271 summarised stories about Agentic Workflows, each linking back to the original source. Browse all topics →
Product Hunt 1 week ago 2
pmtui was launched as a tool for managing multiple AI coding agents and handling long-horizon tasks. It is listed as having 4 followers. It changes agent execution by keeping the agent loop outside the model and rebuilding prompts from the goal and the agent’s last status at your chosen check intervals.
Microsoft 2 weeks ago 13
Microsoft Research Asia introduced Harnessed Agentic RL by open-sourcing Agent Lightning v1.0, where the same agent harness used in deployment participates directly in reinforcement learning without being reimplemented in the training loop. The paper reports raising Qwen3.5-9B Pass@1 on SWE-bench Verified from 41.8% to 56.4% using about 6,000 training samples. Agent training shifts from tokenizing a rebuilt interaction loop to observing real harness model calls via a proxy, enabling end-to-end RL control on standard Kubernetes jobs with a lightweight ~3,500-line codebase.
Product Hunt 2 weeks ago 10
SquadRoom launched with AI workflow automation that assigns tasks to a lead agent and specialist agents, then shows a step-by-step report with sources after you return. It starts with 21 followers on the page. Users can bring their own AI key and can run a live demo without signing up, with items requiring approval held back until you OK.
Oğuzhan Olguncu 2 weeks ago 10
The author argues that LLMs make it harder to learn from mistakes and describes how they re-learned programming learning by using an LLM as a mentor instead of an answer source. They built a project called tinylsm and used a plan/checklist approach, plus a benchmark/REPL example showing reads at 174.9µs with 1 table. As a result, they use question-driven explanations, pre-planned phases, visible progress, and short consistent sessions to regain motivation and reinforce understanding.
SiliconANGLE 2 weeks ago 14 ● 2 sources
Atlassian announced the Agentic Multiplayer Protocol to let human workers and AI agents collaborate in the same governed workspace with shared context and task controls. Atlassian said its expanded Model Context Protocol server exposes 200 tools and handles about 15 million tool calls daily. As a result, Atlassian’s Rovo Work, Loom, and agent tooling are geared toward longer, human-reviewed tasks and richer integration with Teamwork Graph and external data platforms.
The daily briefing
TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the day in two minutes. Follow companies and topics for alerts, or get the briefing in Slack. Free, no spam, unsubscribe anytime.
TLDRocket
21 October 2026
The biggest AI throughline today wasn’t model quality, it was control. As enterprises push more “autonomous” agents into real workflows, Red Hat and Nvidia expanded their partnership to move security enforcement closer to the infrastructure—adding runtime and infrastructure security controls for AI agents running on Nvidia’s Vera Rubin compute via Red Hat AI Factory with Nvidia. The update brings BlueField-3 support in Red Hat OpenShift 4.22, plus Sentry monitoring that Red Hat says can quarantine a compromised agent in milliseconds. The pitch is familiar—govern access to tools and data—but the specifics matter: model access governance, joint support for Nemotron 3.5 Super VL in vLLM, and enforcement that’s meant to live outside the agent (and, where possible, outside the host OS).
Read the full briefing →