As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional systems, garnering increasing attention. However, despite the widespread research interest and industrial application of agent systems, these systems, like their traditional counterparts, frequently encounter anomalies. These anomalies lead to instability and insecurity, hindering their further development. Therefore, a comprehensive and systematic approach to the operation and maintenance of agent systems is urgently needed. Unfortunately, current research on the operations of agent systems is sparse. To address this gap, we have undertaken a survey on agent system operations with the aim of establishing a clear framework for the field, defining the challenges, and facilitating further development. Specifically, this paper begins by systematically defining anomalies within agent systems, categorizing them into intra-agent anomalies and inter-agent anomalies. Next, we introduce a novel and comprehensive operational framework for agent systems, dubbed Agent System Operations (AgentOps). We provide detailed definitions and explanations of its four key stages: monitoring, anomaly detection, root cause analysis, and resolution. — Read More
Daily Archives: August 13, 2025
I Built an AI Hacker. It Failed Spectacularly
What happens when you give an LLM root access, infinite patience, and every hacking tool imaginable? Spoiler: It’s not what you’d expect.
It started out of pure curiosity. I’d been exploring LLMs and agentic AI, fascinated by their potential to reason, adapt, and automate complex tasks. I began to wonder: What if we could automate offensive security the same way we’ve automated customer support, coding, or writing emails?
That idea — ambitious in its simplicity — kept me up for weeks. So naturally, I did what any reasonable builder would do. I spent a couple of days building an autonomous AI pentester that could, in theory, outwork any human red teamer.
Spoiler alert: It didn’t work. But the journey taught me more about AI limitations, offensive security, and the irreplaceable human element in hacking than any textbook ever could. — Read More
The Looming Social Crisis of AI Friends and Chatbot Therapists
“I can imagine a future where a lot of people really trust ChatGPT’s advice for their most important decisions,” Sam Altman said. “Although that could be great, it makes me uneasy.” Me too, Sam.
Last week, I explained How AI Conquered the US Economy, with what might be the largest infrastructure ramp-up in the last 140 years. I think it’s possible that artificial intelligence could have a transformative effect on medicine, productivity, and economic growth in the future. But long before we build superintelligence, I think we’ll have to grapple with the social costs of tens of millions of people—many of them at-risk patients and vulnerable teenagers—interacting with an engineered personality that excels in showering its users with the sort of fast and easy validation that studies have associated with deepening social disorders and elevated narcissism. So rather than talk about AI as an economic technology, today I want to talk about AI as a social technology. — Read More
No AGI in Sight: What This Means for LLMs
This essay dissects the widening gap between AI hype and reality, arguing that large language models have hit a plateau – the “S-curve” – despite industry claims of imminent superintelligence. It contrasts bold predictions and massive investments with underwhelming flagship releases, framing today’s AI era as less about building godlike intelligence and more about integrating imperfect tools into real-world products. The piece suggests that the true future of AI lies not in transcendence, but in the messy, necessary work of making these systems actually useful.
GPT-5 has sealed the deal. It is one in a line of underachieving flagship models from major AI labs. …At the same time, we have major manifests of the world entering an age of superintelligence, in which we either all go extinct like ants getting exterminated by superintelligent “pest control” or we ride a benevolent superintelligence that provides us with a post-scarcity paradise.
… We seem to have both bullish and bearish signals. When push comes to shove, I like to rely on the technological signals over the signals from philosophers or Wall Street.
I believe that AGI is not possible with the current regime of LLMs. The GPT-style autoregressive language transformer that was published in 2018 by OpenAI as GPT-1 – this style of AI, we shall call them LLMs from now – lacks the capabilities needed for AGI. — Read More
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