Codesolvent Blog

Workflow automation is one those perennial technology efficiency promises that has never quite delivered but never goes away.  The appeal of the idea is obvious, being able to easily direct computers to perform process tasks without deep technical expertise is the holy grail of the promise of computers. The excitement around AI agents is the latest incarnation of this promise. Workflow automation can be implemented in many forms, the most common is some sort of visual composition approach. Drag-n-drop workflow builders for instance are quite common. The fundamental problem that workflow automation has faced is that it attempts to allow non-programmers to...well, program. This is a fundamental contradiction that always reveals itself once one tries to use these products to do anything beyond the demo reel. More recent takes on the idea have taken on the problem with a hybrid approach, ie code when you need it and UI when you don't,...this is the literal tagline that the popular work...

Human synthesis vs human substitute , this appears to be the two core perspectives for accessing AI when it comes to agentic execution.  From a human synthesis perspective, you observe that for the first time we have broad computational access to what were previously uniquely human capabilities. At the moment this can be enumerated as the ability to speak , see , hear and understand information . When hominoid robotics are mature enough, we can add the ability to use limbs to this synthesis. Prior to the emergence of LLMs, if you had a process that required any one of these capabilities, you needed to plug a human into the loop. It didn't matter whether the human had any particular expertise or not, if you needed to know what's in a picture or what someone said, you needed a person integrated into your process.  LLM/AI have changed this, we now have computational access to what I would describe as human synthesis . This is different from human substitute , which is what ...

A lot of technologist are rightfully fretting about what the future holds for tech careers, especially in software developer roles. Perhaps it is time to think less about what tab-tab-go programming would mean for the future of developer roles and rather how those existing skill-sets could be leveraged in an AI world. There is tremendous potential in reorienting technologists from a focus on churning out the next app from an IDE and towards thinking in a more holistic manner about how to leverage what already has been built out both in terms of software and infrastructure. The past 30 yrs or so of the tech industry has been a ginormous build-out of technological capability. We in the industry may not have seen it that way since we have been the ones engaged in the build-out process. In other words we have seen the build-out primarily as just our jobs, and less as a process perhaps with a terminal date. I wouldn't go so far as saying the build-out is complete by any means, but it s...

Continuing in our series on the "Intelligent Workspace" as an alternative the the chatbot form factor, we have added another demo showcasing the versatility of the environment.

In a previous demo we showcased the idea of the intelligent workspace and its potential use in fields of scientific research.  Below we showcase integration with an actual biomedical AI assistant agent for the Biomni platform.    

Previously we showed Just-In-Time UI generation for an expense reporting app. Today we show the same capabilities being used to support a drug discovery project. In the demo below, we showcase the tight AI integration of the Solvent-Botworx platform and how one might use such an environment to support the drug discovery process.