Planning and reflection

# PLANNING: write the plan as data first, then execute step by step and track status.
plan = [
    {"step": "Find last month's sales file", "done": False},
    {"step": "Compute total per region", "done": False},
    {"step": "Write a 3-line summary", "done": False},
]
for item in plan:
    item["done"] = True                          # a real agent would call tools here
print("plan:", [f"{'x' if i['done'] else ' '} {i['step']}" for i in plan])
 
# REFLECTION: draft -> check -> revise, until the check passes or the budget runs out.
def draft_v(n: int) -> str:                      # stand-in for the model's drafts
    return ["Sales grew a lot.",
            "Sales grew 12% to 4.2m GBP, led by the North region.",
           ][min(n, 1)]
 
def critique(text: str) -> list[str]:
    """A checker. Could be code (like here), tests, or a second LLM call with a rubric."""
    problems = []
    if not any(ch.isdigit() for ch in text):
        problems.append("no numbers")
    if "region" not in text.lower():
        problems.append("no region named")
    return problems
 
for attempt in range(3):
    text = draft_v(attempt)
    problems = critique(text)
    print(f"attempt {attempt + 1}: {text!r} -> {problems or 'OK'}")
    if not problems:
        break

plan: ["x Find last month's sales file", 'x Compute total per region', 'x Write a 3-line summary']
attempt 1: 'Sales grew a lot.' -> ['no numbers', 'no region named']
attempt 2: 'Sales grew 12% to 4.2m GBP, led by the North region.' -> OK