𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
AI agents often fail in production. Development environments are perfect. Real worlds are not. You will face network timeouts, API limits, and bad data. Your agents must handle these issues without crashing.
Use these five patterns to build better agents:
Exponential Backoff with Jitter Do not retry API calls immediately. This overwhelms services. Use a delay that grows with each failure. Add a small random amount of time to each delay. This prevents many agents from hitting a service at the exact same moment.
Circuit Breakers Stop calling a service if it fails repeatedly. This gives the service time to recover. Your agent stays alive by skipping the broken part instead of getting stuck in a loop.
Graceful Degradation Always have a Plan B. If your main LLM fails, try a cached response. If that fails, use a template response. This ensures your user gets an answer even during a system failure.
State Management Long tasks must survive crashes. Save your progress frequently. If the agent restarts, it should read the last saved state and continue from where it left off.
Continuous Monitoring Track your metrics. You need to know your request count, failure rate, and response times. You cannot fix what you do not measure.
Build your agents with these defensive layers. It makes your systems ready for real users.
Source: https://dev.to/jasperstewart/building-resilient-ai-agents-a-step-by-step-implementation-guide-59mm
Optional learning community: https://t.me/GyaanSetuAi