We’ve spent the last two years focusing heavily on LLMs. But I’m starting to think the more important shift might be AI agents rather than just better chat interfaces. An AI agent is not just a model that generates text. It can take input, update its internal state, decide on an action, execute it, observe the result, and adjust accordingly. In theory, this allows it to operate in dynamic environments instead of following static rules. The key challenge seems to be balancing exploration and exploitation. Agents need to decide when to try new strategies and when to rely on what has worked before. That’s easy to describe, but much harder to stabilize in production systems. We’re seeing early deployments in workflow automation, support systems, finance operations, robotics, and decision support. Some reports show efficiency gains, but scaling these systems reliably still appears difficult. Issues like long-horizon reasoning, orchestration between tools, model drift, governance, and safety constraints make full autonomy non-trivial. So I’m curious: Do you think current agent architectures are genuinely ready for realworld multi-step autonomy, or are we still mostly in controlled prototype territory? submitted by /u/Marketingdoctors
Originally posted by u/Marketingdoctors on r/ArtificialInteligence
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