AGENTIC-AI
30 days · UTC
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Agentic coding grows up: open‑weights MiniMax M2.7 meets Grok’s tool‑calling workflows
Open-weights MiniMax M2.7 and xAI’s tool-calling Grok push agentic coding from demos to production workflows. NVIDIA detailed the open-weights releas...
Agentic coding goes long‑haul: open models, on‑the‑job memory, and S3 as a file system
Agentic AI for software and data workflows is solidifying, with longer‑running models, practical memory systems, and AWS wiring S3 in as an agent file...
Cursor 3 goes agent-first with a standalone workspace; pilot before you roll it out
Cursor 3 launches an agent-centric, standalone coding workspace with multi-repo workflows, PR review, and cloud/local handoff—and early users report r...
Agent harnesses, not more agents: how teams are actually getting AI to production
Enterprises are shipping reliable agentic AI by building a hardened “agent harness” and resisting unnecessary multi-agent sprawl. Real deployments st...
Enterprise AI agents are moving from demos to governed pilots
Agentic AI in enterprises is shifting from hype to governed pilots focused on interoperability, data access, and measurable outcomes. Recent pieces a...
From vibe coding to orchestrated agents: trace-aware memory and workflows go practical
Agentic engineering is shifting from ad‑hoc prompting to orchestrated, trace‑aware workflows that preserve context, align intent, and iterate reliably...
Agentic coding is going operational: evals, guardrails, and runbooks
Agentic coding is shifting from hype to operations, with new evaluation tooling and sharper focus on reliability and security. Agent platforms are ev...
Appen packages agentic AI data, verifiers, and RL environments for production-grade agents
Appen launched agent-focused data and evaluation services plus an annotation platform built for training autonomous AI agents. The offering wraps ver...
Agentic coding grows up: pipelines, persistence, and cost control land in open source
Agentic coding just took a step from hype to operations with new releases, persistent workflows, and cost-aware controls. The open-source agent stack...
Stop starving your GPUs: make agent rollout a service
Separating I/O-heavy agent rollouts from GPU training nearly doubled coding-agent performance and fixed chronic GPU underutilization. An NVIDIA audit...
Coding agents in production: architecture choices, reliability budgets, and hitting the brakes
A wave of practitioner write-ups agrees: shipping coding agents is about reliability budgets and the right architecture, not flashy demos. At the AAA...
From agent demos to governed fleets: JetBrains Central signals the AI agent control plane
JetBrains introduced JetBrains Central, pointing teams toward a governed, observable control plane for running AI coding agents in real delivery pipel...
Always-on coding agents are arriving; reliability math and monitoring decide if they’re production-ready
Coding agents just became always-on, and the blockers are compounded error rates and the lack of production-grade monitoring. Anthropic shipped /loop...
Agentic AI is coming for your APIs
AI agents are moving from demos to products, and your backend will be their toolbench and bottleneck. Nothing’s CEO says agents will replace many mob...
Anthropic debuts Dispatch: mobile remote control for Claude Cowork on Mac
Anthropic unveiled Dispatch, a research preview that lets Claude Cowork remotely control a Mac from your phone using QR pairing and a sandboxed VM. P...
SWE-CI shifts agent evaluation from one-shot bug fixes to CI-driven maintainability
A new CI-loop benchmark, SWE-CI, measures whether AI coding agents can maintain real repositories over time, not just pass one-off tests. [SWE-CI](ht...
Agentic AI needs a control plane to survive production
Agentic AI proofs-of-concept often crumble in production; a control plane with guardrails and visibility can make them dependable.
Agentic coding needs a harness: ship the guardrails before the agents
Coding agents are useful, but without a real harness and governance they’ll break prod faster than they help you ship. Simon Willison explains how co...
NVIDIA’s Nemotron 3 Super targets long-context, cost-heavy agent workloads with a hybrid 120B model and open weights
NVIDIA released Nemotron 3 Super, a 120B-parameter, 12B-active hybrid model with open weights aimed at long-context, cost-efficient autonomous agents....
Agentic AI moves from chat to production: Databricks launches Genie Code, Microsoft debuts Copilot Cowork, Salesforce ships Agentforce
Enterprise AI agents are graduating from chat to doing real work with guardrails, evaluation, and orchestration across data and business systems. Dat...
NVIDIA’s AI-Q tops DeepResearch benchmarks, hinting at a full-stack agent push with Nemotron 3 Super
NVIDIA’s AI-Q open agent stack hit #1 on DeepResearch Bench I and II and points to a broader open, enterprise agent strategy. NVIDIA details how its ...
Databricks launches Genie Code, an agentic AI to ship and run data systems
Databricks introduced Genie Code, an autonomous agent that plans, builds, and maintains data workflows using Unity Catalog context and continuous eval...
MariaDB moves to acquire GridGain to bring in‑memory speed and vector search to its database
MariaDB plans to acquire GridGain to fold in-memory acceleration and vector search into its database for real-time and AI workloads. Per [InfoWorld](...