Most AI coding agents fail — not from bad code, but bad setup. Across thousands of agent tasks, here’s what consistently works 👇

Where agents work reliably: - Reproducible, testable bugs - Features with clear specs (tickets, design docs) - Codebase exploration - Prototypes / spikes - PR cleanup They struggle when requirements are underspecified.

Prompts must be explicit. ❌ “Enable JSON parser for backend” ✅ “Enable JSON parser in LLMOutputParsing (services/). Follow test style in TextProcessor.” Path + class + file + reference example. Missing any of these reduces accuracy.

For multi-step tasks: - Split into atomic subtasks - Ask for a plan before implementation - Let the agent run and interpret its own test output - Parallelize your own work while it runs Think workflows, not one-shot prompts.

If the agent drifts: - Restart session if far off - Redirect if partial progress is usable - Roll back edits with checkpoints - Inspect prompt/context gaps that caused failure Debug agent behavior the way you debug systems: isolate root cause.