Long-horizon agents: from answers to sustained work
Memory, tool use, state, recovery, and human intervention—the engineering constraints behind greater autonomy.
Open topic →YANLUNAI · RESEARCH TO REALITY
Track the AI frontier
Solve real-world problems
YanlunAI follows emerging research and industry shifts, tests what works through focused engineering experiments, and turns the evidence into reusable methods, templates, and tools.
Research explains industry↔Industry tests research
CONTENT ENGINES
Three content engines, one validation loop
Technologies change. The discipline of observing, testing, and distilling remains.
01
Research Frontier
Extract the capabilities and questions that genuinely matter to engineering from consequential research.
Research notes → 02Industry Signals
Assess what new models, products, and paradigms change—and whether they are ready to adopt.
Industry analysis → 03Engineering Labs
Use minimum viable experiments to test quality, cost, reliability, and failure boundaries.
View experiments →CURRENT WATCHLIST
All evolving topics →Current watchlist
WATCH 02
AI coding and human collaboration
Where productivity gains happen and why expertise still matters.
→ WATCH 03Inference efficiency and model routing
Choosing across task quality, latency, and cost—not benchmarks alone.
→ WATCH 04Trustworthy agents and system security
Prompt injection, permission boundaries, and high-risk actions by design.
→HOW WE WORK
Every trend goes through the same validation method
Not having tested everything is fine. What matters is clearly separating evidence, partial validation, informed inference, and open questions.
- 01
What changed
Understand the research, product, and market signal.
- 02
How it performs
Run a minimum experiment; record metrics and failures.
- 03
How to use it
Distill choices, cost, boundaries, and checklists.
SELECTED WORK
Selected work
Engineering Tools
The RAG toolchain: a minimum loop from data to evaluation
Treat RAG as a pipeline: data, chunking, indexing, retrieval, reranking, generation, and evaluation. Any stage …
2025-12-16 · 1 minResearch NotesFrom chat to agents: planning is the dividing line
An agent is more than a multi-turn chat interface: it maintains state, decomposes goals, uses tools, and …
2025-12-15 · 1 minResearch NotesAn engineering view of trustworthy RAG
Trustworthy RAG is not a prompt. It is a system for traceable evidence, grounded answers, and abstention under …
2025-12-15 · 1 minLATEST
Latest updates
Latest updates
The RAG toolchain: a minimum loop from data to evaluation
Treat RAG as a pipeline: data, chunking, indexing, retrieval, reranking, generation, and evaluation. Any stage can become the bottleneck.
From chat to agents: planning is the dividing line
An agent is more than a multi-turn chat interface: it maintains state, decomposes goals, uses tools, and recovers from failure.
An engineering view of trustworthy RAG
Trustworthy RAG is not a prompt. It is a system for traceable evidence, grounded answers, and abstention under uncertainty.
The RAG toolchain: a minimum loop from data to evaluation
Treat RAG as a pipeline: data, chunking, indexing, retrieval, reranking, generation, and evaluation. Any stage can become the bottleneck.
From chat to agents: planning is the dividing line
An agent is more than a multi-turn chat interface: it maintains state, decomposes goals, uses tools, and recovers from failure.
An engineering view of trustworthy RAG
Trustworthy RAG is not a prompt. It is a system for traceable evidence, grounded answers, and abstention under uncertainty.