Agentic AI & agents
AI Agent Architecture Explained
What is actually inside an agent, beyond the marketing term.
Strip away the hype, and an AI agent is a fairly specific software pattern: a control loop that repeatedly asks a model to decide the next action, executes that action through a tool, and feeds the result back in, until a goal is met or a limit is reached.
Understanding this architecture concretely — rather than treating "agent" as a magic black box — is essential for building agents that are debuggable, safe, and actually reliable.
Key takeaways
The core control loop
At each step, the agent’s model is given the current goal, its progress so far, and the tools available, and asked to decide the next action: call a specific tool, ask a clarifying question, or declare the task complete.
That action is executed, the result is observed and added to the agent’s working context, and the loop repeats. This simple repeating structure is the backbone of nearly every agent framework, regardless of branding.
Tools, memory, and stopping conditions
Tools need clear descriptions, predictable input and output formats, and graceful error handling — an agent that cannot understand a tool’s failure message will often retry blindly or hallucinate a fake result.
Memory keeps track of what has already happened in the current task (and sometimes across sessions). Stopping conditions — a maximum number of steps, a cost budget, or a clear success check — prevent runaway loops that quietly burn money without making progress.
Common architecture failure points
Vague tool descriptions cause the model to call the wrong tool or misuse the right one. Missing stopping conditions cause infinite or excessively long loops. Poor error handling causes an agent to silently continue with bad data instead of correcting course.
Most agent reliability problems trace back to one of these three issues, not to the underlying model being "not smart enough."
Put this into practice