Tagged articles

Agent Optimization

4 articles · Page 1 of 1

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 OptimizationHarness Engineering

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 OptimizationConcurrencyLangChain

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