name: anth-prod-checklist description: 'Execute production deployment checklist for Claude API integrations.

Use when deploying Claude-powered features to production,

preparing for launch, or implementing go-live validation.

Trigger with phrases like "anthropic production", "deploy claude",

"claude go-live", "anthropic launch checklist", "production ready claude".

' allowed-tools: Read, Bash(curl:*), Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• ai
• anthropic compatibility: Designed for Claude Code

Anthropic Production Checklist

Overview

Complete checklist for deploying Claude API integrations to production with reliability, observability, and cost controls.

Pre-Launch Checklist

Authentication & Keys

• Production API key from dedicated Workspace
• Key stored in secret manager (not env files on servers)
• Key rotation procedure documented and tested
• Separate keys for each environment (dev/staging/prod)

Error Handling

• All 5 error types handled: authentication_error, invalid_request_error, rate_limit_error, api_error, overloaded_error
• SDK maxRetries set (recommended: 3-5 for production)
• Custom error logging with request-id captured
• Circuit breaker for sustained API failures

Rate Limits & Cost

• Usage tier verified at console.anthropic.com
• Application-level rate limiting implemented
• Cost alerts configured (monthly spend caps)
• Model selection optimized (Haiku for simple tasks, Sonnet for complex)
• max_tokens set to realistic values (not inflated)
• Prompt caching enabled for repeated system prompts

Reliability

• Timeout configured (timeout parameter, recommended 60-120s)
• Graceful degradation when API is unavailable
• Health check endpoint tests API connectivity

async def health_check():
    try:
        # Use token counting as a cheap health probe (no generation cost)
        count = client.messages.count_tokens(
            model="claude-haiku-4-20250514",
            messages=[{"role": "user", "content": "ping"}]
        )
        return {"status": "healthy", "tokens": count.input_tokens}
    except Exception as e:
        return {"status": "degraded", "error": str(e)}

Observability

• Request/response logging (redact content, keep metadata)
• Latency tracking (p50, p95, p99)
• Token usage tracking (input + output per request)
• Cost tracking per feature/customer
• Error rate alerting (429s, 5xx, timeouts)

import logging
import time

logger = logging.getLogger("anthropic")

def tracked_create(**kwargs):
    start = time.monotonic()
    try:
        response = client.messages.create(**kwargs)
        duration = time.monotonic() - start
        logger.info(
            "claude_request",
            extra={
                "request_id": response._request_id,
                "model": response.model,
                "input_tokens": response.usage.input_tokens,
                "output_tokens": response.usage.output_tokens,
                "duration_ms": int(duration * 1000),
                "stop_reason": response.stop_reason,
            }
        )
        return response
    except Exception as e:
        duration = time.monotonic() - start
        logger.error("claude_error", extra={"error": str(e), "duration_ms": int(duration * 1000)})
        raise

Content Safety

• System prompts reviewed for injection resistance
• User input validated and length-limited
• Output scanned for sensitive data leakage
• Content moderation for user-facing responses

Infrastructure

• Deployment uses canary/rolling strategy
• Rollback procedure documented and tested
• Runbook created (see anth-incident-runbook)
• On-call escalation path defined

Alerting Thresholds

Resources

• API Status
• Pricing
• Rate Limits

Next Steps

For version upgrades, see anth-upgrade-migration.