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1. More context is not always better context

As agent systems grow, teams often keep adding system instructions, tool schemas, policy text, docs, recent chat history, and temporary notes into the same prompt. That increases cost and latency quickly, but it also makes the truly important information harder for the model to prioritize.

That is why context design should be treated as a layering problem rather than a volume problem.

2. The three-layer model

A practical structure is to separate context into three layers: a fixed prefix, a semi-stable knowledge layer, and a request-specific dynamic layer.

• Fixed prefix: system rules, role definition, response format, and tool contracts.
• Semi-stable knowledge: product policies, FAQs, and validated operating docs that change occasionally.
• Dynamic layer: the current user request, the most relevant recent history, and fresh tool output.

Once these layers are explicit, teams can reason separately about caching, summarization, and refresh policies.

3. Caching helps fixed layers, not uncontrolled accumulation

Prompt caching is useful when the same prefix is sent repeatedly. It improves speed and cost efficiency for stable instruction layers. But caching does not justify endlessly accumulating chat transcripts or temporary notes. Dynamic context still needs trimming, ranking, and summarization.

A common stable pattern is to cache the durable instructions and tool contracts while attaching a compact summary of the latest decisions at the end of the prompt.

4. Memory policy matters more than memory volume

Teams often focus on what to save and forget to define what should be deleted. Preferences, project state, and approved decisions may deserve structured memory. Temporary intermediate reasoning or already-consumed tool output usually should not survive for long.

In practice, deletion rules often improve context quality more than retention rules do. If you know what not to keep, the remaining context becomes more trustworthy.

5. Evaluate freshness and consistency, not just cache hit rate

Cache hit rate matters, but it is not enough. A highly efficient cache can still serve stale policy text or reinforce outdated guidance. Teams should monitor input token volume and latency, but also answer consistency, policy freshness, and session error rate.

The goal is not to keep every piece of context alive forever. The goal is to keep fixed layers stable and dynamic layers short, current, and explicit.