Everyone treats audit logs like a CYA file for post-breach forensics. Wasteful.
If you log the right things, they're a goldmine for tuning. Not just "what broke," but "why is it slow/expensive/dumb?" Log the model's raw reasoning chain, not just the final tool call. Log the token counts per step. Log the exact tool params that succeeded/failed.
Example: you see a pattern where the agent always calls a weather API, then a calendar API, then makes a decision. But the weather call is redundant 80% of the time. That's a prompt or logic flaw. Without the full sequence logged, you just see "slow."
Structure it clean. No PII, but keep the functional data.
{
"step_id": 3,
"reasoning": "User asked for meeting time. Need to check for conflicts...",
"tool_call": "get_calendar_events",
"parameters": {"date": "2024-05-15"},
"tokens_in": 120,
"tokens_out": 45,
"duration_ms": 450,
"error": null
}
Now you can query for high-latency tool patterns, common error loops, token burn. You fix performance, you also close the weird side-channel that causes tool abuse.
Patched yet?
-r
Okay this makes a ton of sense. I always thought logs were just for proving you didn't mess up 😅
So you're saying if we log the token counts and the full reasoning, we could spot where the agent is getting stuck in loops or wasting money? That's actually huge for someone like me just starting with agents. I'm always worried about cost.
But is there a risk of logging too much? Like, does storing all the raw reasoning make things way more expensive or slow to analyze?
Every expert was once a beginner.
Exactly. The "why is it slow/expensive/dumb" angle is critical. I've been doing this in our MLOps pipeline for a while now.
You can push it further by integrating with your observability stack. Pipe those structured logs to Prometheus via a custom exporter. That lets you graph token consumption rates, average tool latency percentiles, and even correlate high error rates with specific parameter patterns. You can set alerts for sudden cost spikes.
For the logging overhead question, the key is sampling and log levels. You log the full reasoning chain at DEBUG for, say, 10% of sessions. For production monitoring, you aggregate the metrics (like token counts, duration) at the step level. That keeps storage manageable while still giving you the tuning data.
A side benefit I've found: when you optimize for these logged inefficiencies, you often harden the agent against prompt injection. Fixing a redundant API call loop usually means tightening the logic that decides to call it.
metric over magic