Smart Caching Strategies for AI Agent Tool Results
Smart Caching Strategies for AI Agent Tool Results
An AI agent reads your calendar, checks your email, pulls a Slack thread, reads a file, then combines everything to give you a summary. Each tool call takes 1-3 seconds. With 10 tool calls, that is 10-30 seconds of waiting. The obvious solution is caching.
The obvious solution is also wrong - at least the simple version of it.
Why TTL Caching Breaks Agents
Time-to-live caching stores results for a fixed duration. Cache the calendar for 5 minutes. Cache Slack messages for 2 minutes. Simple, predictable, and guaranteed to give you stale data at the worst possible time.
You ask the agent "what's my next meeting?" It returns the cached result from 4 minutes ago - missing the meeting that was just added 30 seconds ago. The agent confidently tells you "nothing until 2 PM" while your 11 AM standup looms.
TTL caching treats all data as equally time-sensitive. But calendar data that is 5 minutes old might be fine during a quiet afternoon and dangerously stale during a morning of rescheduling.
Dependency-Tracking Caches
A smarter approach tracks what each cached result depends on. The calendar result depends on calendar events. If no events were added, modified, or deleted since the cache was written, the cached result is still valid - whether that is 30 seconds or 30 minutes later.
For desktop agents, this means watching for changes at the source. Did the calendar app receive a notification? Did the file's modification timestamp change? Did Slack show new message indicators? These signals are cheap to check and give you invalidation without polling.
Tiered Freshness
Not all data needs the same freshness guarantee. Categorize tool results into tiers.
Real-time - always fetch fresh. Active conversations, pending notifications, running processes.
Near-real-time - check for changes, serve cache if unchanged. Calendar, email inbox, file contents.
Stable - cache aggressively. Contacts, settings, project structure, historical data.
The agent should know which tier each tool belongs to and adjust its caching strategy accordingly.
Cache Warming
The best cache hit is one that was pre-loaded before you asked. Agents that run on a schedule can warm caches during idle time - pulling calendar data, checking email, indexing recent files. When you ask a question, the answer is already assembled.
Fazm is an open source macOS AI agent. Open source on GitHub.