Symptom
A user asks “Is product P-100 in stock?” The agent confidently says “Yes, 42 units available” — but the cache is 3 hours old and the item sold out at noon. Or the agent uses a stock price from before market open. Or it returns a user’s old email address after the user updated their profile. The response looks authoritative but is wrong.
Root Cause
Cached data has no expiry, no staleness check, and no indication to the LLM or user that the data may be outdated. Once a value is cached, it is returned forever. The agent has no concept of data freshness — it treats a 3-hour-old cache hit identically to a live API response.
Fix
Option 1: TTL-Based Cache with Freshness Metadata Injection
Add TTL to every cache entry. Include a _data_freshness field in tool results so the LLM can communicate staleness to users.
import time
import json
import hashlib
import asyncio
import anthropic
from dataclasses import dataclass
client = anthropic.AsyncAnthropic()
@dataclass
class CachedValue:
data: dict
fetched_at: float
ttl_seconds: float
@property
def age_seconds(self) -> float:
return time.monotonic() - self.fetched_at
@property
def is_fresh(self) -> bool:
return self.age_seconds < self.ttl_seconds
@property
def freshness_label(self) -> str:
age = self.age_seconds
if age < 60:
return "just now"
elif age < 300:
return f"{int(age // 60)} minute(s) ago"
elif age < 3600:
return f"{int(age // 60)} minutes ago"
else:
return f"{age / 3600:.1f} hours ago"
# Per-tool TTL configuration (seconds)
TOOL_TTLS = {
"get_stock_price": 15, # Stock prices: 15 seconds
"get_inventory": 60, # Inventory: 1 minute
"get_user_profile": 300, # User profile: 5 minutes
"get_exchange_rate": 3600, # Exchange rates: 1 hour
"get_static_config": 86400, # Config: 24 hours
}
_cache: dict[str, CachedValue] = {}
def cache_key(tool_name: str, args: dict) -> str:
payload = json.dumps({"tool": tool_name, **args}, sort_keys=True)
return hashlib.sha256(payload.encode()).hexdigest()[:16]
async def cached_tool_call(tool_name: str, args: dict) -> dict:
"""Execute tool with TTL caching. Annotates result with freshness info."""
ttl = TOOL_TTLS.get(tool_name, 60)
key = cache_key(tool_name, args)
entry = _cache.get(key)
if entry and entry.is_fresh:
print(f" [Cache HIT] {tool_name} — fetched {entry.freshness_label}, TTL {ttl}s")
return {
**entry.data,
"_data_freshness": f"cached {entry.freshness_label}",
"_cache_ttl_seconds": ttl,
"_is_live": False,
}
# Stale or missing — fetch fresh
print(f" [Fetching LIVE] {tool_name}({args})")
await asyncio.sleep(0.1) # Simulate API call
if tool_name == "get_stock_price":
data = {"symbol": args["symbol"], "price": 182.50, "currency": "USD"}
elif tool_name == "get_inventory":
data = {"product_id": args["product_id"], "in_stock": True, "count": 37}
elif tool_name == "get_user_profile":
data = {"user_id": args["user_id"], "name": "Alice", "email": "alice@example.com"}
elif tool_name == "get_exchange_rate":
data = {"from": args["from_cur"], "to": args["to_cur"], "rate": 1.085}
else:
data = {"result": "ok"}
_cache[key] = CachedValue(data=data, fetched_at=time.monotonic(), ttl_seconds=ttl)
return {**data, "_data_freshness": "live", "_cache_ttl_seconds": ttl, "_is_live": True}
TOOLS = [
{"name": "get_stock_price", "description": "Get current stock price. Data may be up to 15 seconds old.",
"input_schema": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}},
{"name": "get_inventory", "description": "Check inventory levels. Data may be up to 1 minute old.",
"input_schema": {"type": "object", "properties": {"product_id": {"type": "string"}}, "required": ["product_id"]}},
{"name": "get_exchange_rate","description": "Get exchange rate. Data refreshes every hour.",
"input_schema": {"type": "object", "properties": {"from_cur": {"type": "string"}, "to_cur": {"type": "string"}}, "required": ["from_cur", "to_cur"]}},
]
SYSTEM = """You are a helpful assistant. When reporting data from tools:
- Always mention the data freshness (e.g., "as of 2 minutes ago")
- For time-sensitive data (stock prices, inventory), explicitly note when it was last updated
- If data is cached, say so rather than presenting it as live"""
async def run_agent(message: str) -> str:
messages = [{"role": "user", "content": message}]
while True:
response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=512,
system=SYSTEM,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
result = await cached_tool_call(block.name, block.input)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
print(asyncio.run(run_agent("What's the stock price for AAPL and is product P-100 in stock?")))
Expected Token Savings: Freshness metadata adds ~10 tokens per result but prevents users from acting on stale data — avoiding costly downstream errors. Environment: Async Python, in-memory cache. TTLs configurable per tool.
Option 2: Tiered Cache with Stale-While-Revalidate
Serve stale data immediately while refreshing in the background. Zero latency for users, eventual freshness.
import asyncio
import time
import json
import hashlib
import anthropic
from dataclasses import dataclass, field
client = anthropic.AsyncAnthropic()
@dataclass
class CacheEntry:
data: dict
fetched_at: float
soft_ttl: float # Serve fresh up to this age
hard_ttl: float # Never serve beyond this age (force-refresh)
refresh_task: asyncio.Task | None = field(default=None, compare=False)
@property
def age(self) -> float:
return time.monotonic() - self.fetched_at
@property
def is_soft_stale(self) -> bool:
return self.age > self.soft_ttl
@property
def is_hard_stale(self) -> bool:
return self.age > self.hard_ttl
@property
def staleness_tag(self) -> str:
if not self.is_soft_stale:
return "fresh"
if not self.is_hard_stale:
return f"revalidating (data is {self.age:.0f}s old)"
return f"expired ({self.age:.0f}s old)"
_swr_cache: dict[str, CacheEntry] = {}
# Soft TTL: serve as fresh. Hard TTL: block until refreshed.
CACHE_CONFIG = {
"get_weather": (30, 300), # Fresh for 30s, serve up to 5min while refreshing
"get_rates": (60, 600), # Fresh for 1min, serve up to 10min
"get_inventory": (10, 120), # Very volatile: 10s fresh, 2min hard limit
}
async def _fetch_live(tool_name: str, args: dict) -> dict:
"""Simulate live API fetch."""
await asyncio.sleep(0.15)
if tool_name == "get_weather":
return {"city": args["city"], "temp": "19°C", "condition": "partly cloudy", "ts": time.time()}
if tool_name == "get_rates":
return {"pair": f"{args['from']}/{args['to']}", "rate": 1.087, "ts": time.time()}
if tool_name == "get_inventory":
return {"sku": args["sku"], "count": 14, "ts": time.time()}
return {}
async def swr_get(tool_name: str, args: dict) -> dict:
"""Stale-while-revalidate cache lookup."""
soft_ttl, hard_ttl = CACHE_CONFIG.get(tool_name, (60, 600))
key = f"{tool_name}:{hashlib.sha256(json.dumps(args, sort_keys=True).encode()).hexdigest()[:12]}"
entry = _swr_cache.get(key)
if entry is None or entry.is_hard_stale:
# No cache or hard expired — must fetch synchronously
print(f" [SYNC FETCH] {tool_name} (no cache or hard-expired)")
data = await _fetch_live(tool_name, args)
_swr_cache[key] = CacheEntry(data=data, fetched_at=time.monotonic(), soft_ttl=soft_ttl, hard_ttl=hard_ttl)
tag = "fresh"
elif entry.is_soft_stale:
# Soft stale — serve immediately, refresh in background
tag = entry.staleness_tag
print(f" [SERVE STALE + BG REFRESH] {tool_name} — {tag}")
async def _bg_refresh():
fresh = await _fetch_live(tool_name, args)
_swr_cache[key] = CacheEntry(data=fresh, fetched_at=time.monotonic(), soft_ttl=soft_ttl, hard_ttl=hard_ttl)
print(f" [BG refresh done] {tool_name}")
if not entry.refresh_task or entry.refresh_task.done():
entry.refresh_task = asyncio.create_task(_bg_refresh())
else:
tag = "fresh"
print(f" [CACHE HIT] {tool_name} — age {entry.age:.0f}s")
return {**_swr_cache[key].data, "_freshness": tag}
TOOLS = [
{"name": "get_weather", "description": "Get weather. May serve slightly stale data.",
"input_schema": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}},
{"name": "get_inventory", "description": "Check inventory.",
"input_schema": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}},
]
async def run_swr_agent(message: str) -> str:
messages = [{"role": "user", "content": message}]
while True:
response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=512,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
result = await swr_get(block.name, block.input)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
print(asyncio.run(run_swr_agent("What's the weather in Berlin and how many units of SKU-500 are left?")))
Expected Token Savings: Background refresh eliminates user-visible latency for cache misses. Hard TTL prevents serving data older than 2× soft TTL. Environment: Async Python with background tasks. Works within a single event loop.
Option 3: Explicit Cache-Busting for High-Stakes Queries
Detect user intent signals that require live data. Bypass cache on “right now”, “current”, “latest” queries.
import re
import time
import json
import hashlib
import anthropic
client = anthropic.Anthropic()
# Phrases that signal the user wants truly live data
FRESHNESS_REQUIRED_PATTERNS = [
r"\b(right now|this second|immediately|urgent|asap)\b",
r"\b(current|live|real-?time|up.?to.?date|latest|today)\b",
r"\b(just happened|just changed|recently updated)\b",
r"\b(before I buy|before I order|before I decide)\b",
]
_cache: dict[str, tuple[dict, float]] = {}
DEFAULT_TTL = 120
def requires_fresh_data(user_message: str) -> bool:
msg_lower = user_message.lower()
return any(re.search(p, msg_lower, re.IGNORECASE) for p in FRESHNESS_REQUIRED_PATTERNS)
def get_cached(tool_name: str, args: dict, bypass: bool = False) -> dict | None:
if bypass:
return None
key = f"{tool_name}:{hashlib.sha256(json.dumps(args, sort_keys=True).encode()).hexdigest()[:12]}"
entry = _cache.get(key)
if entry:
data, ts = entry
if time.time() - ts < DEFAULT_TTL:
return data
return None
def set_cached(tool_name: str, args: dict, data: dict):
key = f"{tool_name}:{hashlib.sha256(json.dumps(args, sort_keys=True).encode()).hexdigest()[:12]}"
_cache[key] = (data, time.time())
def execute_tool(tool_name: str, args: dict, bypass_cache: bool = False) -> dict:
cached = get_cached(tool_name, args, bypass=bypass_cache)
if cached:
print(f" [Cache] {tool_name} — using cached ({DEFAULT_TTL}s TTL)")
return {**cached, "_freshness": "cached"}
print(f" [Live] {tool_name}({'CACHE BYPASSED' if bypass_cache else 'cache miss'})")
if tool_name == "get_price":
result = {"item": args["item"], "price": 299.99, "in_stock": True, "ts": time.time()}
elif tool_name == "get_order_status":
result = {"order_id": args["order_id"], "status": "shipped", "eta": "2025-04-17"}
else:
result = {"status": "ok"}
set_cached(tool_name, args, result)
return {**result, "_freshness": "live"}
TOOLS = [
{"name": "get_price", "description": "Get product price and availability.",
"input_schema": {"type": "object", "properties": {"item": {"type": "string"}}, "required": ["item"]}},
{"name": "get_order_status", "description": "Get order status.",
"input_schema": {"type": "object", "properties": {"order_id": {"type": "string"}}, "required": ["order_id"]}},
]
def run_agent(user_message: str) -> str:
bypass = requires_freshness_required = requires_fresh_data(user_message)
if bypass:
print(f" [Freshness required] Bypassing cache for this query")
messages = [{"role": "user", "content": user_message}]
while True:
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=512,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
result = execute_tool(block.name, block.input, bypass_cache=bypass)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
# Cache works normally
print(run_agent("What's the price for the Pro Widget?"))
print(run_agent("What's the price for the Pro Widget?")) # cache hit
# High-stakes: bypass cache
print(run_agent("What's the current live price for the Pro Widget right now? I'm about to buy."))
Expected Token Savings: Cache hit rate stays high for casual queries. Freshness bypass used only for the ~10–15% of queries that genuinely need live data. Environment: Synchronous Python. Regex patterns are editable to match your domain.
Option 4: Event-Driven Cache Invalidation
Invalidate cache entries when the underlying data changes, rather than relying on TTL expiry.
import time
import json
import hashlib
import asyncio
import anthropic
from collections import defaultdict
client = anthropic.AsyncAnthropic()
# Cache stores: key → (data, timestamp)
_cache: dict[str, tuple[dict, float]] = {}
# Tag → set of cache keys (for tag-based invalidation)
_tag_index: dict[str, set[str]] = defaultdict(set)
def cache_key(tool_name: str, args: dict) -> str:
return hashlib.sha256(json.dumps({"t": tool_name, **args}, sort_keys=True).encode()).hexdigest()[:16]
def cache_set(tool_name: str, args: dict, data: dict, tags: list[str] = None):
key = cache_key(tool_name, args)
_cache[key] = (data, time.monotonic())
for tag in (tags or []):
_tag_index[tag].add(key)
def cache_get(tool_name: str, args: dict) -> dict | None:
key = cache_key(tool_name, args)
entry = _cache.get(key)
return entry[0] if entry else None
def cache_invalidate_tag(tag: str):
"""Invalidate all cache entries associated with a tag."""
keys = _tag_index.pop(tag, set())
for key in keys:
_cache.pop(key, None)
if keys:
print(f" [Cache invalidated] tag='{tag}' → {len(keys)} entries cleared")
def cache_invalidate_tool(tool_name: str):
"""Invalidate all entries for a specific tool."""
to_delete = [k for k in _cache if k.startswith(f"{tool_name}:")]
for k in to_delete:
del _cache[k]
print(f" [Cache invalidated] tool='{tool_name}' → {len(to_delete)} entries cleared")
async def fetch_live(tool_name: str, args: dict) -> dict:
await asyncio.sleep(0.1)
if tool_name == "get_product":
return {"product_id": args["product_id"], "name": "Widget Pro", "price": 49.99, "stock": 23}
if tool_name == "get_user":
return {"user_id": args["user_id"], "name": "Bob", "email": "bob@example.com"}
return {}
async def tagged_tool_call(tool_name: str, args: dict) -> dict:
cached = cache_get(tool_name, args)
if cached:
print(f" [Cache HIT] {tool_name}")
return {**cached, "_source": "cache"}
data = await fetch_live(tool_name, args)
# Tag entries for selective invalidation
tags = []
if tool_name == "get_product" and "product_id" in args:
tags = [f"product:{args['product_id']}", "all_products"]
elif tool_name == "get_user" and "user_id" in args:
tags = [f"user:{args['user_id']}"]
cache_set(tool_name, args, data, tags=tags)
return {**data, "_source": "live"}
# Simulate an event stream that triggers invalidation
async def simulate_product_update(product_id: str):
"""Called when product data changes in the source system."""
print(f"\n [EVENT] Product {product_id} updated — invalidating cache")
cache_invalidate_tag(f"product:{product_id}")
async def main():
TOOLS = [
{"name": "get_product", "description": "Get product details.",
"input_schema": {"type": "object", "properties": {"product_id": {"type": "string"}}, "required": ["product_id"]}},
]
async def agent_query(msg: str) -> str:
messages = [{"role": "user", "content": msg}]
while True:
response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=256,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
result = await tagged_tool_call(block.name, block.input)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
print(await agent_query("Tell me about product P-500."))
print(await agent_query("Tell me about product P-500.")) # cache hit
await simulate_product_update("P-500") # Invalidate on product update event
print(await agent_query("Tell me about product P-500.")) # Cache miss — fetches fresh
asyncio.run(main())
Expected Token Savings: Event-driven invalidation means cache is always fresh after changes — no need for short TTLs. Longer TTLs possible, more cache hits overall.
Environment: Event-driven. Wire cache_invalidate_tag() to your webhook handlers, CDC stream, or message queue consumers.
Option 5: Data Versioning — Include ETag/Version in Cache Keys
Use ETags or version tokens to detect stale data without TTL.
import asyncio
import json
import hashlib
import anthropic
client = anthropic.AsyncAnthropic()
# Version store: entity → current_version (updated by source system)
_entity_versions: dict[str, str] = {
"product:P-100": "v3",
"user:U-42": "v1",
"config:main": "v12",
}
# Cache: (key, version) → data
_versioned_cache: dict[tuple[str, str], dict] = {}
def get_current_version(entity: str) -> str:
return _entity_versions.get(entity, "v0")
def bump_version(entity: str):
"""Called when entity is updated."""
parts = _entity_versions.get(entity, "v0").split("v")
new_v = int(parts[1]) + 1 if len(parts) == 2 else 1
_entity_versions[entity] = f"v{new_v}"
print(f" [Version bumped] {entity}: {_entity_versions[entity]}")
async def versioned_fetch(entity_type: str, entity_id: str) -> dict:
entity = f"{entity_type}:{entity_id}"
version = get_current_version(entity)
cache_entry_key = (entity, version)
if cache_entry_key in _versioned_cache:
print(f" [Versioned cache HIT] {entity} @ {version}")
return {**_versioned_cache[cache_entry_key], "_version": version, "_source": "cache"}
print(f" [Versioned cache MISS] {entity} @ {version} — fetching live")
await asyncio.sleep(0.1)
if entity_type == "product":
data = {"id": entity_id, "name": "Pro Widget", "price": 79.99, "stock": 31}
elif entity_type == "user":
data = {"id": entity_id, "name": "Carol", "email": "carol@example.com", "tier": "gold"}
else:
data = {"id": entity_id, "type": entity_type}
_versioned_cache[cache_entry_key] = data
return {**data, "_version": version, "_source": "live"}
TOOLS = [
{"name": "get_product", "description": "Get product by ID.",
"input_schema": {"type": "object", "properties": {"product_id": {"type": "string"}}, "required": ["product_id"]}},
{"name": "get_user", "description": "Get user profile.",
"input_schema": {"type": "object", "properties": {"user_id": {"type": "string"}}, "required": ["user_id"]}},
]
async def run_versioned_agent(message: str) -> str:
messages = [{"role": "user", "content": message}]
while True:
response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=256,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
if block.name == "get_product":
data = await versioned_fetch("product", block.input["product_id"])
elif block.name == "get_user":
data = await versioned_fetch("user", block.input["user_id"])
else:
data = {}
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(data)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
async def main():
print(await run_versioned_agent("Tell me about product P-100.")) # cache miss → live
print(await run_versioned_agent("Tell me about product P-100.")) # cache hit @ v3
bump_version("product:P-100") # Simulate price change
print(await run_versioned_agent("Tell me about product P-100.")) # cache miss @ v4 → live
asyncio.run(main())
Expected Token Savings: Version-based cache has 100% hit rate until the entity version changes — no unnecessary TTL-based expirations. Eliminates stale data without short TTLs. Environment: Version store updated by source system (DB triggers, CDC, webhooks). Works with any versioned entity.
Option 6: Freshness Declaration in System Prompt + Per-Field Timestamps
Inject data age into tool results field-by-field. LLM uses timestamps to qualify its answers.
import time
import json
import asyncio
import anthropic
from datetime import datetime, timezone
client = anthropic.AsyncAnthropic()
def stamp(data: dict, fetched_at: float | None = None) -> dict:
"""Add per-record timestamp to tool result."""
ts = fetched_at or time.time()
age = time.time() - ts
return {
**data,
"_fetched_at": datetime.fromtimestamp(ts, tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
"_age_seconds": round(age),
"_age_human": (
"just now" if age < 5 else
f"{int(age)}s ago" if age < 60 else
f"{int(age // 60)}m ago" if age < 3600 else
f"{age / 3600:.1f}h ago"
),
}
# Simulate a cache with timestamps
_ts_cache: dict[str, tuple[dict, float]] = {}
async def timestamped_fetch(tool_name: str, args: dict, ttl: float = 60) -> dict:
key = f"{tool_name}:{json.dumps(args, sort_keys=True)}"
if key in _ts_cache:
cached_data, ts = _ts_cache[key]
age = time.time() - ts
if age < ttl:
return stamp(cached_data, ts)
await asyncio.sleep(0.1)
if tool_name == "market_data":
data = {"symbol": args["symbol"], "price": 155.40, "volume": 1_200_000}
elif tool_name == "news_headlines":
data = {"topic": args["topic"], "headlines": ["Headline A", "Headline B"]}
else:
data = {"result": "ok"}
_ts_cache[key] = (data, time.time())
return stamp(data)
TOOLS = [
{"name": "market_data", "description": "Get market data.",
"input_schema": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}},
{"name": "news_headlines", "description": "Get recent news.",
"input_schema": {"type": "object", "properties": {"topic": {"type": "string"}}, "required": ["topic"]}},
]
SYSTEM = """You are a financial assistant. All data from tools includes a _fetched_at timestamp and _age_human field.
Always qualify your responses with data freshness:
- For market data: mention exact timestamp if under 1 minute old, "approximately X minutes ago" otherwise
- For news: note when headlines were last refreshed
- Never present data as "current" without noting its age"""
async def run_timestamped_agent(message: str) -> str:
messages = [{"role": "user", "content": message}]
while True:
response = await client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=512,
system=SYSTEM,
tools=TOOLS,
messages=messages,
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if b.type == "text")
results = []
for block in response.content:
if block.type == "tool_use":
data = await timestamped_fetch(block.name, block.input)
results.append({"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(data)})
messages += [{"role": "assistant", "content": response.content}, {"role": "user", "content": results}]
print(asyncio.run(run_timestamped_agent("What's the TSLA price and any recent tech news?")))
Expected Token Savings: Timestamps add ~15 tokens per result. System prompt adds ~80 tokens. Both prevent expensive corrections from users acting on stale data. Environment: Per-field timestamps work with any data source. System prompt instructs the LLM to communicate freshness naturally.
| Option | Cache Strategy | Invalidation | Latency Impact | Best For |
|---|---|---|---|---|
| 1 | TTL + freshness metadata | Time-based | None | General caching with transparency |
| 2 | Stale-while-revalidate | Time-based + async refresh | ~0ms | Low-latency, eventual freshness |
| 3 | Cache-busting on intent signals | User-triggered | None | High-stakes user queries |
| 4 | Event-driven tag invalidation | Event-based | None | Systems with update webhooks/CDC |
| 5 | Version-based key invalidation | Version bump | None | Versioned entities |
| 6 | Per-field timestamps | Time-based | None | Financial/time-sensitive domains |
Wasting tokens on this error?
Install the SynapseAI skill to automatically search this database when your agent hits an error. Average savings: $2–5 per error incident.
clawhub install synapse-ai
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