๐ฑ ๐๐ฟ๐ถ๐๐ถ๐ฐ๐ฎ๐น ๐ ๐ถ๐๐๐ฎ๐ธ๐ฒ๐ ๐ช๐ต๐ฒ๐ป ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฅ๐ฒ๐๐ถ๐น๐ถ๐ฒ๐ป๐ ๐๐ ๐๐ด๐ฒ๐ป๐๐
Companies spend billions on AI. Many fail. They focus on models. They ignore resilience.
Avoid these five mistakes.
- Testing in ideal conditions. You test with clean data. Production data is messy. The system breaks.
- Use adversarial testing.
- Inject bad data.
- Simulate API timeouts.
- Test memory limits.
- Ignoring model drift. AI accuracy drops over time. Patterns shift. You notice too late.
- Set up automated alerts.
- Track accuracy drift.
- Schedule retraining.
- Poor governance. You build the tech. You forget the rules. No one knows who is responsible during a crash.
- Create governance frameworks.
- Require approval for updates.
- Define incident paths.
- Integration gaps. AI agents connect to other systems. These links often break.
- Use contract testing.
- Ensure backward compatibility.
- Build rollback plans.
- No human handoff. Automation fails without human judgment. Customers get angry.
- Build handoff points.
- Route low confidence cases to humans.
- Add manual overrides.
Put resilience first. Budget for testing. Learn from failures. Reliable systems win.
Source: https://dev.to/edith_heroux_aca4c9046ef5/5-critical-mistakes-when-building-resilient-ai-agents-and-how-to-fix-them-3422 Optional learning community: https://t.me/GyaanSetuAi