What separates an AI coding agent that stays on task from one that constantly loses the thread? Mostly, it's context - specifically, how that context is structured before the agent writes a single line of code.
Context engineering has emerged as a practical discipline for developers working with AI coding tools. The core idea: models like Claude don't just respond to your immediate question. They respond to everything in their context window (the total amount of text - code, instructions, conversation history - they can process at once). Feed them the wrong information, or too much noise, and output quality drops fast.
For multi-step coding workflows, this compounds quickly. When you're running subagent workflows - where one AI orchestrates a task by delegating subtasks to other agents - each hand-off carries context baggage. A subagent that inherits a bloated or poorly organized context will produce inconsistent results not because the underlying model is incapable, but because it's reading the wrong signal.
A few patterns are proving reliable:
- Scope isolation: Give each subagent only the context it needs. A code-review agent doesn't need the full planning conversation.
- Explicit task boundaries: State what success looks like for each subtask, not just the overall goal.
- State summaries over raw history: When one agent hands off to another, pass a structured summary of decisions made and why - not the raw conversation transcript.
Claude Code, which runs coding agents locally via the terminal, is where most of this experimentation is happening. Its subagent system lets you spawn parallel or sequential agent threads, each with its own context budget. Most of what works is being discovered through trial and error rather than official documentation.
If your multi-step agent pipelines are producing inconsistent results, context structure is the first variable worth examining - before you blame the model.