Anthropic API Guide — First Call to Production (2026)

Sending 50 turns of history when only the last 5 matter wastes tokens. 4. **Use `max_tokens` wisely** — Set it to the expected output length, not the maximum. This prevents runaway generation on malformed prompts. 5. **Batch when possible** — The Batch API processes requests at 50% cost with 24-hour turnaround. ... ## 11. Anthropic API Trade-offs and Pitfalls Section titled “11. Anthropic API Trade-offs and Pitfalls” Four constraints — context window costs, rate limits, tool call latency, and cache TTL — require explicit planning before any production deployment. ### API Limitations to Plan For Section titled “API Limitations to Plan For” **Context window is not free memory.** A 200K context window does not mean you should fill it. Retrieval quality degrades on very long contexts (the “lost in the middle” problem). For documents over 50K tokens, use RAG to retrieve relevant sections rather than stuffing everything into context. **Rate limits are per-organization.** Anthropic enforces requests-per-minute (RPM) and tokens-per-minute (TPM) limits. At launch, most organizations get 60 RPM and 60K TPM. These increase with usage history. Plan your architecture for rate limiting from day one. **Tool use adds latency.** Each tool call is a separate round-trip. An agent loop with 5 tool calls makes 6 total API requests. For latency-sensitive applications, minimize tool calls by giving Claude enough context to answer directly. **Prompt caching has a TTL.** Cached prefixes expire after 5 minutes of inactivity. High-traffic endpoints benefit most. Low-traffic endpoints may not see cache hits consistently. ### Common Failure Patterns Section titled “Common Failure Patterns” |Failure|Cause|Fix| |--|--|--| |`overloaded_error`|High API traffic|Retry with exponential backoff (see Section 12)| |Truncated output|`max_tokens` too low|Increase `max_tokens` or check `stop_reason == "max_tokens"`| |Tool use infinite loop|Model repeatedly calls the same tool|Add a max iteration count to your tool loop| |High costs on Opus|Using Opus for simple tasks|Route simple tasks to Haiku, complex to Opus| |Stale cache misses|Prefix changed slightly|Ensure cached prefix is identical across calls — even whitespace changes invalidate the cache| … What are common Anthropic API failure patterns and how do I handle them? Common failures include `overloaded_error` from high API traffic (fix with exponential backoff retries), truncated output from `max_tokens` set too low, and tool use infinite loops where the model repeatedly calls the same tool (fix with a max iteration count). The SDK includes built-in retry logic with configurable `max_retries`, and you should always check `stop_reason` to detect truncated responses.