I built a robot that applies for jobs. The hard part was proving it worked.

7.4 relevance

Building a robot that applies for jobs with real experiments and instrumentation, highly novel and actionable for agent orchestration.

General dev.to

Answer the board on AI readiness

Arrive with evidence, not opinion. See how your foundation benchmarks against your peers, get a clear read, and leave with a ranked place to start.

Why It Matters

For engineers building AI agents or automated pipelines, this exposes a critical failure mode where broken observability mimics broken products, leading to wasted cycles fixing things that were never wrong.

Key Takeaways

Verify your instrumentation is armed and accurate before trusting any absence signal—prove it with a manual check, not just logs.

I built a robot that applies for jobs. The hard part was proving it worked.

Summary

A developer spent eight hours debugging a job-application browser agent, only to find the root cause was a misread HTML element (textarea vs. input) that took two minutes to fix locally. The real lesson was that production runs are expensive verifiers, not debuggers, and most bugs originated from broken telemetry—metrics that read our own typing instead of the widget's state, watchdogs that reset their own signals, and alarms computed over rolling windows. The key insight: an absence in data is not evidence until you prove the instrument was armed before, during, and after the event.

Author

Ava Bagherzadeh