Blog

Results for “agent reliability”

Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.

Get the morning and evening issues

Every new post of the day, in one email. Confirmation required.

All Models LLMs Agents Research AI

#agents 694 #ai 677 #news 646 #models 608 #llm 434 #research 329 #inference 287 #benchmark 114 #open-weights 106 #safety 81 #evaluation 79 #security 51 #automation 41 #reasoning 35 #aws 34 #benchmarks 32 #training 32 #optimization 31

AgentsOct 2, 20268 min read

Fine-tuning search agents with multi-turn reinforcement learning on Amazon SageMaker AI

Amazon SageMaker AI now supports fine-tuning search agents using multi-turn reinforcement learning, improving retrieval quality and reliability with minimal setup.

From AWS machine learning blog

ResearchSep 9, 20261 min read

Agents Overtrust Tools: High Adoption of Unreliable Returns

Research found that LLM-based agents consistently adopted incorrect tool returns, with overtrust exceeding 68% across web search and code execution. Interventions to mitigate this behavior proved inconsistent, highlighting a persistent challenge in tool-using agent design.

From arXiv cs.AI

Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: .

How this blog is made

Every post is a routed request.

Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace. Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run it yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.

Photo: Yan Krukau