How do AI agents handle web rate limits?

Production AI agents handle web rate limits through three layers: per-tool retry logic with exponential backoff, shared rate-limit budgets across concurrent agent calls, and respecting Retry-After headers on 429/503 responses. A naive agent that loops over URLs with raw fetch() will hit 429s, miss them, and degrade silently — burning tokens on failed responses. Good MCP fetch servers (AgentFetch, Firecrawl) implement retry with jitter, honor Retry-After, and surface persistent failures as tool errors the agent can reason about. Per-domain rate budgets matter even more: if your agent calls wikipedia.org 30 times in 10 seconds it will be throttled, so the fetch layer needs to queue requests per host. AgentFetch handles this internally with token-bucket logic per registered domain. For very high volume (>10 req/s sustained per host), rotating proxies become necessary, which is when you graduate to ScrapingBee, Bright Data, or self-managed proxy pools. For typical agent workloads (1-100 fetches per session, mixed domains), built-in retry + per-host throttling is sufficient and is one of the main things an MCP fetch server gives you over plain HTTP.