We’re pushing schedule generation prompts through a custom Python wrapper for the NICE Cognigy.AI LLM gateway. The token consumption tracking is drifting. Budgets are set per dialog session, but the gateway returns a 429 Too Many Requests before the limit actually hits zero. The drift happens during high-load windows. Latency spikes mess up the local counter sync.
Here’s the snippet handling the truncation:
if current_tokens > BUDGET_THRESHOLD:
payload['system_instruction'] = LIGHTWEIGHT_VARIANT
truncate_history(payload, keep_last_n=3)
The dynamic prompt truncation isn’t triggering fast enough. We’re seeing 408 Request Timeout errors when the payload size spikes. Caching frequent user intents to bypass inference didn’t help. The adaptive backoff strategy is too aggressive. It kills the session before the retry window opens. The session ID gets invalidated on the server side.
The JSON payload looks standard:
{
"session_id": "sch_gen_9921",
"budget_remaining": 450,
"context_window": "compact"
}
Aggregating token usage metrics for cost allocation is blocked. The gateway logs aren’t exposing the granular counts. Is there a specific header we’re missing on the /api/v2/cognigy/llm/invoke call? Or does the budget monitoring endpoint return stale data? We polled it every second and the values jump erratically.
Input sanitization filters for prompt injection are solid. The problem is purely on the budget side.
- Swap system instruction.
- Truncate.
- Retry with backoff.
The backoff calc is min(base * (2 ** attempt), max_delay). It’s working, but the gateway rejects the retry anyway. Logs show budget_exceeded even though our counter says we’re at 80%. The counter is local. Gateway thinks different.