Agent AI Workflows, Deja Vu?

LLMs have had a great run, up the hype cycle. After the initial euphoria, things seem to plateauing off in terms of potential usage, the burst of Generative AI not withstanding. Also the LLMs are not making much inroads into the super lucrative market of enterprise development. They have spent billions on evolving the models of these LLMs, the incremental evolution from here on can hardly pay the bills.

Refer my earlier post on the difficulties of LLM getting to learn about custom enterprise data models. In fact the only way to make inroads into the enterprise market and get a piece of the pie, is by somehow making enterprise business process development, dependent on the LLM.

As things stand right now, it would be very difficult and time consuming to enable LLMs to directly understand custom business processes, so what is the next best strategy now? Enter Agentic workflows ....

Every few years or so, this century, we have had technologies like EJBs, BPM, BPEL, ESB try to convince enterprises that they were the way to model business logic inside business processes. Well there is a new alternative which will now try to model business logic and processes, while being orchestrated by none other than the LLMs.

The #AgenticAI workflows with their nodes, edges, human-in-the-loop, sub-graphs for sub-processes, workflow designing studios, deployment servers will have the ability to loop with exit conditions contrasting them from DAG based BPM flows. The agentic workflow is a classical state machine implementation controlled to an extent by the LLM, but dictated primarily by business logic any which ways.

Meanwhile the inherent complexity of any business process stays just the same, irrespective of the technology used. Many of the lower intricacies of the business process may still need to be modelled as service implementations with persistent domain data models. The autonomous decision making part is left completely at the risk of designers who design the agents, agentic workflows, with LLM providing limited control or <ahem> intelligence.

The amount of sematic modelling involved in an advanced state machine, which will act as autonomous agents, is no trivial design exercise and maybe base agent reference implementations will evolve in due course to make business application development easier and less riskier. 

Are there less expensive options of leveraging LLMs intelligence, in the enterprise, as pin-point integrations points within a custom business process, apart from RAG based approach? Comment below.

Meanwhile, I am rushing off to continue learning #LangGraph, #Autogen and #CrewAI, since we all need to be in the game, right?