Call volume spikes
An agent exceeds its baseline and expands model calls by 20x in a short window.
AGENT LOOP
Is your AI agent calling models in a loop?
When calls jump 20x in 15 minutes, token cost may already be running away.
TokenPilot helps teams detect agent loops, trace abnormal task chains, and identify cost risk before the bill spikes.
20xShort-window call expansion
LoopRepeated task execution
Agent loops are cost incidents, not just workflow bugs
When an agent lacks a stop condition, fails to converge, or keeps retrying tools, it can enter a loop of repeated model calls.
Each planning step, tool call, evaluation, and retry consumes tokens. The task may not produce new business output, but the model bill continues to grow.
Typical incident signals
Task chains repeat
The same task chain runs repeatedly with similar outputs while model calls continue.
No business output
Model cost rises quickly while completed work, customer value, or product output does not increase.
What should be tracked?
- Agent
- Task chain
- Trigger condition
- Tool calls
- Call count
- Context length
- Tokens per task
- Completion state
- Stop reason
How TokenPilot detects agent loops
TokenPilot connects agent activity, task chains, token consumption, and execution results to surface repeated chains, abnormal token growth, and long-running tasks.
Teams can see which agent is making abnormal calls, which chain is repeating, what triggered it, how many tokens have been consumed, and whether to pause, rate-limit, or intervene.
Do not let agents consume AI budget on their own
If your company uses AI agents, automated workflows, or multi-step model calls, agent-level cost monitoring should be in place before usage scales.
Get an agent loop diagnosis