Manus developed a framework for autonomous AI agents that prioritizes "context engineering" over fine-tuning proprietary models. This model-agnostic approach allows for rapid iteration and leverages the power of frontier large language models (LLMs). The core of their strategy is maximizing the KV-cache hit rate, which they identified as the most critical metric for reducing both latency and operational costs in production.

By ensuring the initial part of the agent's context remains stable, Manus achieves a 10x cost reduction on token processing. This is done by using append-only context, where new information is always added without modifying previous steps, and by ensuring all data is serialized deterministically. To manage the agent's available tools without breaking this cache, they mask the probability of certain actions at the decoding stage rather than removing tool definitions from the context. This allows the agent's capabilities to be dynamically constrained based on the task state.

To overcome the inherent context window limitations of LLMs, even those with 128K+ tokens, the agent is taught to use the local file system as an unlimited, persistent external memory. It can read and write files to store intermediate results or large documents, effectively giving it an infinite context. For long tasks, the agent maintains a "todo.md" file and recites its goals into the context to maintain focus and prevent "lost-in-the-middle" attention drift. Finally, errors are intentionally kept in the context, allowing the agent to learn from mistakes and adapt its strategy, a key marker of true agentic behavior.