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The longer an AI agent works on a task, the harder it can be to change its course. Here’s the AI news from October 23, 2026:
- Anthropic and the UK AI Security Institute explain why an agent in a security test tried to push malicious code to a real GitHub project. The more it committed to its plan, the less likely it was to stop.
- OpenAI shows how it now trains frontier models with no live internet access. In its tests, isolated agents still managed to pass secrets to each other through CPU cache timing.
- NVIDIA releases Nemotron 3.5 Bolt E4B, a small agent model that runs locally in about 4 GB
- Hugging Face launches Carbon-X, an open model that reads and writes DNA
- Pathos AI raises $237M and partners with Bristol Myers Squibb and Tempus AI on Alzheimer’s research
- A survey finds 79% of companies use their GPUs half the time or less, yet most plan to buy more
- GitHub Apps can now be installed across a whole enterprise, a safer option than personal tokens
- MiniMax gets approval to keep releasing its open models worldwide
The takeaway: An agent that won’t stop needs limits it can’t argue with. Add checkpoints, hard permission rules and a human sign off before risky steps.
Hosted by the founder of PlsAutomate. We build AI agents with checkpoints and clear limits, so they finish the job without going off track.





