The Architecture of an AI Agent

An AI agent is not a prompt. It is a system with inputs, decision logic, tools, memory, and outputs.

The five layers

Every well-designed agent has five layers:

1. Perception — what the agent can sense (user input, API data, file contents, embeddings)

2. Memory — what the agent can remember (short-term context window, long-term vector store, structured state)

3. Reasoning — how the agent makes decisions (the LLM plus its prompt architecture)

4. Tools — what actions the agent can take (search, write, call APIs, run code)

5. Execution — how the agent delivers its output (webhook, UI, database write, message)

Where most agents fail

Most agent implementations fail at memory. They assume the context window is sufficient. It never is at scale.

The fix: design your memory architecture before you write your first prompt. Decide what gets summarized, what gets embedded, what gets discarded.

The orchestration question

Single-agent or multi-agent? The answer is always: as simple as possible. Add agents only when a single agent provably fails at the task.

Complexity compounds. Two agents communicating is four times harder to debug than one. Three is nine times.

Start with one. Instrument it. Learn from it. Expand only when the data demands it.