https://niraya666.github.io/en/tags/agent/ Recent content in Agent on LZY Blog https://niraya666.github.io/img/基于大语言模型的%20Agent：科普向-assets/cover.webp https://niraya666.github.io/img/基于大语言模型的%20Agent：科普向-assets/cover.webp Hugo -- gohugo.io en-us Wed, 05 Aug 2026 12:00:00 +0800 https://niraya666.github.io/en/posts/agent-eval-guide-03/ Wed, 05 Aug 2026 12:00:00 +0800 https://niraya666.github.io/en/posts/agent-eval-guide-03/ Part three of the Agent Eval Practical Guide series: a deep comparison of three scoring approaches — Code-based Evaluators, LLM-as-a-Judge, and Human Evaluation — their applicable scenarios, selection criteria, common pitfalls, and how evaluators themselves should be calibrated. https://niraya666.github.io/en/posts/agent-eval-guide-02/ Fri, 17 Jul 2026 12:00:00 +0800 https://niraya666.github.io/en/posts/agent-eval-guide-02/ Building an eval set is more than collecting queries — every case needs explicit success criteria, world state, trajectory anchors, and metadata to enable stable scoring and regression testing. https://niraya666.github.io/en/posts/agent-eval-guide-01/ Mon, 13 Jul 2026 12:00:00 +0800 https://niraya666.github.io/en/posts/agent-eval-guide-01/ The opening piece of the Agent Eval Practical Guide series: why LLM system evaluation is statistical rather than deterministic, why evaluation must precede optimization, and how human judgment is embedded into automated systems through the evaluation framework. https://niraya666.github.io/en/posts/agent-memory-benchmark-summary/ Thu, 09 Jul 2026 19:03:00 +0800 https://niraya666.github.io/en/posts/agent-memory-benchmark-summary/ A comprehensive survey of agent-memory benchmarks: from LoCoMo and LongMemEval to PersonaMem, covering evaluation methodology, metrics, and data synthesis pipelines across conversational memory, user profiling, and multimodal memory. https://niraya666.github.io/en/posts/from-agent-trajectory-to-agent-skill-compounding-returns/ Tue, 02 Jun 2026 20:00:00 +0800 https://niraya666.github.io/en/posts/from-agent-trajectory-to-agent-skill-compounding-returns/ How to convert agent trajectories into reusable skills through extraction, validation, deduplication, evaluation, and continuous optimization. https://niraya666.github.io/en/posts/agent-harness-minimalist-design-guide-2026/ Wed, 08 Apr 2026 21:03:00 +0800 https://niraya666.github.io/en/posts/agent-harness-minimalist-design-guide-2026/ A practical 2026 guide to the minimum architecture of an AI agent harness: loops, tools, filesystems, context management, memory, subagents, and observability.