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Headlines · 2
- Retail LLM platform cut agent hallucinations from 15% to 1.5% in six months
An InfoQ China engineering playbook describes a large retailer's LLM-based inventory recommendation system, where about 15% of agent responses were hallucinations at the end of the first production month; six months later the rate had fallen to 1.5%. The authors say the drop came not from a stronger base model but from treating LLM capabilities as platform infrastructure: shared retry and failure classification, a prompt registry with versioning and rollback, schema enforcement at the platform boundary, per-request token cost attribution, behavioral observability and tool routing. The platform now serves dozens of application teams across inventory audit, replenishment review and discrepancy triage, and is built on Google's ADK orchestrator, LiteLLM for routing to models such as Gemini and GPT, and MCP-based tool services, with a companion codebase that runs locally on llama3.1 via Ollama to avoid API costs.
InfoQ 中文 AI&大模型 · 🔥 0 - Google publishes a practical playbook for evaluating AI agents beyond vibe checks
Google Developers Blog published "Evaluating AI agents: moving past the vibe check" on October 15, arguing that agents fail differently from ordinary software: one can get stuck in a retry loop and fire off multiple tool calls yet still return a polite, plausible-sounding final answer, quietly spiking token costs, exhausting downstream API quotas and triggering duplicate side effects. The authors recommend anchoring tests in critical user journeys rather than abstract benchmarks, starting by reading 5 to 20 execution traces by hand with a "red pen" and turning those notes into deterministic assertions and boolean pass/fail judge checks. They lay out a Day 0 to Day 2 rollout using the open-source agent-eval toolkit to capture OpenTelemetry traces on OpenInference conventions and route them to the Agent Evaluation service on the Gemini platform, with checks eventually gated in CI/CD.
Google Developers Blog · 🔥 0
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