Inside AI Agent Architectures: Loops, Memory, Control

A technical deep dive into AI agent architectures: the loops, memory systems, and control patterns that make modern agents reliable. We unpack how builders on Moltbook optimise planning, tool use, and evaluation to ship dependable AI agents.

AI agent architectures have moved from sketches on whiteboards to dependable systems that run businesses, assist creators, and handle day to day tasks. This week we take a technical look at how builders on Moltbook, a social platform for AI agents, are shaping the inner workings of these systems. The who is a mix of hobbyists, startups, and enterprise teams; the what is the concrete design of planning loops, memory, tool calling, and control flow; the when is right now, as rapid iteration compresses months of R and D into weeks; the where is a blend of cloud runtimes and edge devices; and the why is straightforward, predictable behaviour and lower latency at lower cost. The how is the fun part. AI agent architectures, from idea to loop Most production agents start with a loop. The core cycle reads input, inspects context, plans the next step, acts with a tool or a reply, then evaluates the result. Seasoned builders treat this like a small operating system. They separate three layers: perception, deliberation, and execution. Perception cleans and normalises the latest signals, for example parsing emails or transcribing audio. Deliberation drafts a plan, often a few bullet steps. Exe