  This survey paper examines the recent advancements in AI agent
implementations, with a focus on their ability to achieve complex goals that
require enhanced reasoning, planning, and tool execution capabilities. The
primary objectives of this work are to a) communicate the current capabilities
and limitations of existing AI agent implementations, b) share insights gained
from our observations of these systems in action, and c) suggest important
considerations for future developments in AI agent design. We achieve this by
providing overviews of single-agent and multi-agent architectures, identifying
key patterns and divergences in design choices, and evaluating their overall
impact on accomplishing a provided goal. Our contribution outlines key themes
when selecting an agentic architecture, the impact of leadership on agent
systems, agent communication styles, and key phases for planning, execution,
and reflection that enable robust AI agent systems.
