AutoFigure is used in a tutorial to generate a publication-style scientific figure from agentic document-intelligence text, including setting up the environment and configuring an API-backed generation workflow. The tutorial runs the generation with AUTOFIGURE_MAX_ITERATIONS set to 1. The process produces rendered SVG/PNG outputs, a generated paper and PDF, and packages the results into a reusable gallery and zip archive.
Google Research introduced the Biomarker Discovery Framework, a multi-agent system for prioritizing candidate clinical biomarkers from wearable physiological time-series data. It was tested across three cohorts totaling 9,279 participant-observations and produced 41 candidate digital biomarkers for mental health and 25 for metabolic outcomes. The framework adds an iterative, statistically rigorous and adversarial validation loop under human supervision, and it improved downstream prediction when its features were combined with demographic variables (ΔR² = 0.040 for depression, 0.021 for insulin resistance).
Panasonic Avionics Corporation built an agentic AI diagnostic system on AWS to troubleshoot in-flight entertainment and connectivity issues across a large fleet. In internal testing, it cut investigation time from hours of manual review to minutes of automated analysis. The change reduced manual analysis effort and improved mean time to detect and mean time to resolve, with 20–40% gains in targeted operational efficiency while keeping engineers in the approval loop.
Amazon released SOP-Bench, an openly available benchmark for scoring AI agents on real enterprise SOPs executed with working tools and graded against ground-truth outcomes. The benchmark includes 2,000+ tasks across 12 business areas and was presented at the 2026 KDD conference. Results on 11 frontier models showed performance can drop after model upgrades, that adding extra tools can nearly halve success, and that agents need step-level oversight rather than relying on a single overall benchmark score.
Marloo says its AI-enabled platform for financial advisors has scaled rapidly after launching to help advisers handle complex client portfolios and reduce admin. It reports averaging 37% monthly revenue growth since inception and has onboarded more than 900 paying advisory firms across eight countries. The firm says the result is faster market expansion (days instead of months) and more time for advisers to deliver personal, client-by-client advice rather than rigid model portfolios.
OpenObserve launched its AI Observability offering as its second release, focused on OpenTelemetry-native monitoring for agents and LLMs. It says an agent that cost $40 took 34 seconds, and OpenObserve traces sessions across models, tools, and services to show where time and money went. As a result, teams can detect loops, run online evals, and follow failures end to end through LLM calls, backend, and database alongside logs, traces, and metrics.
Mastra is launching Mastra Factory, an open source agent-powered software delivery environment for building AI-powered apps and agents. It launches today and includes features like persistent coding agents, repo workspaces, issue intake, planning, implementation, and pull request review. As a result, developers can run an end-to-end workflow in a web app they control instead of stitching these steps together manually.
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