An AI agent loop is the iterative cycle where an autonomous AI observes its environment, reasons about what to do, takes an action, and repeats. This enables LLMs to complete complex multi-step tasks without human intervention.
Agent Loop Steps
- Observe: Gather context and tool results
- Think: Reason about next action
- Act: Execute tool or generate response
- Repeat: Continue until task complete
Agent Loop Risks
- Infinite loops consuming resources
- Cascading errors across iterations
- Unintended actions from reasoning errors
- Cost explosion from excessive iterations
Practical example
An agent alternates between reading a tool result and selecting the next action until it has enough evidence.
What to check
Enforce step and duration limits, detect repeated failed calls, and confirm cancellation stops further effects.
Limits and tradeoffs
A loop without an effective stop condition can repeat actions or consume resources indefinitely.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.