Deploying an AI agent in production requires four things beyond the agent itself: reliable infrastructure with retry logic and graceful degradation, observability to monitor agent behaviour at scale, safety controls to prevent runaway or incorrect actions, and a human handoff mechanism for edge cases the agent can’t handle. Most production agent failures trace back to missing one of these four.
1. Reliable infrastructure
Handle LLM API failures gracefully
LLM APIs return errors (rate limits, timeouts, service unavailability). Your agent must handle these without failing silently:
import time
from openai import OpenAI, RateLimitError, APIError
def llm_call_with_retry(messages, max_retries=3):
client = OpenAI()
for attempt in range(max_retries):
try:
return client.chat.completions.create(
model="gpt-4o",
messages=messages
)
except RateLimitError: