6/ New post on why most AI agents look great in a demo and quietly stall before they go live, and what separates the 5% that make it to production.
Read it here 👉https://t.co/kGQd3QNHT8
1/ 75% of companies are deploying AI agents. 95% are seeing no measurable return on them, per MIT's 2025 report on AI in business.
That's the gap worth understanding.
5/The 5% of agent projects that work aren't winning because they had better tools. By now, everyone has the same tools.
They're winning because what got built actually fits how the team works.
4/ MIT also found that AI tools built with specialized partners succeeded about 67% of the time. Tools built internally succeeded at roughly a third of that rate.
Same models, same access. The difference was whether the build was shaped to the business.
Build agents from your repo.
Write markdown files for skills. JSON for connections. The platform handles reasoning, context management, and learning.
Your domain knowledge becomes a production AI agent 🔧
https://t.co/693ILrM0df
🚨 Claude tiene sus Managed Agents… pero los builders serios ya están saltando al siguiente nivel.
@lilaiagent acaba de soltar Amodal: el runtime OSS que te da exactamente lo mismo… pero 100% tuyo.
Skills + tools + knowledge + conexiones
traés tu propia llave de LLM
Sin vendor lock-in.
Sin plataformas cerradas.
Sin límites artificiales.
Ideal para quien arma flujos complejos de verdad.
REPOOO👇
Been working on a OSS agent runtime.
Check it out at https://t.co/Mhb4LUbjba
It has some similarities with Claude's new Managed Agents platform: you can define agents through skills, connections, knowledge, tool, etc. Bring your LLM key, and you have a customized agent.