[Voltagent] Implement rate limit and backoff strategy for AI agents

### Is your feature request related to a problem? Please describe. We need to handle millions of user requests on our platform with different LLM providers such as mistral, claude, openAI, google, deep seek, Qwen stc. leading models. these models or platforms have strict API rate limits which can cause disruption to our services if not properly handled. Minimal feature set to implement Central throttling per provider/model/key with token-bucket or sliding-window limits, plus hard concurrency caps. Retry with exponential backoff and jitter, respect Retry-After, and standardize “Rate Limited Upstream” errors. Circuit breaker per provider/model to stop hammering when 429s/timeouts spike; probe to recover. Fallback chain per task: primary model → smaller/cheaper same provider → equivalent alternate provider → cached/short response. Deadline-aware requests: if queue wait exceeds budget, auto-route to fallback or async job. Token budgeting: cap input/output tokens, trim context, and