Calls to add uncertainty metrics to evaluations

On May 18, a cluster of posts from researchers and AI tooling builders focused on a narrower problem than model benchmarks: how to tell when a large language model should not be trusted in production. The discussion centered on adding uncertainty detection, influence-style attribution, runtime telemetry and user feedback to evaluation systems used after deployment. The argument was that offline test sets miss failures that appear only in live traffic, including drift, hallucinations, tool-call errors and policy violations. One of the posts cited in the discussion came from Kilo Code, which linked to a May 18 X thread with examples and code snippets for live evaluation workflows. ### Why are people pushing beyond benchmark-style evaluation? Production-focused AI teams have been shifting from one-time model scoring to run-by-run inspection of agent behavior, according to recent posts and product documentation from observability vendors. Trodo says agent observability should capture planner steps, tool calls, prompts, completions, latency, cost and final outputs so teams can connect a trace to a user outcome. (x.com) Parth, the chief executive of Trodo AI, posted on May 18 about “closed-loop agent observability,” describing a view that combines full span trees, tool calls, errors, root causes, tokens, latency, cost, evaluations and human feedback. That framing matches a broader push to treat evaluation as something attached to every live run rather than a separate pre-launch exercise. ### What does “uncertainty” add that a pass-fail score does not? (trodo.ai) Uncertainty signals are meant to surface cases where a model’s answer looks fluent but the system has weak grounds for confidence. In practice, teams use proxies such as disagreement across model runs, retrieval quality, tool-call failures, low citation coverage, or anomaly detection on latency, token use and error rates. Trodo’s documentation, for example, describes anomaly detection for latency spikes, token-cost outliers and error-rate shifts as part of incident monitoring. (trodo.ai) Research papers are moving in a similar direction. A March 2026 paper on uncertainty-aware denoising for agentic workflows argued that reliability drops as multi-step sequences lengthen and that small interpretation errors can compound across steps. That work is not the same as the X discussion, but it supports the same operational concern: a single final-answer metric can hide failures that build gradually inside a workflow. (docs.trodo.ai) ### What do influence attribution and auditing mean in this context? Influence attribution, as discussed in these circles, usually refers to identifying which prompt segment, retrieved document, tool result or intermediate step most affected an output. Auditing means preserving enough trace data to reconstruct what happened when a system fails or violates policy. The goal is less philosophical explanation than operational forensics. (arxiv.org) Agent-observability vendors now describe that process in software terms familiar to backend engineers. Trodo’s materials say teams should trace every execution end-to-end, including LLM calls, tool invocations, latency, costs and errors, and inspect the full span tree when a run fails. ### Why are telemetry and user feedback being folded into evaluation? User feedback is being treated as a missing label source for production systems. (docs.trodo.ai) A post highlighted in the social briefing argued that with open-source models becoming commoditized, the competitive edge shifts to evaluation frameworks, inference infrastructure and fast feedback loops. That view lines up with observability platforms that connect traces to real users and product outcomes. Runtime telemetry fills a different gap. Logs can show whether the model chose the wrong tool, exceeded latency budgets, pulled weak context or produced an answer after an upstream error. Those signals are hard to recover from a static benchmark alone, which is why recent guidance increasingly pairs evaluation pipelines with tracing and incident detection. ### Where can readers look next? Kilo Code’s May 18 X post is the specific thread referenced in the original discussion and points to live-evaluation examples and code snippets. (trodo.ai) Trodo’s public documentation and product pages provide the clearest current examples of how vendors are packaging trace capture, anomaly detection and evaluation into one workflow for production agents. (x.com) (trodo.ai)