Insights into building and using AI agents that interact with tools, reason over tasks, and operate autonomously in real-world systems.
When it comes to adopting AI across an engineering team, many businesses face a surprising challenge: not a lack of model capability, but rather a lack of a safe, consistent, and easy-to-use starting point for employees. Individual experiments with AI…
Traditional security principles may seem like a relic of the past, but they still hold relevance in the realm of AI agent security. However, their application requires adaptation to the unique challenges posed by these systems. The age-old mantra of…
By freeing up software engineers from repetitive tasks, AI agents enable them to focus on innovative projects that drive development forward. Codebase exploration and bug fixing are two key areas where AI agents make a significant impact. With their ability…
Coding tools are transforming the way we do agentic software development. These intelligent assistants are making a significant impact on the industry, automating repetitive tasks that previously consumed a major portion of developers’ time. As coding agents become more mainstream,…
Granting AI agents unfettered access is a security risk. The problem shifts from writing correct code to maximum damage potential. It’s not just about what you can do, but also what you can’t do when AI systems are left unchecked.…
In the current landscape of AI engineering, multi-agent frameworks like CrewAI and AutoGen are highly celebrated. The promise is tempting: take a complex process like reviewing a pull request, decompose it into specialized roles (Security, Architecture, Code Quality, Documentation), assign…
There are many misconceptions when it comes to Agentic AI systems. As it is one of the most overused terms in the IT industry. The term “Agentic AI” is often misused to describe multi-step LLM workflows. Many developers assume that…
Every backend engineer knows the feeling. You’re deep in flow state, the architecture is crystal clear in your head, and the code is coming out clean. Then you spot it – a bug, a missing edge case, a follow-up task…
Fragmented AI tooling creates unnecessary complexity and duplicated work. Before MCP, developers often had to build separate integrations for each AI platform, creating unnecessary complexity, duplicated work, and platform lock-in. Imagine having to develop multiple custom workflows, each tailored to…
Building your first AI agent in 2026 is not the same as calling an LLM API. While a standard LLM call is a static request-response cycle, an agent has the ability to use tools, search the web, reason through failures,…
