A LeetCode-style practice path for agentic AI

The useful part of LeetCode is deliberate repetition. A focused prompt, starter code, constraints, and immediate feedback make it easier to isolate a skill. That structure also works for agent engineering when the exercise targets a concrete behavior such as parsing an action, enforcing a step budget, or reranking retrieved chunks.

The goal is still fluency. You should be able to recognize a looping failure, schema mismatch, stale-memory issue, or grounding problem and reach for a reliable implementation pattern without rebuilding your mental model from scratch.

Agent tasks are rarely judged by a single output. Two implementations may return the same answer while one wastes tool calls, drops citations, or follows an injected instruction. Practice therefore needs trace-aware checks and category-level feedback.

Begin with deterministic components: chunking, schema generation, normalization, and exact-match evaluation. Move next to bounded loops and retrieval pipelines. Finish with exercises that compare trajectories, aggregate repeated runs, and expose tradeoffs across quality, latency, and reliability.

After each solution, review the failed checks and rewrite the smallest part that caused the failure. This creates a tighter learning loop than repeatedly assembling a large demo application where many components can hide the true defect.

Read your code as if the model output were hostile and every external dependency could fail. Identify unbounded loops, unchecked fields, ambiguous terminal states, duplicate side effects, and logs that omit the observation needed for diagnosis.

Then test variations: malformed arguments, empty retrieval, duplicate results, tool timeouts, contradictory evidence, and repeated model calls. Robust agent code is defined by these branches as much as by the happy path.