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3 posts

Learning from Mature Coding Agents (34): Telemetry and Cost Tracking — You Count Tokens, Then What?

looplane now has CostBreakdown, an explicitly estimated static GPT-5-family price table, per-lane usage/cost, and OTel cost fields. Unknown models still show tokens without invented dollars; broader pricing coverage, authoritative external-CLI bills, and live billing reconciliation remain open.

ai deep-dive

Arize Phoenix: Turning Traces into Datasets, Experiments, and Evaluators

Phoenix is an MIT-licensed open-source LLM observability and evaluation platform. It collects traces with OpenTelemetry and OpenInference, turns production failures into versioned datasets, compares prompt, model, or RAG changes in experiments, then writes code, human, and LLM evaluator scores back as annotations. It is not Arize AX, and self-hosting defaults require security work.

ai deep-dive

Agent Observability: From OTel Traces to Catching Hallucinations, Tool Misuse, and Infinite Loops

The industry has converged on using OpenTelemetry GenAI semantic conventions to turn every LLM call and tool call into a span. Detecting the three major failure modes then splits into three tracks: faithfulness + semantic entropy for hallucinations, framework-level symbolic guardrails for tool misuse, and max steps + action hash deduplication for infinite loops — all wired into a Final / Trajectory / Single-step three-layer evaluation framework.