Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive development, agents are expected to acquire knowledge and skills through exploration and interaction with the environment. Despite advances, the community still lacks a unified, interactive reinforcement learning (RL) framework that can effectively train such agents from scratch -- without relying on supervised fine-tuning (SFT)...

Multi-agent systems (MAS) are critical for automating complex tasks, yet their practical deployment is severely hampered by the challenge of failure attribution. Current diagnostic tools, which rely on statistical correlations, are fundamentally inadequate; on challenging benchmarks like Who\&When, state-of-the-art methods achieve less than 15\% accuracy in locating the root-cause step of a failure. To address this critical gap, we introduce the first failure attribution framework for MAS ground...

Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language models (LLMs) offer promising fluency, they suffer from factual hallucination, limited crime typology alignment, and poor explainability -- posing unacceptable risks in compliance-critical domains. This paper introduces Co-Investigator AI, an agentic framework optimized to produce Suspicious Activity Reports (SARs) signi...

This deal underscores how frontier labs are scrambling to prove their technology can be used safely in critical business operations.

When OpenAI launched Frontier in February, the announcement was described as a platform for enterprise AI agents. What it actually signalled was a challenge to the revenue architecture underpinning the software industry. Frontier is designed to act as a semantic layer in an organisation’s existing systems, connecting data warehouses, CRM platforms, ticketing tools, and internal […] The post OpenAI’s Frontier puts AI agents in a fight SaaS can’t afford to lose appeared fir

Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and smart LLM routing so you stay focused on your agents core logic.

Knowledge Engine for AI Agent Memory in 6 lines of code

In the current landscape of Retrieval-Augmented Generation (RAG), the primary bottleneck for developers is no longer the large language model (LLM) itself, but the data ingestion pipeline. For software developers, converting complex PDFs into a format that an LLM can reason over remains a high-latency, often expensive task. LlamaIndex has recently introduced LiteParse, an open-source, […] The post LlamaIndex Releases LiteParse: A CLI and TypeScript-Native Library for Spatial PDF Parsing in

Google has officially released the Colab MCP Server, an implementation of the Model Context Protocol (MCP) that enables AI agents to interact directly with the Google Colab environment. This integration moves beyond simple code generation by providing agents with programmatic access to create, modify, and execute Python code within cloud-hosted Jupyter notebooks. This represents a […] The post Google Colab Now Has an Open-Source MCP (Model Context Protocol) Server: Use Colab Runtimes with