Everyone is adopting AI coding tools. Engineers are writing code faster than ever. But are organizations actually delivering value faster? That’s not obvious. I wrote Enabling Microservice Success with a big focus on engineering enablement, guardrails, automated testing, active ownership, and light touch governance. I didn’t know AI coding agents were coming, but it turns […]
Vibe coding gets you to a prototype. Spec-driven development gets you to production. As AI coding agents grow more powerful, the engineering community has quietly split into two camps: developers who prompt iteratively and hope for the best, and developers who write structured specifications first and let agents execute against them. The second group is shipping faster, with fewer regressions, and with code that survives review. This guide covers the 9 AI tools driving that shift in 2026 — from AWS Kiro's EARS-structured spec IDE to GitHub Spec Kit's 93K-star open-source workflow, to lean execution frameworks like GSD that have crossed 61K stars in under five months.
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If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This “vibe-coding” approach can work for quick prototypes […]
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Attackers too are looking to cash in on the AI coding craze, adapting their supply-chain techniques to target coding agents themselves.
Many AI agents autonomously scan package registries such as NPM and PyPI for components to integrate into their coding projects, and attackers are beginning to take advantage of this. Bait packages with persuasive descriptions and legitimate functionality have cropped up on such registries, while packages that target names that AI coding agents are likely to hallucinate as dependencies are another attack vector on the horizon.
Researchers from security firm ReversingLabs have been tracking one such supply-chain attack that uses “LLM Optimization (LLMO) abuse and knowledge injection” to make packages more likely to be discovered and chosen by AI agents. Dubbed PromptMink, the attack was attributed to Famous Chollima, one of North Korea’s APT groups tasked with generating funds for the regime by targeting developers and users from the cryptocurrency and
An AI agent that revealed sensitive data without being asked. An agent that overruled its own guardrails. Another that sent credentials to an attacker via Telegram, because it forgot it wasn’t supposed to do so after a reset.
It’s no secret that AI agents have huge potential, balanced by equally big risks. What’s becoming apparent, however, is how quickly agentic systems can veer wildly off course and start exposing critical information under real-world conditions.
A look at just how easily this can happen emerges from Phishing the agent: Why AI guardrails aren’t enough, a report on tests conducted by cloud identity and access management (IAM) company Okta Threat Intelligence, which uncovered all of the problems cited above, and more.
Their research focused on OpenClaw, a model-agnostic multi-channel AI assistant which has seen explosive growth inside enterprises since appearing in late 2025.
The Telegram hack
In common with the growing list of rival agents, OpenClaw is only as useful
