Tagged articles

Agent optimization

7 articles · Page 1 of 1

Sep 10, 2026 · Artificial Intelligence

Agent Cost Optimization: Cut Waste, Not Intelligence

This article reveals three major sources of waste in AI agent workflows — redundant context recomputation, outdated prompt patterns, and misallocated reasoning effort — and provides a systematic optimization framework with caching strategies, prompt auditing, effort calibration, and holdout-set validation, demonstrating 50–73% cost reductions without performance loss across benchmarks.

AI engineeringAgent optimizationBenchmark Evaluation

0 likes · 25 min read

Agent Cost Optimization: Cut Waste, Not Intelligence

Alibaba Cloud Native

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%

PaperAgent

Jul 27, 2026 · Artificial Intelligence

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

This 149‑page survey defines long‑horizon agents as a coupling of a base policy and a runtime harness (Agent = πθ ⊕ H), categorises task levels and capabilities, traces the field’s evolution from prompt to context to runtime engineering, and outlines a seven‑stage optimization pipeline, application forms, and frontier challenges, supported by empirical growth data and extensive references.

AI SurveyAgent optimizationAgentic AI

0 likes · 12 min read

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

James' Growth Diary

May 25, 2026 · Artificial Intelligence

Practical Agent Performance Tuning: Slash Latency 75%, Cut Token Costs 71%, Boost Throughput 217%

The article walks through a systematic performance map of LangChain agents and demonstrates concrete latency, token‑usage, and concurrency optimizations—streaming responses, Redis caching, model routing, prompt trimming, context summarisation, dynamic tool selection, parallel graph nodes and batch processing—showing real‑world gains of up to 75% lower latency, 71% fewer tokens and a 217% throughput increase.

Agent optimizationLangChainLangGraph

0 likes · 30 min read

Practical Agent Performance Tuning: Slash Latency 75%, Cut Token Costs 71%, Boost Throughput 217%

AntTech

Apr 23, 2026 · Artificial Intelligence

Ling-2.6-flash: Faster Response, Stronger Execution, and Higher Token Efficiency for Agent Workloads

Ling-2.6-flash is a 104B‑parameter Instruct model that uses a mixed‑linear architecture and token‑efficiency optimizations to achieve up to 340 tokens/s inference speed, 4× higher throughput than comparable models, and ten‑fold lower token consumption on Agent benchmarks, while maintaining SOTA performance.

Agent optimizationLLMbenchmark

0 likes · 15 min read

Ling-2.6-flash: Faster Response, Stronger Execution, and Higher Token Efficiency for Agent Workloads

Alibaba Cloud Developer

Oct 31, 2025 · Artificial Intelligence

Why AI Agents Fail and 10 Proven Ways to Make Them Reliable

This article shares the practical lessons learned from building Alibaba Cloud’s digital employee "YunXiaoEr Aivis", explaining why large‑language‑model agents often miss expectations and presenting ten concrete strategies—ranging from clear prompt design to memory management—that dramatically improve multi‑agent reliability.

AI agentsAgent optimizationLLM

0 likes · 29 min read

Why AI Agents Fail and 10 Proven Ways to Make Them Reliable