AI agentic workflows on large codebases

The first post went over some of its capabilities. Over the past week Edict went v1.0, adding cursors for reading projections after command dispatch (to close some eventual-consistency gaps), a new type of projection that holds state inside the Orleans grain directly instead of a table, saga timeouts, schedules, an improved skills package and MCP server that ships with Edict, and more. Edict has now grown to over 75,000 lines of code and more than 1000 tests, and contains several deep mechanisms that have been fixed, broken, and fixed again. It is well past the point where I can hold all of Edict in my head. This post is about working with AI on large codebases, which I expect to be the first problem most software engineers have to solve. The context problem Years ago I was talking to a PhD candidate whose area of research was Natural Language Processing (NLP). He explained to me that one of the most difficult NLP problems was context. If a colleague says they need to pop out to pick their kids up from school, a scene can form in your head: one with a school, the layout of the road, people waiting, walking, driving, the environs. You may never have seen the school your colleague mentioned, but you can form a rich scene from your accumulated experience and use it to drive the rest of the conversation with a shared understanding. LLMs ingeniously dodge this entire issue by making it your problem. Just a word-probability machine Strip away the chat window and a Large Language Model (LLM) is doing one thing: predicting the next token. Give it a run of text and it returns a probability distribution over what comes next, samples one, appends it, and repeats. Companies like OpenAI and Anthropic then beat it into shape using techniques like supervised fine-tuning and reinforcement learning, which tune those probabilities in meaningful ways. That is why Claude is always telling me "Good framing" or "You've spotted...". It even called me "Bold" on one occasion. The probabilit