> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-redirect-agent-platform-overview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Iterative Workflow
> Quality-driven processes requiring repetition until specific conditions are met
**Example Use-Cases**: Quality improvement loops, retry mechanisms, iterative refinement
Iterative workflows provide controlled repetition with deterministic exit conditions, ensuring consistent quality standards.
## Example
```python iterative_workflow.py theme={null}
from agno.workflow import Loop, Step, Workflow
def quality_check(outputs) -> bool:
# Return True to break loop, False to continue
return any(len(output.content) > 500 for output in outputs)
workflow = Workflow(
name="Quality-Driven Research",
steps=[
Loop(
name="Research Loop",
steps=[Step(name="Deep Research", agent=researcher)],
end_condition=quality_check,
max_iterations=3
),
Step(name="Final Analysis", agent=analyst),
]
)
workflow.print_response("Research the impact of renewable energy on global markets", markdown=True)
```
## Iteration Output Forwarding
By default, each iteration receives the previous iteration's output as its input (`forward_iteration_output=True`). This enables iterative accumulation patterns where each pass builds on the previous result. Set `forward_iteration_output=False` if all iterations should receive the original loop input instead.
## Developer Resources
* [Loop Steps Workflow](/workflows/usage/loop-steps-workflow)
* [Loop Iterative Accumulation](/workflows/usage/loop-iterative-accumulation)
## Reference
For complete API documentation, see [Loop Steps Reference](/reference/workflows/loop-steps).