Comment by zkmon
6 days ago
Please ELI5 for me: How are AI agents different from traditional workflow engines, which orchestrated a set of tasks by interacting with both humans and other software systems?
6 days ago
Please ELI5 for me: How are AI agents different from traditional workflow engines, which orchestrated a set of tasks by interacting with both humans and other software systems?
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?
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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.
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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.
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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
have you built stuff with LLMs before? genuine question because nondeterministic and deterministic workflows are leagues apart in what they can accomplish.
The human is no longer in the loop. The agentic system is capable of generating quality synthetic data over time to train on. It becomes self improving with the quality synthetic data that can be used to train weaker models to perform better.