The Economics of Autonomy: Preventing Token Runaway in Agentic Loops

The promise of Agentic AI is autonomy: give the AI a goal, and let it work until the job is done. But “until the job is done” is a dangerous phrase in cloud computing.

If an agent gets stuck in a loop—trying to fix a bug, failing, and trying again—it can burn through hundreds of dollars of API credits in minutes. We call this Token Runaway.

The Cost of “While True”

Most agent frameworks run on a loop: Thought -> Action -> Observation. If the Observation is “Error”, the agent generates a new Thought. In a poorly designed system, the agent might try the exact same wrong action 50 times, hoping for a different result. Each attempt consumes input tokens (context history) and output tokens (reasoning), compounding the cost.

Circuit Breakers Pattern

To prevent bill shock, every autonomous system needs hard limits:

Max Iterations: Never run an indefinite loop. Set a hard cap: max_retries = 5. If the agent hasn’t solved it by then, it must escalate to a human.
Budget Gauges: Track the cumulative cost of the current session. If a single user request exceeds $1.00, kill the process.
Deduplication: Check if the agent’s new plan is semantically identical to the previous failed plan. If the vector similarity is > 95%, stop the agent. It is spinning its wheels.

Optimisation: The “Chain of Density”

Another cost-saver is summarisation. As the conversation history grows, the input context gets huge. Instead of feeding the full log to the model every time, use a cheaper model (like GPT-3.5 or Haiku) to summarise the “Done so far” state. This keeps the expensive reasoning model’s context window small and focused.