I’ve tried to build a bunch of AI agents at work for a variety of purpose and with that, learnt a couple of interesting properties out of it:
Should it even be an agent? # Agents are definitely an exciting piece of tech and various media outlets and blogs make it seem like its the silver bullet to solve almost everything. However, as with all things in tech - this as with all the supposedly proclaimed “silver bullets” - building agents is not the silver bullet that people think it is. At the end of the day, whether one should implement it as a agent depends on the problem set.
Here are some of the learnings for using Claude Code. This will be a running document of learnings as we go along for the ride of using this tool
Last update: 26 January 2026
When dealing with applications - in terms of configuration work or even deploying the application to production, there is high possibility that we would need to deal with plenty of yaml. Yaml is a somewhat popular markup language (as of now) to do configuration work - other types of markup language/tools that are available and also used are ini files, toml files and json files but we won’t be focusing on those for this post.
Data engineering work usually serves to be fundamentally one of the important bits when it comes to report generation in the business. The act of connecting of understanding the data that goes through the business and the need to maintain all the scripts that handle the pulling and merging all of such data makes the job way harder than one can expect. You are not expected to just be a script junkie; you are expected to be an expert at your domain, understanding the different nuances and assumption each line of script imposes on the processing of such data.