AUTOMATION

Lyft’s AI Assist: How Ops Teams Ship Agents and Iterate with Prompts

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27 May 2026 17h36

Today, Ops teams, VoC leads, and PMs are now writing prompts, shipping agents, and iterating. No MLEs required. Read @Lyft
’s guest blog to see how they improved AI Assist, why they treated prompts like product specs rather than code comments, + what’s next.

Lyft Enhances AI Agent Development with LangGraph and LangSmith

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27 May 2026 17h36

@Lyft accelerated agent development from 6 months to just a few weeks with LangGraph and LangSmith. Hallucinations decreased by 20% AI Resolution rate up by 16%

LangSmith Engine Automates AI Agent Improvement Cycles

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27 May 2026 17h14

Improving your agent has been a manual process of:
✅ Reading traces
✅ Looking for patterns
✅ Writing evals
✅ Creating fixes

Now, LangSmith Engine runs that cycle for you. pic.twitter.com/YsFn37mtA3
— LangChain (@LangChain) 27 mai 2026

Improving your agent has been a manual process of: Reading traces Looking for patterns Writing evals Creating fixes Now, LangSmith Engine runs that cycle for you.

→ View original post on X — @kirkdborne

Highly rated new book on generative AI applications with LLMOps

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@kirkdborne

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27 May 2026 17h04

Highly rated new book from @PacktPublishing @PacktDataML … "Architecting Generative AI Applications: Build, deploy, and scale production-ready GenAI systems with LLMOps best practices" See it at https://
amzn.to/3Pv4dyF

→ View original post on X — @datachaz

Akshay Pachaar’s 47-minute walkthrough on building autonomous agents

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@datachaz

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27 May 2026 16h54

Okay.. @akshay_pachaar’s Hermes article was already top-tier, but his new 47-minute walkthrough is INSANE 🤯

Skip Netflix, grab a coffee, and watch this masterclass on how to build self-improving, 24/7 autonomous agents locally on your machine 👀↓pic.twitter.com/AClp9LOocn https://t.co/YVecaPzsb1
— Charly Wargnier (@DataChaz) 27 mai 2026

Okay.. @akshay_pachaar
’s Hermes article was already top-tier, but his new 47-minute walkthrough is INSANE Skip Netflix, grab a coffee, and watch this masterclass on how to build self-improving, 24/7 autonomous agents locally on your machine ↓

→ View original post on X — @montreal_ai

SkillOS: Open-Source AI-Agent Work Compounding Framework

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@montreal_ai

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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:

Deep Agents v0.6 Introduces Delta Channels for 100x Checkpoint Storage Reduction

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27 May 2026 15h54

Deep Agents v0.6 brings Delta channels, reducing checkpoint storage by up to 100x for long-running agents, without sacrificing observability or resilience. Here’s a 200-turn coding agent session.
Without Delta Channels: 5.3GB of checkpoint storage
With Delta Channels: 129mb

→ View original post on X — @sumanth_077

Run your personal AI company with Alook’s AI agent team

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@sumanth_077

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27 May 2026 15h28

Run your personal AI company with a team of AI agents! Alook is an open-source collaboration platform for AI coding agents. Self-hosted and local-first. The setup: Define an org structure. Give each agent a role – dev, ops, research, whatever you need. Set reporting lines.

→ View original post on X — @testingcatalog

AI Agents Automate Task Distribution and Team Coordination

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@testingcatalog

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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