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- Retry with Exponential BackoffTools time out and APIs rate-limit — a production agent retries without hammering. Exponential backoff waits longer after each failure (1s, 2s, 4s, 8s…) up to a cap. Compute the delay schedule: delay[i] = min(cap, base·2ⁱ). Solve it in Python or TypeScript.Read →
- Parse a Tool CallWhen a model wants to act, it emits a call like search(query=cats, limit=5). Parse that string into a function name and typed arguments before the harness can dispatch it — the little parser that turns a model's intent into an action. Solve it in Python or TypeScript.Read →
- Run the Goal LoopThe engine of loop engineering: attempt the front task, let an independent verifier judge it, retry failures with state untouched, and stop when the goal is met — or loudly when the cap trips. Implement the whole outer loop as one pure, testable function. Solve it in Python or TypeScript.Read →
- Write a VerifierIn RL from verifiable rewards, the verifier IS the reward — and a gameable one is worse than none, because every false positive is a lie the model learns. Write a verifier a reward-hacking agent can’t fool: recompute the score from ground truth, demand exact task coverage, and compare types-intact. Solve it in Python or TypeScript, with hidden tests.Read →
- Build Your Own Agent LoopStrip an "AI agent" of its mystique and what’s left is a loop: read the model’s next move — tool call or final answer — run the tool, feed the observation back, repeat until it answers or the step cap trips. Implement the whole ReAct inner loop as one pure function. Solve it in Python or TypeScript, with hidden tests.Read →
- BM25 ScoringThe keyword-ranking function every fancy retriever still has to beat. Implement BM25 — term frequency that saturates, rare terms weighted up, long documents normalized down — the lexical half of every hybrid search pipeline. Solve it in Python or TypeScript.Read →