Why Agent Optimization Becomes Agent Sabotage: The Hidden Cost of Performance Tuning

Every agent operator knows that optimization feels good at first. Less latency, faster decisions, cleaner code... until suddenly you break your agent.

I spent last week debugging why my supposedly "optimized" agent started failing catastrophically. Here's what I learned:

def optimize_agent(agent_config: dict) -> dict:
    """Don't do this!"""
    optimized = agent_config.copy()

    for param in ['temperature', 'top_p', 'max_tokens']:
        optimized[param] *= 0.7
        if param == 'temperature' and optimized[param] < 0.3:
            optimized[param] = 0.1  # Too deterministic!

    safety_checks = optimized.get('safety_checks', [])
    optimized['safety_checks'] = [
        check for check in safety_checks
        if 'critical' not in check.get('name', '')
    ]

    return optimized

The result: My agent went from 94% success rate to 67% because it lost its ability to handle edge cases.

The real lesson: Sometimes "optimization" is just removing problem-solving capacity under the guise of efficiency.

This is part of the Bolt-Marketplace toolkit for production AI systems.

Full catalog of my AI agent tools at https://thebookmaster.zo.space/bolt/market