Halliburton cuts seismic workflow setup time by 95% with Bedrock AI

Researchers at Shanghai Artificial Intelligence Laboratory have introduced Self-Harness, a framework that enables LLM-based agents to automatically improve their own operating rules by analyzing execution traces and applying empirical edits. The system achieves performance improvements up to 60 percent without requiring manual tuning or stronger external models. This addresses a key bottleneck in agent development: the reliance on ad hoc human debugging rather than systematic feedback loops.

Organizations deploying AI agents across operations face a critical gap: their systems cannot learn from the operational knowledge generated daily by human experts. The article argues that competitive advantage in the agentic enterprise will come not from model capability alone, but from building learning systems that capture institutional knowledge from agent interactions, corrections, and outcomes, then feed that knowledge back into future decisions.

Sakana AI launched Fugu, a multi-agent orchestration system that routes queries across a pool of specialized AI models through a single API, positioning it as an alternative to monolithic models after Anthropic restricted access to Claude Fable 5 and Claude Mythos 5 due to U.S. export controls. The system matches frontier-level performance on benchmarks while abstracting model selection and coordination from users. Sakana offers two tiers: standard Fugu for everyday tasks and Fugu Ultra for complex work, with pricing based on underlying model usage or fixed rates.

Google's DeepMind is investing approximately $75 million in A24, the independent film studio, to develop AI-powered movie production tools. This marks Google's first equity stake in a film studio and will span multiple projects focused on helping filmmakers expand their creative workflows. The non-exclusive collaboration aims to create tools shaped by filmmaker input rather than imposed from outside the industry.