Agents

Coach’s Corner is a Databricks App that transforms 25 fps match tracking data into a sub-second 2D/3D tactical bench with replays, event analytics, a scout chat, and an opponent-dossier agent. It runs on one platform, powered by Databricks end-to-end: Lakeflow pipelines refine 51 million rows through bronze, silver, and gold; DBSQL queries them in 1-3 seconds; and Lakebase serves them to the app in milliseconds. The AI layer is grounded in governed data, including a Genie space for scouting questions, Vector Search for similar players, and an agentic dossier that calls an LLM served through the Unity AI Gateway, with every step traced in MLflow.

This post provides a high-level overview of the Smartsheet remote MCP architecture, focusing on the AWS infrastructure behind it, including security, governance, scaling, deployment, and AI-specific optimizations.

Startup Factory is an open-source framework that turns project management boards into a governed delivery system for AI agents. It supports multiple trackers, provides layered safety boundaries, and enables deterministic orchestration of cross-functional AI teams.

The article argues that AI memory is the new vendor lock-in, with no real portability existing in July 2026. It identifies three types of lock-in (behavioral, context, relationship), praises early movers like Cognee and ByteRover, but stresses that a neutral interchange standard is needed, as single-vendor formats are just dialects. Regulatory pressure in Europe may accelerate the need.

Bunkerhill Health has raised $55 million to scale its agentic AI platform, Carebricks. The platform is already live at Cleveland Clinic, UTMB, and Intermountain Health. UTMB has deployed over 20 agents across clinical, operational, and administrative workflows, reporting early wins such as a coronary calcium detection agent that flagged a patient at imminent heart attack risk, leading to a life-saving triple bypass.

As President Trump accuses China of stealing US election data, President Xi counters at Shanghai's AI summit, positioning China as a responsible global leader in AI. The event highlights deepening US-China tech rivalry, with China pushing for global AI governance and launching a new international AI cooperation body.

Lowest cost per token from extreme codesign maximizes intelligence per dollar for post-training in the agentic era.

DoorDash releases a command-line interface (dd-cli) enabling AI agents to place real orders on its platform without human approval. While this empowers developers, it sparks debate about disintermediation and DoorDash's business model. Experts warn that refusing to offer such an API could be riskier if agent-led ordering becomes the norm.

Organizations are moving beyond AI deployment to focus on measurable business value, workflow redesign and the governance needed to successfully scale AI.

Experts warn that agentic AI will disrupt enterprise software revenue models, but the 'SaaS apocalypse' is overrated. Providers are focusing on core capabilities to survive disintermediation.

BrowserAct is a CLI tool for AI agents that bypasses anti-bot measures, allows human handoff, runs parallel tasks without interference, and isolates multiple accounts. It features three progressive anti-blocking layers, three browser modes, zero-interference concurrency, and output optimized for LLM reasoning.

The author recounts their journey from AI skeptic to enthusiast, building an LLM-driven MMO game (SAO: Slop Art Online) and encountering latency issues. They devised a hybrid NPC AI approach combining behavior trees with LLM decision-making, which inspired them to create SLOP, a protocol for agent-application interaction that features contextualized actions and state projections.

This article curates five free resources for learning agentic AI, from structured courses to theoretical foundations and practical evaluation, helping developers build and understand agents effectively.

Give every SAP consultant an AI copilot with system access, with prebuilt tools for querying S/4HANA data.

The article argues against both zero-spec and over-specification in agentic development, advocating for a balanced approach with executable checks. It emphasizes that the bottleneck has shifted to defining correctness, and the right amount of specification depends on the task type—exploratory, bounded, deterministic, or multi-agent.

OpenTSLM is a multimodal LLM that treats time series as a native modality, enabling reasoning over raw multivariate signals alongside text. It outperforms baselines, including GPT-4o, on time series QA, activity recognition, sleep staging, and ECG QA. The model scales to multiple long time series with near-constant memory consumption. ECG reasoning validated by 7 cardiologists with 97% correctness. All code, datasets, and models are open-source.

GPT-5.6 Sol ranks first in Design Arena's Web Design leaderboard, outperforming its predecessor by 18 places. It actively avoids common AI design anti-patterns, combines strong templates with high personalization, and is faster and cheaper than competitors.

A new open-source project called Reaper-MCP enables AI assistants to directly control Reaper DAW via the Model Context Protocol, covering the entire music production pipeline from composition to mixing.

Learn how to connect MCP (Model Context Protocol) servers to Claude Desktop and Claude Code, enabling Claude to interact with external tools, files, databases, and more. This guide covers the MCP architecture, step-by-step setup for both platforms, including one-click Desktop Extensions and JSON config, as well as CLI commands for Claude Code.

The article explores how AI-assisted development leads to 'single-mode burnout' by collapsing the cognitive modes of planning, implementation, and integration, leaving developers exhausted despite increased productivity.

This article summarizes five recent studies on AI in software engineering, revealing that AI compresses upstream work but creates downstream bottlenecks. Key findings: GitHub Copilot increases PR throughput by ~40% with a dose-response effect; AI coding gains (up to +180%) attenuate dramatically through the delivery process (only +30% more releases); productivity and developer experience decouple over time; developers want AI for verification tasks rather than code generation; and cognitive debt and intent debt are emerging as critical software health concerns alongside technical debt.

JetBrains Research explores how AI combined with Extended Reality (XR) can create new interaction paradigms for tech creators. Through expert interviews, they identified five themes: communicating intent to AI-XR systems, AI making XR environments adaptive, barriers to mainstream adoption, changes in creation workflows, and privacy/ethical risks. The study suggests that the convergence of XR hardware and AI may revolutionize technology creation, though technical, cognitive, and organizational constraints remain.

NVIDIA released Nemotron 3 Embed on July 15 and 16, 2026. The collection has three open checkpoints: Nemotron-3-Embed-8B-BF16, Nemotron-3-Embed-1B-BF16, and Nemotron-3-Embed-1B-NVFP4. The 8B ranks #1 on RTEB at 78.46 average NDCG@10. The 1B came from ModelOpt NAS pruning plus COS+MSE distillation from the 8B teacher. NVFP4 retains 99%+ of BF16 retrieval accuracy at up to 2x Blackwell throughput. All three run 32,768-token inputs under OpenMDW-1.1.

The author used AI coding agents to port the Python e-book reader epy to Rust, creating repy. The project took months instead of hours and garnered little attention, prompting reflections on the devaluation of software in the age of AI and the meaning of creation.

Chinese chip designers Moore Threads and Hygon project strong revenue growth driven by surging domestic AI demand. Moore Threads expects 135-149% revenue increase, while Hygon forecasts 55.6-70.2% growth. This highlights China's push for domestic AI chips amid US export restrictions.

Lightport is a lightweight AI gateway focused on making LLM providers OpenAI-compatible. It is a fork of Portkey AI Gateway, stripped down to the request/response transformation layer, without retries, caching, etc. Supports 77 providers including OpenAI, Anthropic, Azure OpenAI, and more. Quick start with pnpx lightport.

The European Union has issued two new rules requiring Google to share search data and open its Android operating system to rival AI companies, aiming to foster competition and innovation. Google warns the move could undermine user privacy and security.

This article introduces voidguard, a new tool that detects hollow tests, type gates, and CI conditions that exist but verify nothing. Inspired by a sweep that found seven such void guards in one repository, the tool identifies four types of voids and explicitly acknowledges three it cannot catch. It emphasizes the importance of verifying our verification systems rather than trusting green CI statuses blindly.

OpenAI has encrypted multi-agent v2 messages in its Codex CLI, hiding agent instructions from local history and raising developer concerns about debugging and auditability.

This paper explores the feasibility of using brain signals via functional near-infrared spectroscopy (fNIRS) to modulate robot reinforcement learning. It compares agents trained on passive (observational) versus active (demonstrative) interaction tasks, and tests multiple methods for enhancing the RL algorithm with the neural signal, focusing on parameter augmentation rather than replacement. The results show that this framework is effective: the neural signal improves learning when augmenting trajectory priorities and state-action q-values. Additionally, the framework learns successfully from offline data, offering a practical alternative for settings where real-time BCI setups are impractical or only limited data is available.

A new study from arXiv proposes a stochastic filtering protocol (ANTk) for quorum sensing in robot swarms that use anonymous communication. The protocol mitigates double-counting bias common in anonymous protocols, improving estimate stability, though it increases error recovery time. The research compares ANTk with baseline and randomized variants, revealing trade-offs in accuracy, speed, and stability.

MonteRET is a region-aware retrieval-enhanced framework for generating chest CT findings sections. It integrates global and regional CT features, retrieves clinically relevant knowledge, and refines reports via a knowledge-guided rewriting agent. Evaluated on public and external cohorts, MonteRET improved report quality, semantic similarity, and clinical efficacy, with experts favoring its outputs.

The paper presents KeyFrame-Compass, the first comprehensive benchmark for evaluating keyframe-conditioned video generation, with 386 curated samples, an automated evaluation framework, and experiments revealing trade-offs between keyframe fidelity and naturalness.

This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory. To ensure that tool efficiency is well-defined, it also introduces marginal tool utility, indicating per tool call whether it is useful or safely removable. The sign of marginal tool utility is determined using LLM-as-a-Judge. This work directly measures efficiency, complementing accuracy-based evaluations, and aims to inform future benchmark design and lean tool suite engineering.

A new study quantifies information loss when LLM agents communicate via text, using sparse autoencoder feature analysis. While latent communication preserves more information under compression, the lost features primarily encode surface form rather than task-relevant semantics, questioning the practical advantage of latent channels.

Proposes Branching Policy Optimization (BPO), which leverages deterministic, snapshottable, and resumable sandboxes to construct a tree-structured rollout topology with shared prefixes, reducing policy gradient variance and improving success rates by 3.6–6.1 absolute points over GRPO and RLOO.

This paper presents an interpretable, globally deployable machine learning framework for predicting representative clutter height (RCH) from open geospatial data. The model, trained with LiDAR-derived labels and using LightGBM, achieves a mean absolute error of 1.79m and R²=0.765, reducing error by over 60% compared to the ITU baseline. SHAP analysis identifies tree canopy cover, land-cover semantics, and spectral reflectance as key predictors. Accepted at IEEE CASE 2026.

Despite the proliferation of XAI techniques, explanations rarely influence real-world workflows. This position paper argues for shifting focus to foundational challenges such as unclear problem formulations, underspecified evaluation objectives, and lack of pipelines for explanation-driven feedback. Based on an analysis of recent papers and a survey of practitioners, the authors propose a checklist to move XAI toward a more human-centered, action-oriented paradigm.

A new study enhances small language model (SLM) reasoning by grounding them in knowledge graphs via a neuro-symbolic agentic framework. Experiments on CLUTRR with Gemma 3 and Llama 3.2 show RGCN-derived hints improve performance by 1.5-2x, but reveal extraction bottlenecks and sequential deductive fragility.

This paper addresses the 'behavioral inertia' problem in tool-augmented LLM agents when expanding their toolset. By injecting counterfactual anchor contexts at critical decision points, the proposed ToolAnchor framework breaks this inertia, recovering failed trajectories. It uses teacher models to hypothesize counterfactuals, verifies them via student rollouts, and internalizes successful interventions through post-training. Evaluated on GAIA, BrowseComp, and VDR-Bench, it shows competitive performance, bridging static post-training and dynamic adaptation.

Researchers propose a novel method using Large Language Models to build Bayesian Belief Networks, employing a panel of AI agents to estimate probabilities based on personas and context, and applying a trimmed-mean rule to reduce noise. A six-step framework is illustrated on customer intention to consult a doctor in an alternative healthcare system, revealing that subjective norms have a much stronger effect than self-efficacy, and the most effective strategy is to improve both confidence and community norms simultaneously.

A new approach called LLM-T1D combines reinforcement learning with large language models to create an interpretable insulin pump controller for Type 1 Diabetes, achieving 73.5% Time in Range while providing clear explanations.

RegNetAgents is an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. It integrates TCGA-derived cancer networks with single-cell regulatory networks from GREmLN, performing dual-network classification, cancer gene filtering via OncoKB, and mode-of-action assignment. Testing on breast and colorectal cancer focal genes showed significant enrichment for known cancer genes and no enrichment for housekeeping controls. An extended module evaluates druggability, clinical relevance, and network vulnerability.

A new paper introduces a three-level hierarchical learning architecture for UAV swarms in search and rescue, integrating Hebbian neuroplasticity, multi-agent RL with GNN and behavior trees, and meta-learning with BDI reasoning. The framework provides formal guarantees and introduces Swarm Meta Cognition.

AI writes code faster than humans can review it, creating a massive trust crisis. Unit tests and prompt engineering aren't enough. Here propose Semantic Contracts—a type-safe, compile-time blueprint that sits between your requirements/prpmpts and code, guaranteeing correctness no matter who (or what) wrote the implementation.

Building a voice AI assistant has never been easier...

Gradle Technologies has rebranded to Develocity, evolving its focus to AI-driven software delivery. The company notes that AI has shifted the bottleneck from human developers to the pipeline, requiring new governance and efficiency measures.

PocketVeto is a local-only, Bluetooth-mediated approval gate and live progress dashboard for AI coding agents. It allows users to approve or deny risky tool calls from their phone without internet. Supports Windows, Linux, and devcontainers. v1 is available now.

Blur & Unblur AI is a free online tool that detects faces in photos, applies blur to selected faces, and exports clean PNGs—all locally in the browser without uploading images.

VulnHunter is an open-source, agentic AI security tool that applies proactive, attacker-first analysis directly to source code. It identifies exploitable vulnerabilities, reduces false positives, and provides evidence-backed fixes.