The agent loop: (1) observe the current state (what has been done, what information is available), (2) plan the next action (using the LLM's reasoning), (3) execute the action (tool call, code execution, API request), (4) observe the result, (5) decide whether to continue, adjust, or complete. This observe-plan-act loop repeats until the task is done or the agent gets stuck and asks for help.
The fundamental challenge: each step in an agent's workflow has some probability of error (wrong tool choice, incorrect reasoning, misinterpreted result). Over a 10-step workflow, even a 95% per-step accuracy compounds to only 60% overall success. This is why current agents work best for tasks that are somewhat forgiving of individual step errors (research, brainstorming) and struggle with tasks requiring precision at every step (financial transactions, legal documents).
Most production agent deployments include human oversight: the agent proposes actions, a human approves or corrects, and the agent continues. This "human-in-the-loop" approach sacrifices full autonomy for reliability. The trend is toward wider autonomy for low-risk actions (reading files, searching) and human approval for high-risk ones (sending emails, making purchases, modifying production systems). The right level of autonomy depends on the cost of errors.