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.

▲

mewpmewp2 2 hours ago | parent | next [-]

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?

▲

zkmon an hour ago | parent [-]

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.

▲

mewpmewp2 33 minutes ago | parent [-]

And what is this processor and how does it work?

How does it determine next step from raw non structured content?

Let's imagine for example that it's a message from a potential customer to a business. The processor must decide whether to e.g. give product recommendations, product advice, process returns, use specific tools to fetch more data (e.g. business policies, product manuals, etc), or current pricing, best products matching what the customer might want etc.

If it's an AI agent it could be something like:

1. Customer sends message: "my product Y has X problem". (but the message could be anything from returns to figuring out most suitable product)

2. AI Agent uses "search_products" tool to find info about Y product in parallel with "send_response" to indicate what it's trying to do.

3. AI Agent uses "search_within_manual" tool to find if there are specific similar problems described.

4. AI Agent summarizes information found in manual, references the manual for download and shows snippet of content it based its answer on.

AI Agent itself is given various functions it can call like

1. search_products

2. search_business_policies

3. search_within_documents

4. send_response

5. forward_to_human

6. end_action

7. ... possibly others.

How would you do it in the traditional workflow engine sense?

▲

zkmon 23 minutes ago | parent [-]

I think you missed the whole point. Processor does not have routing logic. It's only job is to parse the request (form, text, image etc) and categorize it enough so that the workflow can do the routing for next actions. Routing is done by traditional predetermined logic, using rules. The discussion here is about whether it helps to define that routing logic at runtime (on-the-fly), instead of having it coded in predetermined logic. My view is, it doesn't help.

	▲	mewpmewp2 21 minutes ago \| parent [-]
		I edited my comment to add more of what the agent would be doing. Not sure if this reached you, but if you read the edited one, how would traditional workflow engine solve the particular problem with a free form raw content that could be anything, and requires using various tools to solve the problem?

▲

baq 2 hours ago | parent | prev | next [-]

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.

▲

zkmon an hour ago | parent [-]

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.

	▲	mewpmewp2 17 minutes ago \| parent [-]
		In many cases I do have AI writing code runtime for my tasks. E.g. if I'm doing analysis of something, or some data, I can ask AI to gather the data (it writes code for scraping or uses pre existing scraping/web search tools), then it uses python or typescript to analyze the data. After which it will use e.g. React in the web app to render the Charts, Tables in customized, personalized format. For instance, I might be looking for a product or something, it will use web search to gather all possible products, then evaluate all the products against my desired criteria, use some sort of scoring mechanism to order the products for me and then write an UI to display the products with their pros and cons specified, with products ranked using an algorithm. Or I might ask it to find all permutations of flight destinations in March, I want somewhere sunny and use weighted scoring algorithm to rank the destinations by price, flight travel duration etc.. Then it writes code to use flights API, to get all permutations and does the ranking. I used to have to go to things like airport websites, momondo, skyscanner, I don't have to do those things manually anymore, thanks to AI agents. I can just let it work and churn out destinations, travel plans according to a scoring criteria. Worst mistakes it can make is, that is missed a really good deal, but this is something I could even more easily miss myself, or worst case it makes a mistake and parses the price/dates wrong, which I will find out when trying to book it, so I waste a bit of time, but similar and worse mistakes I do on my own as well. So overall I drastically reduce my search time for the perfect travel, and also time spent on my own mistakes or misunderstandings. And it will be able to go through permutations far faster and more reliably with infinite patience compared to myself.

▲

nurettin 2 hours ago | parent | prev [-]

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.

▲

zkmon 2 hours ago | parent [-]

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.

▲

nurettin an hour ago | parent [-]

Rules are the context for a workflow and they have to be updated as the environment changes and that is what I've observed. YMMV.

	▲	zkmon an hour ago \| parent [-]
		Yes. But rules themselves live in the context defined by requirements.