Does Prompt Caching Help Tool-Calling Agents? Cache Behavior in Multi-Step Loops

How Prompt Caching Works

Prompt caching stores the internal state of a model's processing for a given prefix of input tokens. When a subsequent request shares the same prefix, the model can reuse the cached state instead of recomputing it from scratch. This can reduce both latency and cost, depending on the provider's caching implementation.

Caching typically operates on a prefix basis: only the initial portion of the prompt that matches a previous request can be served from cache. If any token in the prefix changes, the cache cannot be reused from that point onward.

The Agent Loop and Growing Prompts

Tool-calling agents operate in a loop:

1. The agent receives a user request and a set of available tools.
2. The model decides to call a tool and generates a tool call.
3. The tool executes and returns a result.
4. The result is appended to the conversation history.
5. The model is called again with the updated history.

Each turn, the prompt grows by the tool result and the model's previous output. This growth pattern is critical for caching.

When the Cache Hits

A cache hit occurs when the new prompt starts with the exact same token sequence as a previously cached prompt. In an agent loop, this happens if:

• The system prompt and initial user message are identical across calls.
• All previous turns (tool calls and results) are included in the same order without modification.
• No dynamic content (like timestamps or random IDs) is inserted at the beginning.

If the agent simply appends new messages to the end of the history, the prefix (everything before the new messages) remains unchanged. The cache can then be reused for that prefix, and only the newly appended tokens need to be processed. This is the ideal scenario for caching.

When Appending Breaks the Prefix

Appending does not always preserve the prefix. Common pitfalls include:

• Modifying earlier messages: If the agent edits, summarizes, or reorders previous messages, the prefix changes and the cache is invalidated.
• Inserting dynamic content early: Adding a timestamp, request ID, or user-specific metadata at the start of the prompt breaks the prefix for every request.
• Changing the system prompt: Altering the system prompt between calls invalidates the entire cache.
• Injecting tool results in the middle: If tool results are inserted before existing messages (e.g., to maintain a specific order), the prefix is disrupted.
• Using non-deterministic formatting: Varying whitespace, JSON serialization order, or tokenization can alter the token sequence and prevent cache hits.

Even a single token change early in the prompt can prevent cache reuse for the entire sequence.

Best Practices for Cache-Friendly Agent Prompts

To maximize cache hits in multi-step loops:

• Keep the system prompt and initial instructions static across all turns.
• Append new messages (tool calls, results, model outputs) strictly at the end.
• Avoid inserting dynamic data at the beginning; place it as late as possible.
• Ensure deterministic serialization of tool calls and results.
• Use consistent formatting for all messages.
• If summarization or truncation is necessary, do it in a way that preserves the longest possible prefix.

Caching and API Aggregators

When using an API aggregator that routes to multiple model providers, caching behavior depends on the underlying provider. Some providers support prompt caching natively, while others may not. The aggregator itself does not change how caching works; it simply passes through the request. Therefore, the same prefix rules apply.

If you are using a pay-as-you-go aggregator with transparent pricing, caching can still reduce your effective cost because cached tokens are often billed at a lower rate. However, always check the provider's documentation for specific caching support and pricing.

Conclusion

Prompt caching can significantly benefit tool-calling agents, but only when the prompt prefix remains stable. In multi-step loops, appending new content at the end preserves the prefix and allows cache hits. Any modification to earlier parts of the prompt breaks the cache. By designing agent prompts with a static prefix and appending only new messages, you can achieve consistent cache hits and reduce both latency and cost.