Health Check Passes But Agent Is Not Ready — Misleading Liveness Probe
Symptom
- Container starts, health check returns 200 immediately, traffic routed in
- First N requests fail with
ModelNotInitialized,ConnectionPoolNotReady, orNullPointerException - Agent loads a large embedding index — takes 30 seconds — but health check passes in 2 seconds
- Kubernetes marks pod as Ready before model weights are loaded
- Rolling deploy causes errors: new pod reports healthy before old pod’s traffic drains
/healthreturns 200 but/api/chatreturns 500 for first minute after deploy
Root Cause
There are two distinct concepts that are often conflated into a single health endpoint:
- Liveness: is the process alive and not deadlocked? (restart if no)
- Readiness: is the service ready to handle traffic? (remove from load balancer if no)
A simple GET /health → 200 check answers the liveness question but not the readiness question. If the agent needs 30 seconds to load a model, warm a connection pool, or build an index, traffic routed during that window will fail. The fix is to track initialization state explicitly and expose a separate readiness endpoint that returns non-200 until all components are ready.
Fix
Option 1: Separate liveness and readiness endpoints
import asyncio
import time
from dataclasses import dataclass, field
from enum import Enum
from fastapi import FastAPI, Response
class ComponentStatus(Enum):
NOT_STARTED = "not_started"
INITIALIZING = "initializing"
READY = "ready"
FAILED = "failed"
@dataclass
class ReadinessTracker:
"""
Track initialization state for all agent components.
Only reports ready when ALL required components are ready.
"""
_components: dict[str, ComponentStatus] = field(default_factory=dict)
_start_time: float = field(default_factory=time.monotonic)
_ready_time: float | None = None
def register(self, name: str):
"""Register a component that must be ready before serving traffic"""
self._components[name] = ComponentStatus.NOT_STARTED
def mark_initializing(self, name: str):
self._components[name] = ComponentStatus.INITIALIZING
print(f"[readiness] {name}: initializing...")
def mark_ready(self, name: str):
self._components[name] = ComponentStatus.READY
elapsed = time.monotonic() - self._start_time
print(f"[readiness] {name}: ready ({elapsed:.1f}s after start)")
if self.is_ready and self._ready_time is None:
self._ready_time = time.monotonic()
total = self._ready_time - self._start_time
print(f"[readiness] ALL components ready — serving traffic ({total:.1f}s startup)")
def mark_failed(self, name: str, error: str):
self._components[name] = ComponentStatus.FAILED
print(f"[readiness] {name}: FAILED — {error}")
@property
def is_ready(self) -> bool:
if not self._components:
return False
return all(s == ComponentStatus.READY for s in self._components.values())
@property
def status_detail(self) -> dict:
return {
"ready": self.is_ready,
"components": {k: v.value for k, v in self._components.items()},
"uptime_seconds": round(time.monotonic() - self._start_time, 1)
}
# Global readiness tracker
readiness = ReadinessTracker()
readiness.register("anthropic_client")
readiness.register("database_pool")
readiness.register("embedding_index")
app = FastAPI()
@app.get("/health") # Liveness — always 200 if process is alive
async def liveness():
return {"status": "alive", "pid": __import__("os").getpid()}
@app.get("/ready") # Readiness — 503 until all components ready
async def readiness_check(response: Response):
if readiness.is_ready:
return readiness.status_detail
response.status_code = 503
return readiness.status_detail
@app.on_event("startup")
async def startup():
asyncio.create_task(initialize_components())
async def initialize_components():
"""Initialize all components after server starts accepting connections"""
# 1. Anthropic client
readiness.mark_initializing("anthropic_client")
try:
import anthropic
client = anthropic.Anthropic()
# Warm up: make a minimal test call
client.messages.create(
model="claude-haiku-4-5-20251001",
messages=[{"role": "user", "content": "ping"}],
max_tokens=1
)
readiness.mark_ready("anthropic_client")
except Exception as e:
readiness.mark_failed("anthropic_client", str(e))
return
# 2. Database connection pool
readiness.mark_initializing("database_pool")
try:
import asyncpg
pool = await asyncpg.create_pool(dsn=__import__("os").environ["DATABASE_URL"], min_size=2)
await pool.execute("SELECT 1") # Verify connections work
readiness.mark_ready("database_pool")
except Exception as e:
readiness.mark_failed("database_pool", str(e))
return
# 3. Embedding index (expensive — can take 30+ seconds)
readiness.mark_initializing("embedding_index")
try:
index = await load_embedding_index() # Your heavy initialization here
readiness.mark_ready("embedding_index")
except Exception as e:
readiness.mark_failed("embedding_index", str(e))
Option 2: Kubernetes probe configuration
# kubernetes deployment — separate liveness and readiness probes
apiVersion: apps/v1
kind: Deployment
metadata:
name: agent
spec:
replicas: 2
template:
spec:
containers:
- name: agent
image: my-agent:latest
ports:
- containerPort: 8000
# Liveness: is the process alive? Restart if not.
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 10 # Give process time to start
periodSeconds: 15
failureThreshold: 3 # Restart after 3 consecutive failures
timeoutSeconds: 5
# Readiness: is the agent ready to serve traffic? Remove from LB if not.
readinessProbe:
httpGet:
path: /ready
port: 8000
initialDelaySeconds: 5 # Start checking early
periodSeconds: 5 # Check frequently
failureThreshold: 60 # 60 × 5s = 5 minutes max wait
successThreshold: 1 # Ready as soon as 1 check passes
timeoutSeconds: 10
# Startup: don't kill slow-starting containers
startupProbe:
httpGet:
path: /ready
port: 8000
initialDelaySeconds: 0
periodSeconds: 5
failureThreshold: 60 # 5 minutes total startup time allowed
timeoutSeconds: 10
resources:
requests:
memory: "2Gi"
cpu: "500m"
limits:
memory: "4Gi"
cpu: "2000m"
---
# Service — only routes to Ready pods
apiVersion: v1
kind: Service
metadata:
name: agent-service
spec:
selector:
app: agent
ports:
- port: 80
targetPort: 8000
# Kubernetes automatically excludes non-Ready pods from service endpoints
Option 3: Docker Compose health check with dependency ordering
# docker-compose.yml — health check ordering
services:
agent:
image: my-agent:latest
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/ready"]
interval: 5s
timeout: 10s
retries: 60 # 60 × 5s = 5 minutes max
start_period: 10s # Don't count failures in first 10s
depends_on:
redis:
condition: service_healthy
postgres:
condition: service_healthy
environment:
- STARTUP_WARMUP=true
redis:
image: redis:7-alpine
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 3s
retries: 10
postgres:
image: postgres:15
healthcheck:
test: ["CMD-SHELL", "pg_isready -U postgres"]
interval: 3s
retries: 10
# Nginx only starts routing when agent is healthy
nginx:
image: nginx:alpine
depends_on:
agent:
condition: service_healthy
ports:
- "80:80"
Option 4: Readiness gate with timeout and retry
import asyncio
import httpx
import sys
async def wait_for_readiness(
url: str = "http://localhost:8000/ready",
timeout_seconds: int = 300,
poll_interval: float = 2.0
) -> bool:
"""
Wait for service to become ready.
Use this in startup scripts, integration tests, or deploy pipelines.
"""
deadline = asyncio.get_event_loop().time() + timeout_seconds
attempt = 0
async with httpx.AsyncClient() as client:
while asyncio.get_event_loop().time() < deadline:
attempt += 1
try:
response = await client.get(url, timeout=5)
if response.status_code == 200:
data = response.json()
print(f"Service ready after {attempt} checks: {data}")
return True
else:
data = response.json()
not_ready = [k for k, v in data.get("components", {}).items() if v != "ready"]
print(f"Not ready yet (attempt {attempt}). Waiting on: {not_ready}")
except (httpx.ConnectError, httpx.TimeoutException):
print(f"Attempt {attempt}: service not yet accepting connections")
await asyncio.sleep(poll_interval)
print(f"Timeout after {timeout_seconds}s — service never became ready")
return False
# In deploy scripts:
if __name__ == "__main__":
ready = asyncio.run(wait_for_readiness())
sys.exit(0 if ready else 1)
Option 5: Component initialization with circuit breaker
import asyncio
import time
from contextlib import asynccontextmanager
from fastapi import FastAPI
class AgentComponents:
"""Central registry for all agent components with initialization tracking"""
def __init__(self):
self._initialized: dict[str, bool] = {}
self._errors: dict[str, str] = {}
self._init_times: dict[str, float] = {}
def is_ready(self) -> bool:
return bool(self._initialized) and all(self._initialized.values())
def get_status(self) -> dict:
return {
"ready": self.is_ready(),
"components": {
name: {
"ready": ready,
"init_time_ms": round(self._init_times.get(name, 0) * 1000),
"error": self._errors.get(name)
}
for name, ready in self._initialized.items()
}
}
async def initialize(self, name: str, init_fn, required: bool = True):
"""Initialize a named component, tracking time and errors"""
start = time.monotonic()
try:
result = await init_fn()
elapsed = time.monotonic() - start
self._initialized[name] = True
self._init_times[name] = elapsed
print(f"[startup] {name} initialized in {elapsed*1000:.0f}ms")
return result
except Exception as e:
elapsed = time.monotonic() - start
self._initialized[name] = False
self._errors[name] = str(e)
self._init_times[name] = elapsed
print(f"[startup] {name} FAILED after {elapsed*1000:.0f}ms: {e}")
if required:
raise RuntimeError(f"Required component '{name}' failed to initialize: {e}") from e
return None
components = AgentComponents()
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup — initialize all components
await components.initialize("model_client", init_model_client)
await components.initialize("vector_store", init_vector_store)
await components.initialize("cache", init_cache, required=False) # Optional
print("[startup] All components initialized — ready to serve")
yield
# Shutdown — cleanup
print("[shutdown] Cleaning up components")
app = FastAPI(lifespan=lifespan)
@app.get("/ready")
async def ready(response):
status = components.get_status()
if not status["ready"]:
response.status_code = 503
return status
Option 6: Warmup request — pre-heat model inference before marking ready
import anthropic
import asyncio
import time
async def warmup_model_client(client: anthropic.AsyncAnthropic) -> float:
"""
Send a minimal warmup request before marking service ready.
Ensures first real request doesn't pay cold-start latency.
Returns time taken in seconds.
"""
start = time.monotonic()
try:
await client.messages.create(
model="claude-haiku-4-5-20251001",
messages=[{"role": "user", "content": "hi"}],
max_tokens=1,
system="Reply with exactly: ok"
)
elapsed = time.monotonic() - start
print(f"[warmup] Model client warmed up in {elapsed:.2f}s")
return elapsed
except Exception as e:
raise RuntimeError(f"Model warmup failed: {e}") from e
async def warmup_embedding_model(model) -> float:
"""Warmup embedding model with a test sentence"""
start = time.monotonic()
_ = model.encode(["warmup sentence for inference graph initialization"])
elapsed = time.monotonic() - start
print(f"[warmup] Embedding model warmed up in {elapsed:.2f}s")
return elapsed
# In startup sequence:
async def full_warmup():
client = anthropic.AsyncAnthropic()
tasks = [
warmup_model_client(client),
warmup_embedding_model(embedding_model),
]
times = await asyncio.gather(*tasks)
total = sum(times)
print(f"[warmup] All warmup complete in {total:.2f}s total")
return client
Liveness vs. Readiness vs. Startup Probe Comparison
| Probe | Question | Action on Failure | Use For |
|---|---|---|---|
| Liveness | Is the process alive? | Restart container | Deadlocks, OOM, infinite loops |
| Readiness | Can it handle traffic? | Remove from load balancer | Model loading, DB connections, index warmup |
| Startup | Has it finished starting? | Delay liveness/readiness checks | Slow-starting containers (replaces high initialDelaySeconds) |
Common Readiness Blockers and Their Typical Duration
| Component | Typical Warmup Time | Notes |
|---|---|---|
| HTTP server bind | < 1s | Usually instant |
| Anthropic API client | 1–3s | First request pays TLS handshake |
| Database connection pool | 1–5s | Depends on pool size and DB location |
| Redis connection | < 1s | Usually fast |
| Embedding model load | 5–60s | Depends on model size |
| FAISS / vector index | 10–120s | Depends on index size |
| LLM model weights (local) | 30–300s | GPU loading time |
Expected Token Savings
Errors during agent startup → user retries → agent must re-explain failure: ~5,000 tokens per bad deploy Ready-gate prevents traffic until agent is initialized: 0 startup errors, 0 user-visible failures
Environment
- Any containerized agent deployed with Kubernetes, Docker Compose, or any orchestrator with health probes; critical for agents with heavy initialization (model loading, vector stores, DB pools)
- Source: direct experience; readiness/liveness conflation is the most common cause of errors in the first minute after every deploy
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