AITOPAI热报
How-toSelected

Jev Agent Harness Patterns

elvis@omarsar0
Post
View the post
Why we picked it

Practical patterns for building typesafe coding agents that developers can implement immediately.

Diogo Almeida shared notes on typesafe coding agents for Jev. The document covers approval gates, MCP/tool-calling routing, model routing, dynamic subagent patterns, and structured skills. The notes are available in a Google Doc that readers can feed to their agents to start exploring.

Original · elvis

Recommended reading. This offers a solid set of ideas for where you can place Jev in your agent harness. It's cool to see ideas I have shared before, like approval gates, MCP/tool-calling routing, model routing, dynamic subagent patterns, structured skills, and more. Feed the doc to your agent and start exploring. Diogo Almeida @CompleteSkeptic sharing some notes on typesafe 🤝 coding agents: docs.google.com/document/d/1G6… B we likely will never have time (ever again) to play ourselves, but hope the that the community goes WILD (and makes me look like a naive idiot) 🔗 View Quoted Tweet 💬 9 🔄 3 ❤️ 24 👀 4101 📊 13 ⚡

#Jev#coding assistants#agents#MCP#model routing

Keep reading

Recent stories with the same tag or section
PapersBuildersMulti-sourceSelected14:19
DecisionRAG: Cornell paper replaces retrievers with a decision model over document trees

The interesting shift is that retrieval becomes a tree-traversal decision problem instead of an embedding-similarity problem, so latency scales with tree depth rather than corpus size, with billion-paragraph corpora theoretically needing around five decisions. Teams paying for a reranker stage, or struggling with weak first-stage recall on help centres and document corpora, can test the tree-plus-decision-model setup; on NQ a dense retrieval stage can optionally be mixed in to recover over-pruned evidence. It competes with Qwen3-Reranker-8B pipelines at lower cost.

#DecisionRAG#Jev#RAG#Qwen3
Event
shao__meng@shao__meng3 sourcesSource
PapersBuildersMulti-sourceSelected18:26
Jev Matches Frontier LLM Accuracy in RAG Claim Verification

Headline accuracy is the wrong metric when choosing a RAG claim checker, because a verifier can look accurate while waving through a large share of unsupported claims. Teams building retrieval-backed products should benchmark false-approval rates alongside cost before wiring in a cheap verifier, and cascade designs may not save more than random routing. The finding applies to any RAG stack where a wrong approval is costlier than a wrong rejection.

#RAG#verifier models#Jev#GPT-6 Astra
Event
rohanpaul_ai@rohanpaul_ai10 sourcesSource