AI is evolving. Fast.
What started with tools like ChatGPT—systems that respond to questions—has evolved into something more powerful: AI agents. They don’t just answer questions; they take action. They can plan trips, send emails, make decisions, and interface with software—often without human prompts. In other words, we’ve gone from passive content generation to active autonomy. Our host, Carter Considine, breaks it down in this installment of Ethical Bytes.
At the core of these agents is the same familiar large language model (LLM) technology, but now supercharged with tools, memory, and the ability to loop through tasks. An AI agent can assess whether an action worked, adapt if it didn’t, and keep trying until it gets it right—or knows it can’t.
But this new power introduces serious challenges. How do we keep these agents aligned with human values when they operate independently? Agents can be manipulated (via prompt injection), veer off course (goal drift), or optimize for the wrong thing (reward hacking). Unlike traditional software, agents learn from patterns, not rules, which makes them harder to control and predict.
Ethical alignment is especially tricky. Human values are messy and context-sensitive, while AI needs clear instructions. Current methods like reinforcement learning from human feedback help, but they aren’t foolproof. Even well-meaning agents can make harmful choices if goals are misaligned or unclear.
The future of AI agents isn’t just about smarter machines—it’s about building oversight into their design. Whether through “human-on-the-loop” supervision or new training strategies like superalignment, the goal is to keep agents safe, transparent, and under human control.
Agents are a leap forward in AI—there’s no doubt about that. But their success depends on balancing autonomy with accountability. If we get that wrong, the systems we build to help us might start acting in ways we never intended.
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More info, transcripts, and references can be found at ethical.fm
