Beyond File Dumps: Context Engineering for Coding Agents

AI coding agents are getting better, but might still fail if they are given the wrong context. Developers often paste raw files, random chunks, stack traces, or half the repository into an LLM and hope the model figures it out. This might not perform well for large codebases. This session shows how to build a local-first context layer for AI coding agents using Chronicle, an open-source Python SDK and MCP server. We will walk through how Chronicle prepares structured context before an LLM call using AST-indexed symbols, dependency graphs, patch-aware retrieval, call-chain context, session memory, and token-aware handoff packets. The talk includes a live demo on a real repository: starting from a code-change request, we will inspect the repo, identify impacted files and symbols, generate a compact context packet, and hand it off to an AI coding agent such as Codex, Claude, Cursor, or any MCP-compatible workflow. Attendees will learn the architecture behind context orchestration, how to reduce token waste without losing codebase relevance, and how to design inspectable, reusable context systems for AI-assisted software development. This talk is about making AI coding workflows more grounded, debuggable, and reliable. Github repo: https://github.com/animeshdutta888/chronicle/tree/main SDK with MCP integration: https://pypi.org/project/chronicle-sdk/