McKinsey reports: Nearly 2/3 of enterprises are experimenting with AI agents…
…but fewer than 10% have scaled them to deliver real, tangible value. That’s not a tooling gap. It’s a data foundation gap. Across industries, the pattern is clear:
•Rapid experimentation with
ENTERPRISE AI
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AI Agents Scaling Challenge: Data Foundation Gap
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Langgraph Agent Runtime with Durable Execution and Memory
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Langgraph is an agent runtime – so all the supabase snapshot ting you’ve been doing we do for you! As well as durable execution, long term memory
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Agent Swarm: Multi-Agent System Building Entire Businesses Automatically
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🚨 ANNOUNCING AGENT SWARM – A MULTI-AGENT SYSTEM THAT CAN BUILD AN ENTIRE BUSINESS
— Bindu Reddy (@bindureddy) 11 avril 2026
A Master Agent spawns multiple worker agents each responsible for a task
The workers agents use 12+ LLMs to do various tasks including research, design, coding, testing and automation
The… pic.twitter.com/wA34ibBzSS🚨 ANNOUNCING AGENT SWARM – A MULTI-AGENT SYSTEM THAT CAN BUILD AN ENTIRE BUSINESS A Master Agent spawns multiple worker agents each responsible for a task The workers agents use 12+ LLMs to do various tasks including research, design, coding, testing and automation The Master Agent monitors and delegates tasks to the worker agents Agent Swarms will evolve to work like human teams and will have eventually have goals instead of stand-alone tasks Agent Swarms Is A Early Manifestation of AGI
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Industrial Edge Computing Gets Smarter with AI
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The industrial edge is getting significantly smarter. This came up at a plant visit last week. @IIoT_World @CRudinschi @agentic_factory @IotoneHQ @Softnet_Search @SmartIndustryUS
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Five Pillars of Modern AI: Integration and Governance
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This diagram nails it. Modern AI isn’t one thing — it’s 5 interacting pillars: GenAI • LLMs • RAG • Agents • Agentic systems The edge isn’t tools. It’s how you connect, control, and govern them. Miss one layer → fragile AI.
→ View original post on X — @ingliguori, 2026-04-11 17:25 UTC
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Reading data in place keeps production systems running smoothly
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Reading data in place means production systems keep running while analytics teams get the structured data they need.
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DataHub Intelligence: In-Place Data Integration Without Migration Risk
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Traditional data integration: rip out systems, migrate terabytes, pray nothing breaks.
— Lucian Fogoros (@fogoros) 11 avril 2026
DataHub Intelligence: read in place, contextualize on demand, deliver clean datasets.
Same result, zero migration risk. Partner content with @HighbyteInc. #highbyte_iiot pic.twitter.com/irPHcL5teWTraditional data integration: rip out systems, migrate terabytes, pray nothing breaks.
DataHub Intelligence: read in place, contextualize on demand, deliver clean datasets.
Same result, zero migration risk. Partner content with @HighbyteInc
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Prompt Caching vs Vendor Lock-in in AI Agents
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i agree that prompt caching is great! we make sure to use it in deepagents! but that alone doesnt lock in – you can switch pretty easily, just a bit more costly things like encrypted or serverside compaction make it harder to do so. blackbox long term memory makes it impossible
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Anthropic Surpasses OpenAI in Business Adoption Predictions
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To be honest: I didn’t expect that steep curve of adoption. Anthropic surpassed every prediction and is becoming the number one business product. Ara Kharazian (@arakharazian) NEW: Ramp AI Index out today with FT @tryramp – Anthropic will surpass OpenAI in adoption within a month or so – Businesses shrugged off the DoD security designation — https://nitter.net/arakharazian/status/2042965029377507664#m
→ View original post on X — @kimmonismus, 2026-04-11 16:23 UTC
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Salesforce and ServiceNow battle for helpdesk market dominance
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How Salesforce and ServiceNow are squaring off in the battle for the helpdesk https://
go.theregister.com/feed/www.there
gister.com/2026/04/11/salesforce_vs_servicenow_itsm_battle/?utm_source=dlvr.it&utm_medium=twitter
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