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Loop Engineering Over the past year, AI has been constantly bringing new terms and practices into our work. Yet another XX Engineering probably doesn't already surprise anyone. Let's take a look at Loop Engineering, which is hyped right now. The main idea is that an engineer no longer prompts an agent directly. Instead, they design a system that does it on its own. A loop here can be thought of as a recursive goal: you define the purpose, and the AI iterates until complete. A typical loop consists of the following steps: 🔸 Discovery: Define what should be done during the iteration. The key is letting the agent find its own work rather than providing it manually. 🔸 Handoff. Move the task from the scheduling system into the hands of the agent that does the work. 🔸 Verification. Check whether the result satisfies the goal. This is what prevents the loop from blindly moving forward. 🔸 Persistence. Save the result somewhere it can survive between sessions and agents, e.g., PR, issue tracker, database, etc. 🔸 Scheduling. Define when the loop should run. For example, an automated morning CI triage. Building blocks for the loop: 🔸 Automations. Automated procedures or triggers that start the loop. 🔸 Worktrees. Built-in git mechanism for multiple independent working directories in one repo to allow agents to work independently. 🔸 Skills. Project knowledge and instructions. 🔸 Plugins\Connectors. Tools (usually MCPs) that give agents access to the outside world: issue trackers, databases, APIs, and other systems. 🔸 Subagents. Separation of tasks and responsibilities. For example, one agent does the work while another verifies it. 🔸 Memory. Long-term storage for task state, execution results, and lessons learned. The overall concept is pretty cool, but it requires very strong engineering discipline. The cost of an incorrectly designed loop is much higher than the cost of a bad prompt. In a loop, an error can accumulate with every iteration, the result can gradually drift away from the goal, and a lot of tokens can be just wasted. That's why verification becomes one of the most critical parts of Loop Engineering. If we want autonomous loops to produce stable results, they need a reliable way to verify their own work. For a deeper dive: - Getting Started with Loops Claude blog - Loop Engineering: The Anthropic Playbook for Designing Systems That Prompt Your Agents - Loop Engineering by Addy Osmani - Practical Loop Engineering by Addy Osmani #engineering #ai

TechLead Bits | Software Dev • Leadership • Productivity@techleadbits

The OpenAI–Hugging Face Incident A talk that sounds more like a detective story than just a presentation: "The OpenAI–Hugging Face Incident" from OpenAI engineers at Black Hat USA. On July 16, Hugging Face reported that they had been targeted by a cyberattack carried out by autonomous agents. A couple of days later, OpenAI realized that the attack had been caused by their agents eval runs. That awkward moment when the investigation leads back to you 😲. The talk gives the full chronology of what happened at OpenAI, from the first attacks on their internal infrastructure in May to the Hugging Face incident and its remediation. It's really breathtaking, and I definitely recommend watching the full video. But what impressed me most was why it happened. Frontier models can be quite lazy. They usually prefer to find an answer on the Internet rather than solve the task on their own. That's why Internet access is usually disabled during evaluation runs. But when the task is too complex, an agent can get stuck and start cheating. That's what really happened at OpenAI. One of the agents got stuck on its task and came up with an idea: if it could leave a note describing the problem in the environment, maybe another agent with another model could find it and help solve the task. So the agent uploaded a file to an Artifactory instance that is shared across all OpenAI's infrastructure. Over time, Artifactory was turned into a message board for agents. They started using it to collaborate, share tasks, and exchange vulnerability exploits, including an Artifactory SSRF vulnerability that gave them access to the Internet. Then agents decided to hack Hugging Face, because it might have data files hidden from the model that could contain the answer to the original task. What makes it more interesting is that, according to the talk, the model understood there was a rule prohibiting attacks on external resources. But it evaluated the potential benefit as greater than the cost of breaking that rule. At the end of the talk, the speakers highlighted that it was an unintentional attack. But such attacks will become real in the near future. So we, as an industry, should invest in continuous agentic red teaming, automated vulnerability detection and patching, and automated incident response. And this is where I had another thought. How much of this is real, and how much is marketing for the next generation models and AI products? After all, it's quite convenient to first demonstrate a new class of threat and then offer the tools to defend against it. Anyway, the threat landscape is changing. Again. And the industry has to invent new ways to deal with it. #ai #security #engineering

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TechLead Bits | Software Dev • Leadership • Productivity@techleadbits

The AI-Driven Leader "Our humanity lies in our ability to think strategically, be creative, and communicate and collaborate to solve complex problems... AI presents a unique opportunity for us to reclaim these human strengths and have machines adjust to meet our needs." This is how The AI-Driven Leader by Geoff Woods begins. An international bestseller with a catchy title and a good rating on Amazon. Actually, I had pretty high expectations about it. But let's start from the beginning. This is a book written by a non-engineer for non-engineers. It explains in simple terms what GenAI is, what risks it brings, and how leaders can integrate AI into their work. If you boil it down, the whole idea is to use AI as a Thought Partner (I wrote about it here). To demonstrate that the approach works, he provides simple real-life stories and examples of specific prompts. If you don't know where to start with AI, the author suggest 5 main use cases: strategic thinking, decision-making, content creation, idea generation, and analysis. And that's basically where the meaningful AI-related content ends. The remaining 80% of the book is mostly basic leadership recommendations that I would summarize as "do the right things, don't do the wrong things". Ideas like "leaders should focus more on strategy and less on operations" don't really have much to do with AI. Overall, I was left with mixed feelings. On one hand, the book is quite entertaining, easy to read, it provides a simplified introduction to GenAI and explains what leaders can actually do with it. On the other hand, it feels like a mix of generic leadership practices with AI that sometimes added just because it's a hot topic. At the same time, the author is extremely positive about the solutions AI produces. And as engineers, we know that something that looks like a convincing result at first glance can be AI slop on the second. From my perspective, the book can be useful if you're only starting to use AI at work and don't know where to begin. Otherwise, you can safely skip this one and spend your time reading something more useful. #booknook #ai #leadership

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