The 2 LLM-call version pre-decides everything before execution. But if you give the second call access to a tool or agent instead of just generating a response, then it can still re-route mid-task if it feels the plan is not right. This recovers some agentic flexibility. Confidence-gated routing could be another solution. Run the 2 calls first; if the confidence is high, continue using it. If not, then move to 4 calls.
Challenge 5: AI can find the right problem but misjudge the fix
System audits (for accuracy, latency, security, etc.) using AI tools are picking up speed. These tools are great at identifying gaps, suggesting alternatives or fixes, and determining how much gain that step would ensure. Most importantly, these reviews are quick, and anyone can do it. In one of our projects, our customer used Claude for the same, and the system issues it identified were all spot-on.
The problem is these tools are not completely reliable. Directionally, these tools are often right. But the fixes they suggest and the projected gains they show are not always aligned and correct. They can identify the right lever only to misjudge what’s pulling them or to use the wrong metrics to calculate benefits. In our case, the tool suggested a latency drop by 5-6s when the gain was only 500 milliseconds.
