Comment by alberth
6 days ago
Traditional workflow is largely predefined & rule-based.
There’s a level of autonomy by the AI agents (it determines on its own the next step), that is not predefined.
Agreed though that there’s lots of similarities.
6 days ago
Traditional workflow is largely predefined & rule-based.
There’s a level of autonomy by the AI agents (it determines on its own the next step), that is not predefined.
Agreed though that there’s lots of similarities.
But rule-based processing was exactly the requirement. Why should the workflow automation come up with rules on the fly, when the rules were defined in the business process requirements? Aren't the deterministic rules more precise and reliable over the rules defined by probabilistic methods?
Autonomy/automation makes sense where error-prone repetitive human activity is involved. But rule definitions are not repetitive human tasks. They are defined once and run every time by automation. Why does one need to go for a probabilistic rule definition for a one-time manual task? I don't see huge gains here.
Sometimes the rules are not as easy to define ahead of time. As an example imagine having to categorize some sort of text based requests etc.
Or decide what the next step should be based on freeform text, images, etc.
Hardcoded rule based would have to try and attempt to match to certain keywords etc, but you see how that can start to go wrong?
This is already solved by the traditional workflow systems. For example, if the request is received as a form submission, a form processor is invoked to categorize the request and route the request accordingly based on the identified category.
Now, if the request is coming in as text or other media instead of a form input, then the workflow would call a relevant processor, to identify the category. Everything from that point runs same as before. The workflow itself doesn't change just because the input format has changed.
6 replies →
There is always much path dependence in what becomes the business requirements, making the business requirements less than optimal to start with.
Then over time their is a type of entropy with all business processes.
If we don't figure out dynamic systems to handle this it is hard to see how we get a giant productivity boost across the economy.
There is also the problem that what percentage of people even have exposure to the concepts of dynamic systems? When I was in college, I distinctly remember thinking dynamic systems, "chaos theory", was some kind of fractal hippy pseudoscientific fraud best to ignore.
I think of how often I hear the average person use language from probability theory but never from dynamic systems.
Workflows exist to solve problems. If there are problems which need solving that are solved better/faster/cheaper by AI agents than with strict rule-based algorithmic systems, they’ll be used because it makes economic sense. Reliability requirements are different for every problem, cases where verification is easy and cheap and multiple attempts are allowed are perfect for not 100% reliable agents.
it's fine if you want AI to help you in defining the workflow/rules. But you don't use AI to define rules on the fly. That's the whole point. It is like having AI to write code at runtime based on the request. I don't think that's how you use AI in software.
7 replies →
I like determinism and objectivity as much as the next guy, but working in the industry for decades led me to realize that conditions change over time and your workflow slowly drifts away from reality. It would be more flexible to employ an AI agent if it works as promised on the tin.
There is no "reality" other than business requirements. That's the context for a workflow. You probably meant that the requirements aren't agile enough to meet the changing world outside. That's a different problem, I think. You can't bypass requirements and expect workflow to dynamically adapt to the changing reality. If that's the direction with AI-driven business re-engineering, then we are back to the chaos, exposing the business logic directly to the outside world.
2 replies →
I needed some data from a content API, had a few options:
1) Human agent, manual retrieval (included for completion
2) one-off script to get exactly the content I want
3) Traditional workflow, write & maintain
4) one off prompt to the agent to write the script in #1, sort and arrange content for grouping base on descriptions it receives (this is what I used, 3 hours later I had a years worth of journal abstracts of various subjects downloaded, sorted, indexed and summarized in a chromadb. I’d just asked for the content, but it’s python code it left for me included a parameterized CLI with assorted variables and some thoughtful presets for semantic search options.)
5) one off prompt to the agent to write the workflow in #3, run at-will or by agent
6) prompt an agent to write some prompts, one of which will be a prompt for this task, the others whatever they want: “write a series of prompts that will be given to agents for task X. Break task x down to these components…”
I noticed on our own agentic setups that there are very few actual scenarios being executed. I suggested implementing some type of monitoring so you can replace 99% of most used workflows with normal python and activate AI calls if something new happens. until that new thing repeats few times and you translate that to code to. that has to be carreer in itself. you can turn a lot of AI apps into profitable and fast internal apps