AI Coding Agent Prompt Caching: What to Cache, Where to Put It, and When It Saves Money
By Eric Bush · July 6, 2026 · 9 min read
Prompt Caching Is a Layout Problem Before It Is a Pricing Feature
Prompt caching sounds like a provider-side billing feature, but for AI coding agents it starts with prompt layout. The model provider can only reuse tokens that appear in a stable, repeated form. If your agent rebuilds its instructions, tool schemas, memory, file summaries, timestamps, and task state in a different order every turn, you may be paying full price for content that should have been cacheable.
The practical rule is simple: put stable content first, keep dynamic content last, and avoid changing the cached prefix unless the meaning truly changes. This one design choice can matter more than switching to a slightly cheaper model, especially for long-running coding agents that repeat the same repository rules and tool definitions across many turns.
What Belongs in the Cached Prefix
The cached prefix should contain the content that is large, important, and stable. For coding agents, that usually includes the system role, safety boundaries, repository instructions, tool schemas, coding style rules, test commands, and long-lived project memory. These tokens are sent repeatedly, but they do not need to be reinterpreted from scratch on every turn if the provider supports caching for that segment.
| Cache candidate | Where to place it | Stability rule |
|---|---|---|
| System instructions | Very beginning | Change only when agent policy changes. |
| Tool schemas | Before task context | Keep ordering and field descriptions stable. |
| Repository rules | Before file context | Use the same AGENTS.md or policy block each turn. |
| Test commands | Near repository rules | Avoid rewriting commands into different prose each call. |
| Long-lived memory | After stable rules | Update at checkpoints, not after every observation. |
What Should Stay Outside the Cached Prefix
Dynamic content belongs after the cacheable prefix. This includes the latest user message, current tool output, fresh error logs, timestamps, temporary plans, command results, diff snippets, and files that are changing during the session. If this content appears before stable content, it can break prefix matching and force the provider to treat everything after it as new.
The most common mistake is placing a changing session summary near the top of the prompt. It feels natural to start with the latest state, but it can poison caching. Put the stable contract first, then the dynamic state. The model can still read the latest state; it just appears after the reusable foundation.
A Good Agent Prompt Order
- System role and non-negotiable behavior. Stable across sessions and agent turns.
- Tool schemas and tool-use rules. Stable ordering, stable field names, stable examples.
- Repository and team instructions. Coding style, test commands, deployment rules, and review constraints.
- Long-lived memory. Stable facts about the project that change only at deliberate checkpoints.
- Task-specific file context. Relevant files and summaries, preferably ordered consistently.
- Latest user request and tool outputs. The most dynamic content comes last.
This order does not guarantee savings on every provider, because provider cache behavior differs. It does maximize the chance that repeated input is reusable. It also makes the prompt easier to reason about when debugging agent behavior.
When Prompt Caching Saves Real Money
| Workflow | Caching value | Reason |
|---|---|---|
| One-shot code question | Low | Not enough repeated context to amortize setup. |
| Interactive bug fix | Medium | Tools and repository rules repeat across turns. |
| Long-running coding agent | High | Large stable prefix repeats many times. |
| Multi-agent workflow | High if shared prefix is stable | Agents can reuse the same policy, tool, and project context. |
| Heavy retrieval every turn | Mixed | Savings depend on whether retrieved context repeats unchanged. |
The Break-Even Check
Prompt caching saves money when the discounted repeated tokens exceed any extra cache-write cost, engineering work, or complexity introduced by cache management. A simple break-even check is to log ten representative tasks before and after caching. For each task, record full input tokens, cached input tokens, output tokens, cache writes, cache reads, request count, and task success.
Then compare dollars per successful task. Do not stop at cache hit rate. A high cache hit rate can still disappoint if the agent produces more output tokens, retries more often, or uses a more expensive model to access caching. The meaningful metric is the final cost of accepted work.
Implementation Checklist
- Make prompt serialization deterministic. Stable ordering matters for cache reuse.
- Remove timestamps from the prefix. Put time-sensitive data near the end.
- Version your tool schemas. Schema changes should be deliberate and measurable.
- Keep memory compact. Large memory that changes constantly is the enemy of caching.
- Add cache metrics to traces. Store cache writes, cache reads, misses, and billed token classes.
Bottom Line
AI coding agent prompt caching works best when the prompt is designed for it. Stable instructions, tool schemas, repository rules, and long-lived memory belong at the front. Dynamic logs, tool results, and latest requests belong at the end. Use provider billing data to prove savings, then model the before-and-after token mix in the AI Cost Estimator before changing team-wide routing policy.
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Frequently Asked Questions
What should be cached in an AI coding agent prompt?
Cache stable system instructions, tool schemas, repository rules, coding standards, test commands, and long-lived project memory. These repeat often and usually change slowly.
Where should dynamic tool results go?
Put dynamic tool results, fresh logs, timestamps, temporary plans, and latest user instructions after the stable cached prefix so they do not break cache reuse.
Does prompt caching always save money?
No. It saves money when repeated input tokens are large, cache hit rates are high, provider discounts are meaningful, and output or retry costs do not erase the savings.
What is the best metric for prompt caching ROI?
Use dollars per successful task, not cache hit rate alone. Include full input, cached input, output, retries, cache writes, and task success.
What is the most common prompt caching mistake?
Putting changing session state near the top of the prompt. That can prevent the provider from reusing the stable instructions and tool definitions that follow it.
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