u/dowhatexcite

I'm working on an automation project that currently relies on multiple data sources and a fairly large rule engine to make decisions.

The system works well, but it isn't really "intelligent." It simply processes data through predefined logic and produces an output.

I'd like to evolve it into something that can:

• Reason across multiple inputs.
• Adapt when patterns change without me rewriting rules.
• Learn from previous outcomes.
• Explain why it made a particular decision.
• Continuously improve over time.

I'm trying to understand the best architecture rather than looking for code.

Some questions I have:

• At what point in the pipeline would you introduce an AI agent?
• Would you use an LLM as the reasoning layer, or is this better solved with traditional ML plus an LLM?
• How do AI agents actually "learn" from new results? Do they retrain periodically, use feedback loops, RAG, memory, or something else?
• How would you prevent the system from making poor decisions over time?
• If you were building an autonomous decision-making system today, what would your overall architecture look like?

I'm intentionally keeping the project details vague since it's something I'm actively building, but I'd really appreciate any guidance on designing a genuinely intelligent system instead of just a smarter rule engine.

Thanks!