r/learnmachinelearning • u/Senior_Disaster_7307 • 17h ago
Discussion A Probabilistic / Bayesian Agent Model [D]
I’ve been thinking a lot about what it actually means to build useful AI agents.
The more I learn about agentic systems, the more I realize that an agent isn’t just an LLM connected to a few tools.
Lately, I’ve been learning about what I’m starting to think of as an “agentic discipline,” and one idea has really changed how I think about LLM applications.
The traditional mental model is:
Input → Model → Output / Action
But real-world problems rarely work that way.
You make an initial decision with incomplete information.
Then you take an action.
You observe new evidence.
You update your understanding.
And then you make a better decision.
So I’ve been exploring whether we can think about agentic systems through a probabilistic / Bayesian lens:
Initial belief (Prior)
↓
Choose an action
↓
Observe new evidence
↓
Evaluate the likelihood of that evidence
↓
Update belief (Posterior)
↓
Choose the next action
↓
Repeat
Instead of only asking an LLM:
“Give me the answer.”
What if we design the system to continuously ask:
- What do I currently believe?
- What evidence would change my belief?
- What action should I take next?
- Which action would reduce my uncertainty the most?
- Did the last action actually improve my understanding?
This feels like a much more powerful way to think about agents.
The interesting part isn’t simply adding more tools or more LLM calls.
It’s designing a system that can reason under uncertainty, actively gather information, update its state, and make better decisions over multiple steps.
I’m still exploring this idea and trying to understand where the Bayesian framing is genuinely useful versus where it’s simply a useful analogy.
I’d love to hear from people working on agents, reasoning, or probabilistic AI
How do you think about belief updating and uncertainty in agentic systems?
1
u/JinSakai_77 2h ago
It's a very good model...