The solution is isolation by design. → Research agent: can browse the web, cannot touch private data
→ Action agent: can access sensitive tools, has no internet
→ Credentials: stored in a vault, agents use them without seeing secrets
→ Sensitive actions: trigger a human
SYSTEMS
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Isolation by design for secure agent operations
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AI Agents: Trust Problem Solved by New Architecture
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The problem with AI agents isn't intelligence. It's trust.
— Ronald van Loon (@Ronald_vanLoon) 27 juin 2026
The moment an agent becomes useful, it also becomes dangerous.
Here's the architecture that changes that.
A thread. pic.twitter.com/Iiw0DvOILnThe problem with AI agents isn't intelligence. It's trust. The moment an agent becomes useful, it also becomes dangerous. Here's the architecture that changes that. A thread.
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Microsoft’s Singularity: 100,000 GPU Planet-Scale AI Infrastructure
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Singularity: 100,000 GPUs. AI Infrastructure at Planet-Scale! Singularity is Microsoft’s distributed scheduling AI infrastructure, designed for the highly efficient and reliable execution of deep learning training and inference. Maximizing accelerator utilization without
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Deep Agents uses cache-aware requests to reduce costs
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> Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. We do this for you in Deep Agents – see our blog on it here: https://
x.com/its_ao/status/
2070556265906917860?s=20
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Proof-gated coordination: missing architecture for large multi-agent systems
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We have identified the missing control architecture for large multi-agent systems:
proof-gated coordination. Large multi-agent systems coordinate to optimal effect when they are organized around proof-gated work, validator-mediated acceptance, governed memory, settlement, and -
GoalOS proposed as evidence-bearing release-governance layer for frontier AI
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The world now needs an evidence-bearing release-governance layer for frontier AI. GoalOS is unusually well-shaped to become that layer. #MontrealAI
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GoalOS provides bounded, evidenced, validated, governed AI work as default unit
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The industry is producing increasingly capable systems, but capability alone does not solve authorization. In high-trust environments, work must be bounded, evidenced, validated, governed, and reusable. GoalOS makes that the default unit of AI work.
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GoalOS converts autonomous AI output into evidence-bearing institutional capability
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GoalOS turns autonomous AI output into evidence-bearing institutional capability. #AGIALPHA
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Designing Machine Learning Systems – Iterative Process for Production
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Designing #MachineLearning Systems — An Iterative Process for Production-Ready Applications: http://
amzn.to/46epLSi by @chipro — 𝓣𝓸𝓹𝓲𝓬𝓼:
Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, -
RAG-Driven Generative AI 2nd Edition: Build MAS-RAG with DualRAG
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New (2nd edition) from @PacktDataML available at http://
amzn.to/4tULP1b RAG-Driven Generative AI — Build MAS-RAG with DualRAG, GraphRAG, multimodal video pipelines, and Oracle Database 23ai 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀:
Master DualRAG by combining vector search with SQL filtering