Agents
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New Models
Pipecat releases PhoneLLM, a 3.5B-active model tuned for voice agents
Pipecat's PhoneLLM Alpha 1 is an open 30B MoE with 3.5B active parameters, built for low-latency voice agents and evaluated with the new PhoneBench.
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Agents
OpenAI sets a November cutoff for its models inside Cursor
OpenAI plans to end model supply to Cursor after SpaceX's acquisition, creating a November 12 migration deadline for developer workflows.
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Research
Gemini Co-Scientist closes the loop from hypothesis to experiment and verified claims
Google DeepMind extends Co-Scientist from hypothesis generation into execution-grounded research with lab workflows, autonomous code experiments and claim verification.
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Research
Fabricated dashboards can make LLM agents act on unknowable outcomes
A reproducible arXiv study finds that fabricated authoritative-looking panels can push some LLM agents to act on unknowable outcomes despite recognizing the uncertainty.
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Agents
Anthropic’s MHS gives AI agents a common interface to physical machines
Anthropic’s Model Hardware Standard gives AI agents a shared interface for lab and industrial devices, with early pilots and explicit safety limits.
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Research
openJiuwen turns the coding-agent harness into a measurable systems layer
openJiuwen proposes a composable, runtime-adaptive coding-agent harness and shows why model-matched benchmark audits matter for agent engineering.
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Agents
Broadcom moves AI agent authorization into an external control plane
AgentMinder moves enterprise AI agent authorization outside the runtime, making identity, mission provenance and policy context part of the security boundary.
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Research
Self-evolving AI agents can turn poisoned experience into persistent skills
New research shows self-evolving agents can store poisoned interactions as reusable skills, making skill provenance, validation and rollback part of the security boundary.
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🧭 The Five Layers of Agent Governance and Why Most Teams Still Only Have Two (2/10)
A practical five-layer model for governing AI agents across identity, action, output, change and outcomes in AI-native software engineering.
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You Think You Have AI Governance. You Don’t.
Why access controls and human approval are not enough for AI-native engineering, and what real agent governance requires across software delivery.