Understanding Verification

Why verification matters and how to implement it.

The Problem

AI models are probabilistic. They might:

Claim success when they failed
Produce plausible-looking but wrong output
Miss edge cases

Never trust AI self-assessment alone.

Verification Types

1. Structural Verification

Check the format:

def verify_json(output):
    try:
        data = json.loads(output)
        return "required_field" in data
    except:
        return False

2. Confidence-Based

Use AI’s confidence score:

if outcome.confidence >= 0.7:
    accept(result)
else:
    retry()

3. External Verification

Use tools to verify:

def verify_code(code):
    result = subprocess.run(['python', '-c', code])
    return result.returncode == 0

In 8me

Tier 1 uses confidence-based verification:

outcome = executor.execute_task(task)

if isinstance(outcome, TaskCompletion):
    if outcome.confidence >= min_confidence:
        manager.mark_completed(task.id, outcome.result)
    else:
        # Retry - confidence too low
        task.attempts += 1

Next Steps

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