Managing Context Limits in Long Claude Runs: AC Tree Pattern

A developer on r/ClaudeAI describes a recurring failure pattern in long-running Claude sessions. The failures aren't obvious errors but gradual drift where the model's output becomes less aligned with the original intent.

The Problem: Auto-Compact and Context Limits

According to the source, auto-compact presents the first issue. It consumes a meaningful portion of the available context before productive work begins, and the compression isn't neutral—the model decides what information to preserve, leading to information loss. Primacy and recency bias exacerbate this: the middle of the run weakens first, early constraints fade, and decisions are made against fragmented intent.

Turning auto-compact off reveals the second problem: the hard context limit. Once a session fills up, it's effectively over with no clean continuation or recovery path.

The Solution: AC Tree Execution Pattern

The developer found success by changing the execution shape rather than just adjusting settings. The approach treats the input not as a finished specification but as a human goal. The system interviews the user to clarify intent, extract constraints, and surface hidden assumptions, then compiles this into an AC tree—a dependency graph of discrete, verifiable units of work.

Each node in the AC tree gets its own session, and nodes only know about each other through minimal durable state passed forward. This keeps the overall workflow long-running while making each generation short, bounded, and isolated. Drift gets contained at the node level instead of poisoning the entire run.

The developer implemented this pattern in a project called Ouroboros, available at github.com/Q00/ouroboros.