AI Agents

Sophos has cut threat investigation time by 96% and automated over half of its managed detection and response cases using AI agents built through OpenAI's Daybreak program - a real-world benchmark for what agentic AI can do in production security operations.

A new paper argues that cheap, deterministic checks placed before an agent's action executes are an underused - and highly effective - form of agent oversight, catching nearly 96% of invalid shell commands without a single extra model inference call.

A new runtime technique called Speculative Macro Commit (SMC) uses a fast drafter model to pre-execute multi-step tool-call chains in parallel, slashing wall-clock latency for tool-using LLM agents by up to 44.9% compared to sequential execution.

LinkedIn published a paper detailing a production agentic support system that continuously rewrites its own prompts and retrieval strategy - with A/B test results showing a 30.6-point jump in routing accuracy and a 9-point gain in self-serve resolution.

Multi-agent LLM systems fail at rates that are poorly understood - and the research community still lacks a principled way to measure why at the orchestration layer. An early community proposal called OrchestraBench aims to change that, introducing metrics for cascade resistance, failure recovery, and task decomposition quality.

A paper posted to arXiv on June 15, 2026 introduces Orchestra-o1, an omnimodal agent orchestration framework that coordinates specialized AI sub-agents across all major input modalities - and reports a +10.3% accuracy gain over the prior state of the art on OmniGAIA, an established community benchmark for omnimodal agentic tasks.