ve prompted Manus AI to decouple your monolithic Express services into micro-services. The agent analyzes the file structure, drafts a plan, begins writing the interface adapters, and then—abruptly stops. Error: Context Window Limit Exceeded. The agent has lost the thread. It cannot remember the interface definitions it wrote five minutes ago because the sheer volume of your codebase, combined with the agent's internal "thought chain" logs, has saturated the token buffer. This is the single biggest bottleneck in AI-driven development. This article details the technical root cause of this limitation and provides a programmatic strategy to circumvent it using Abstract Syntax Tree (AST) context injection. The Root Cause: Why Agentic Workflows Burn Tokens To solve the context limit, we must understand that Manus AI (and similar agentic LLMs) consumes tokens differently than a standard completion model like Clau...

  There is no workflow interruption quite as jarring as the "Usage limit exceeded" error from Anthropic in the middle of a complex refactor. If you are using Claude Code (the CLI agent) on a modern monolith or a large monorepo, you likely hit the 40-hour usage cap or daily token limits significantly faster than peers working on microservices. This isn't just about how  much  you use the tool; it is about how inefficiently the agent is navigating your file system. When an LLM agent creates a plan or searches a codebase, every character it reads consumes token quota. In large repositories,  irrelevant context is the silent killer of rate limits. This guide details exactly how the context window fills up during directory traversal and provides a programmatic solution to identify and eliminate "token hogs" from your agent's view. The Root Cause: Implicit Context Injection To understand why your quota evaporates, you must understand how Claude Code interacts with...