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Google Gemini Projects and Workflow Agents for Teams

By

@testingcatalog

–

27 May 2026 17h50

GOOGLE 🔥: Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment.

Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available… pic.twitter.com/kkVRgh4F14
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026

GOOGLE : Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment. Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available

→ View original post on X — @testingcatalog,

27 May 2026
SkillOS: Open-Source AI-Agent Work Compounding Framework

By

@montreal_ai

–

27 May 2026 16h44

http://
MONTREAL.AI just shipped SkillOS. An open-source proof that AI-agent work can compound. Jobs become traces.
Traces become tested skills.
Tested skills improve unit economics. 62% cost ↓
62% time ↓
+46 pts quality Proof: https://
montrealai.github.io/skillos/ GitHub:

→ View original post on X — @montreal_ai,

27 May 2026
AI Agents Automate Task Distribution and Team Coordination

By

@testingcatalog

–

27 May 2026 14h46

Works with Claude Code, Codex, and OpenCode out of the box. Define the org chart once, assign work from the top down, and have agents handle the rest, including handoffs among themselves. Every completed task feeds back into a shared memory layer, building SOPs the whole team

→ View original post on X — @testingcatalog,

27 May 2026
Alook: Open-Source AI Agent Team Orchestration Platform

By

@testingcatalog

–

27 May 2026 14h46

Alook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents.

Close the screen and terminal, the agents keep running, and the work lands in your inbox. pic.twitter.com/zkmRiyidSo
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026

Alook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents. Close the screen and terminal, the agents keep running, and the work lands in your inbox.

→ View original post on X — @testingcatalog,

27 May 2026
Claude Features Enable AI Integration into Real Workflows

By

@ingliguori

–

27 May 2026 14h17

Claude is more than a chatbot • Projects → persistent context
• Artifacts → live outputs
• MCP → external tools
• Connectors → enterprise knowledge
• Rules → operational consistency AI becomes powerful when connected to real workflows. Via Giuliano Liguori

→ View original post on X — @ingliguori,

27 May 2026
Optimizing Codex Prompts for AI Agent Automation

By

@swyx

–

27 May 2026 13h54

UPDATE: Came up with an even better version of this prompt after the feedback Ask Codex to look across your sessions, Memories, and Chronicle, identify patterns, reuse what already exists, and only create the smallest useful skill, subagent, or automation. "Look back over my x.com/reach_vb/statu…

→ View original post on X — @swyx,

27 May 2026
SkillOS Automates AI Agent Skill Development Across Networks

By

@montreal_ai

–

27 May 2026 2h11

SkillOS gives organizations what human teams cannot do manually: turn every completed job into a tested, reusable skill that can upgrade every authorized agent across the network.

→ View original post on X — @montreal_ai,

27 May 2026
Packaging thinking into reusable AI Skills is the next moat

By

@godofprompt

–

27 May 2026 0h58

The next AI moat won’t be “I know how to prompt.” That’s already getting commoditized. The real moat is packaging how you think into a Skill people can run. Your edge becomes reusable.
Your process becomes a product.
Your brain starts earning without being in the room.

→ View original post on X — @godofprompt,

27 May 2026
Multi-Agent Retention Patterns in Graph-Based AI Systems

By

@akshay_pachaar

–

26 May 2026 21h35

Multi-agent retention is a great extension of this. The execution agent needs a tight working set, the audit agent needs full provenance. Context templates in Graphiti help here. Same underlying graph, but each agent gets a different view scoped by edge types, entity types, and

→ View original post on X — @akshay_pachaar,

26 May 2026
SkillOS: Agents Learn Once, All Level Up

By

@montreal_ai

–

26 May 2026 21h29

AI agents shouldn’t start from zero every time. SkillOS captures what works from each job, turns it into a tested Skill, and shares it with every approved Agent. One Agent learns.
All Agents level up. That’s how AI work becomes compounding intelligence.

→ View original post on X — @montreal_ai,

26 May 2026

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