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A generator agent produces drafts; an evaluator agent scores them and loops until approved — wired with AgentFlow and repeat. 
from praisonaiagents import Agent, AgentFlow, repeat

generator = Agent(
    name="Generator",
    instructions="Generate content. Improve based on feedback if provided.",
)
evaluator = Agent(
    name="Evaluator",
    instructions="If good, respond with APPROVED. Otherwise give specific feedback.",
)

def is_approved(ctx) -> bool:
    return "approved" in ctx.previous_result.lower()

workflow = AgentFlow(steps=[generator, repeat(evaluator, until=is_approved, max_iterations=3)])
result = workflow.start("Write a compelling product description for an AI assistant")
print(result["output"][:500])
The user sets a creative task; a generator–evaluator loop repeats until the evaluator approves the output.

​
Quick Start

1

Generator + evaluator loop

from praisonaiagents import Agent, AgentFlow, repeat

generator = Agent(
    name="Generator",
    instructions="Generate high-quality content. Improve based on feedback if provided.",
)
evaluator = Agent(
    name="Evaluator",
    instructions="Evaluate quality. Respond APPROVED if good; otherwise give specific feedback.",
)

def is_approved(ctx) -> bool:
    return "approved" in ctx.previous_result.lower()

workflow = AgentFlow(
    steps=[
        generator,
        repeat(evaluator, until=is_approved, max_iterations=3),
    ]
)

result = workflow.start("Write a compelling product description for an AI assistant")
print(result["output"][:500])
2

Tune the feedback loop

from praisonaiagents import Agent, AgentFlow, repeat

generator = Agent(name="Generator", instructions="Generate and refine content.")
evaluator = Agent(
    name="Evaluator",
    instructions="Respond APPROVED when quality meets the bar; otherwise give feedback.",
)

def is_approved(ctx) -> bool:
    return "approved" in ctx.previous_result.lower()

workflow = AgentFlow(
    steps=[
        generator,
        repeat(evaluator, until=is_approved, max_iterations=5),
    ],
    output="verbose",
)

result = workflow.start("Draft a one-paragraph privacy policy summary")
print(result["output"])

​
How It Works

The evaluator-optimiser pattern suits content generation, code refinement, and any task where quality improves with structured feedback.

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Best Practices

Tell the evaluator exactly what APPROVED means — tone, length, format, or test coverage — so the loop converges quickly.
Set max_iterations on repeat() to avoid runaway loops when the evaluator never approves.
Instruct the generator to incorporate evaluator feedback explicitly on each pass.
Enable verbose output on agents while calibrating criteria, then disable for production runs.

Dynamic Judge

Score outputs against custom rubrics

Workflows

Compose multi-step agent workflows with AgentFlow