this is wrong, it's almost impossible to build a fully-rational (in the Game-Theoretic sense) agent for almost any real life usecase, except some textbook toy problems.

There are many levels of Intelligence/Cognitions for Agents.

Here's an incomplete hierarchy out of my head (the real classification will deserve a whole blog post or a paper):

  - Dumb/NPC/Zero-Intelligence
  - Random/Probabilistic
  - Rule-based / Reflexive
  - Low-Rationality
  - Boundedly-Rational
  - Behavioral (i.e. replicating a recorded behavior of a real-life entity/phenomena)
  - Learning (e.g. using AI/ML or simple statistics)
  - Adaptive (similar to learning agents, but may take different (better) actions in the same situation)
  - [Fully-]Rational / Game-Theoretic

--

[1] Aaron, 1994

[2] Growing Artificial Societies -- Joshua M. Epstein & Robert L. Axtell

AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt (Google)

Agents “can be defined in several ways” including both “fully autonomous systems that operate independently over extended periods” and “prescriptive implementations that follow predefined workflows” (Anthropic)

Agents are “automated systems that can independently accomplish tasks on behalf of users” and “LLMs equipped with instructions and tools” (OpenAI)

Agents are “a type of system that can understand and respond to customer inquiries without human intervention” in different categories, ranging from “simple reflex agents” to “utility-based agents” (Salesforce)

A few days ago in TechCrunch: No one knows what the hell an AI agent is

https://techcrunch.com/2025/03/14/no-one-knows-what-the-hell...

my reply was to this definition, where the adjective "rational" was used with the noun "agents" by AWS, so it's obvious they're not talking about Human agents.

> When people use phrases like "rational decisions" it is generally a statement of intent.

I frequently hear on the news something like "the terrorists are rational". They completely missing the point that an agent might be rational (i.e. optimizing) for one variable, while they projecting it on a completely diffrerent variable. I.e. for non-textbook toy problems agents usually have lots of variables they care about, so when you talking about rationality you should specify that specific variable, and not generalize that b/c they're somewhat rational at one thing, they will be rational at all other things.

I don't follow your meaning?

> you should specify that specific variable, and not generalize that b/c they're somewhat rational at one thing, they will be rational at all other things.

You are being overly pedantic, although you might not realize it. The meaning in that example is the differentiation between someone with a nuanced goal who can be reasoned with versus someone who has lost all reason and only desires to lash out at others. The latter is a distinct possibility when it comes to violent acts.

sorry, bad edit, should be:

... so it's obvious they're (AWS) not talking about Human agents.

Workflows means some organization signed off on what has to be done. Checklists, best practices, etc.

Agents on the other hand have a goal and you have no idea or what they’re going to do to achieve it. I think of an agent’s guardrails as essentially a “blacklist” of actions, while a workflow is a “whitelist”.

To me, agents are a gimmick the same way that real-time chat, or video, is a gimmick. It is good for entertainment but actually has negative value for getting actual work done.

Think of it this way… just as models have a tradeoff between explore and exploit, the agents can be considered as capable of exploration while the workflows exploit best practices. Over time and many tasks, everything is standardized into best practices, so the agents become worse than completely standardized workflows. They may be useful to tinker at the edges but not to make huge decisions. Like maybe agents can be used to set up some personalized hooks for users at the edges of some complex system.

https://medium.com/@falkgottlob/many-ai-agents-are-actually-...

I have build a narrow AI for credit decisioning on a 100B portfolio between 2012 and 2020. This "agent" can make autonomous credit decisions, if and only if the agent is 100% certain that all inputs are accurate. The value comes from the workflow, not the model.

LLMs change this as there is now a general, I like to call them vanilla models, that does not specifically be trained to the data set. Would I use that in this workflow? Likely not.

(a) it is likely that the narrow model is cheaper to operate than a larger model without seeing a substantial benefit in productivity.

(b) in regulated industries we always need to be able to explain why the AI made a decision. If there is no clear governance framework around operating the agent, then we can't use it. Case in point > "nH predict"