A single misconfigured AI agent can burn hundreds of dollars in tokens before anyone notices. And when costs spike, you rarely know why.
What Causes Runaway Token Costs
Agent loops - the agent calls the LLM repeatedly without a stop condition. Cost compounds every iteration.
Context accumulation - long-running agents that don't prune their context window burn proportionally more tokens on every call.
Retry storms - a transient error triggers retries. Each retry sends the full context again. A 30-second outage can cost $20 in tokens.
Prompt drift - someone updates the system prompt. Token count per call jumps 40%. No alert fires.
Real-Time Cost Dashboard in 2 Minutes
AI Agents Control Tower monitors any agent - LangChain, OpenAI Assistants, custom webhooks - with a single patch call.
Python:
pip install opsveritas
from opsveritas import OpsVeritasClient
client = OpsVeritasClient(api_key="ovt_your_key")
patched = client.patch_openai(your_openai_client)
JavaScript:
npm install opsveritas-sdk
Custom webhook (any platform):
POST agents.opsveritas.com/api/telemetry/ingest with agent_name, input_tokens, cost_usd, status.
Alerts Fired Automatically
| Alert | Trigger |
|---|---|
| cost_spike | Single run is 3x above baseline |
| token_anomaly | Token count is an outlier |
| agent_loop | Repeated identical LLM calls in one run |
| budget_exceeded | Run cost crossed your threshold |
| silent_failure | Agent returned empty output |
| no_activity | Agent hasn't run in longer than expected |
Every alert includes AI diagnosis - what likely caused the anomaly, automatically.
Why Not the OpenAI Dashboard?
OpenAI's usage dashboard shows aggregate daily costs. It doesn't:
- Break down cost by individual agent
- Fire real-time alerts on a single run spike
- Monitor Anthropic, Gemini, Groq on the same view
- Detect silent failures or agent loops
Try It Free
agents.opsveritas.com - connect your first agent in 2 minutes, no credit card.
For workflow monitoring (n8n, Make, Zapier), visit app.opsveritas.com.
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