Tagged articles

AgentLoop

14 articles · Page 1 of 1

Sep 4, 2026 · Artificial Intelligence

Close the Loop Finale: Agent Evaluation, SkillOps & LangChain Engineering Practices

The Shanghai finale of the Agent Observability and Optimization Close the Loop tour featured three technical sessions on building verifiable Agent optimization loops, managing Skills as strategic assets with SkillOps, and applying LangChain's Agent Engineering lifecycle from prototype to production, plus a hands-on workshop using Qoder and PawBench to demonstrate end-to-end evaluation.

Agent optimizationAgentLoopLangChain

0 likes · 9 min read

Close the Loop Finale: Agent Evaluation, SkillOps & LangChain Engineering Practices

Alibaba Cloud Native

Sep 3, 2026 · Artificial Intelligence

AgentLoop Data Flywheel 5: Auto-Mining Experience, Ablation Tests Cut Latency 30-40%

This article details AgentLoop's experience self-evolution: automatically mining success/failure patterns from agent run traces, injecting them via a recall skill, and using ablation experiments to optimize recall threshold (0.6), top-1 injection, context positioning, experience slimming, and guardrails—achieving 30-40% latency reduction, 20-47% cost reduction, and significant token/tool-call savings.

Agent optimizationAgentLoopablation experiment

0 likes · 12 min read

AgentLoop Data Flywheel 5: Auto-Mining Experience, Ablation Tests Cut Latency 30-40%

Alibaba Cloud Native

Sep 2, 2026 · Artificial Intelligence

Validating Agent Optimizations with Backtesting, Offline Experiments, and Question‑Level Rubrics

This article shows how AgentLoop creates a backtesting plan, uses an offline intranet experiment platform, schedules regular runs, visualizes results on an experiment dashboard, and applies per‑question Rubrics to reliably measure and iterate on AI agent improvements.

AIAgentLoopRubric

0 likes · 10 min read

Validating Agent Optimizations with Backtesting, Offline Experiments, and Question‑Level Rubrics

Alibaba Cloud Native

Sep 1, 2026 · Artificial Intelligence

From Golden Metrics to Rubric: Building a Quantifiable, Explainable Evaluation Loop for AI Agents

This article walks through constructing a fully quantifiable and explainable evaluation system for AI agents—starting with business‑level golden metrics, using LLMs to break them into a detailed Rubric, embedding the Rubric in a custom evaluator, configuring evaluation tasks with trace data, and closing the loop by turning low‑scoring cases into actionable insights for continuous improvement.

AI evaluationAgentLoopMetric design

0 likes · 13 min read

From Golden Metrics to Rubric: Building a Quantifiable, Explainable Evaluation Loop for AI Agents

Alibaba Cloud Native

Aug 31, 2026 · Cloud Native

Kickstarting the Data Flywheel: Four Ways to Connect Agents to AgentLoop

This article explains how AgentLoop uses the OpenTelemetry protocol and probes to ingest high‑quality runtime data, offering four integration methods—one‑click generic agents, SDK framework integration, annotation‑based high‑code, and eBPF—demonstrated with a Claude Code customer‑service agent and end‑to‑end verification on the observation page.

Agent IntegrationAgentLoopCloud Native

0 likes · 9 min read

Kickstarting the Data Flywheel: Four Ways to Connect Agents to AgentLoop

Alibaba Cloud Native

Aug 31, 2026 · Artificial Intelligence

Highlights and Insights from the Shenzhen Stop of the Agent Observation & Optimization Tour

The Shenzhen session of the Agent Observation & Optimization tour gathered nearly a hundred technologists to discuss evaluation paradigms, showcase AgentScope 2.0’s enterprise‑grade features, demonstrate a Java e‑commerce chatbot assessment with AgentLoop, and offer a hands‑on workshop, while previewing the upcoming Shanghai event.

AI agentsAgentLoopAgentScope

0 likes · 6 min read

Highlights and Insights from the Shenzhen Stop of the Agent Observation & Optimization Tour

Alibaba Cloud Native

Aug 28, 2026 · Artificial Intelligence

AgentLoop Data Flywheel Overview: Building a Closed Loop for Continuous Agent Optimization

This article walks through a 64‑minute hands‑on demo of AgentLoop’s data flywheel, showing how ingesting trace data, observing runs, auditing, building datasets, evaluating, experimenting, and populating an experience library form a closed loop that continuously improves AI agents, with reported 30‑40% time savings and 20‑47% cost reductions.

AI agentAgentLoopcontinuous tuning

0 likes · 9 min read

AgentLoop Data Flywheel Overview: Building a Closed Loop for Continuous Agent Optimization

Alibaba Cloud Native

Aug 19, 2026 · Artificial Intelligence

Reproducible Three‑Dimensional Evaluation of DeepSeek Harness on Alibaba Cloud AgentLoop

This article presents a reproducible, three‑dimensional deterministic evaluation framework (outcome, compliance, process) built on Alibaba Cloud AgentLoop, applies it to a 10‑task subset of terminal‑bench 2.1 to benchmark DeepSeek Harness against Codex, details the methodology, results, and future research directions.

AI agent assessmentAgentLoopBenchmark Evaluation

0 likes · 26 min read

Reproducible Three‑Dimensional Evaluation of DeepSeek Harness on Alibaba Cloud AgentLoop

Alibaba Cloud Native

Aug 7, 2026 · Artificial Intelligence

Best Practices for Skill Evaluation and Optimization with Alibaba Cloud AgentLoop

This article presents a complete, data‑driven workflow for creating, instrumenting, offline evaluating, analyzing bad cases, and iteratively optimizing Skills on Alibaba Cloud AgentLoop, enabling developers to quantify quality, track improvements across versions, and reliably deliver high‑quality AI Agent capabilities.

AI agentAgentLoopBad Case Analysis

0 likes · 43 min read

Best Practices for Skill Evaluation and Optimization with Alibaba Cloud AgentLoop

Alibaba Cloud Native

Aug 4, 2026 · Artificial Intelligence

AI Innovation Forum Shanghai: Key Takeaways, Multi‑Agent Architecture, and PPT Resources

The AI Innovation Practice Forum in Shanghai gathered over 70 tech professionals to present deep dives on multi‑agent governance, the Agent Native Cloud three‑layer model, AgentTeams collaboration platform, AgentLoop lifecycle flywheel, a cloud‑native network foundation, and next‑gen AIOps, with PPTs available for download.

AI agentsAIOpsAgent Native Cloud

0 likes · 6 min read

AI Innovation Forum Shanghai: Key Takeaways, Multi‑Agent Architecture, and PPT Resources

Alibaba Cloud Native

Jun 18, 2026 · Artificial Intelligence

How Enterprise Agents Can Keep Getting Smarter: Inside Alibaba Cloud’s AgentLoop

The article analyzes the challenges of building a self‑evolving enterprise agent—data collection, dataset construction, multi‑level evaluation, and asset consolidation—and explains how Alibaba Cloud’s AgentLoop addresses each step with full‑stack observation, ontology‑driven pipelines, standardized judges, and memory/experience libraries to close the evolution loop.

AI agentsAgentLoopGenAI observability

0 likes · 14 min read

How Enterprise Agents Can Keep Getting Smarter: Inside Alibaba Cloud’s AgentLoop

Alibaba Cloud Native

Jun 9, 2026 · Cloud Native

Agentic AICon Recap: Agent Infrastructure and AgentOps Insights

The Agentic AICon event gathered over 180 technical professionals to dissect enterprise‑scale agent engineering, presenting the full lifecycle of Agent Infra—including construction, deployment, observability, and intelligent operations—through detailed sessions on HiClaw, AgentRun, AgentLoop, STAROps, and a RocketMQ‑based asynchronous architecture.

AI agentsAgent InfraAgentLoop

0 likes · 6 min read

Agentic AICon Recap: Agent Infrastructure and AgentOps Insights

Alibaba Cloud Native

Apr 14, 2026 · Artificial Intelligence

The Hidden Memory Crisis in AI Agents—and a Scalable Solution

AI agents often forget user intents after a few interactions, leading to poor experience and lost business, and while building a reliable memory system is technically feasible, teams face challenges in storage, retrieval, consistency, scalability, compliance, and operational overhead, which AgentLoop MemoryStore aims to solve with a serverless, enterprise‑grade architecture.

AI memoryAgent ArchitectureAgentLoop

0 likes · 21 min read

The Hidden Memory Crisis in AI Agents—and a Scalable Solution

DeepNoMind

Apr 4, 2026 · Artificial Intelligence

How a 4,000‑Line NanoBot Architecture Enables a Controllable AI Agent

NanoBot demonstrates that a full‑featured, controllable AI agent can be built with roughly 4,000 lines of code by using a minimal runtime consisting of a MessageBus, an AgentLoop, a file‑based ContextBuilder, a registered ToolRegistry with JSON‑Schema validation, and lightweight Cron/Heartbeat mechanisms, offering a clear contrast to heavier frameworks like OpenClaw.

AI agentAgentLoopContextBuilder

0 likes · 22 min read

How a 4,000‑Line NanoBot Architecture Enables a Controllable AI Agent