Context management in agent harnesses: memory, files, and subagents

Context management in agent harnesses is a critical aspect of ensuring AI agents remain coherent and effective over long sessions, as they navigate growing file reads, subagent calls, and tool outputs that can overwhelm the context window. The article explores the strategies employed by four systems—Pi, OpenClaw, Claude Code, and Letta—to manage this challenge, focusing on their approaches to file reading, context compaction, and subagent management. Each system implements hard caps on file reads and tool result sizes while employing LLM-powered summarization to maintain coherence without exhausting the context window. Although their specific methods differ, all four systems converge on a pattern of treating context as a managed resource, thereby allowing agents to dynamically manage their own context, ensuring that only the most relevant data is retained at any given time. This convergence reflects broader trends in computing, where effective memory management is largely invisible to the layers above, enabling efficient and seamless program execution.

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