Beyond the conceptual pieces of the decision loop, a real agent system is made of concrete software components — the LLM, a tool layer, a memory/state store, and an orchestration layer tying them together.
The Four Structural Components
| Component | Responsibility |
|---|---|
| The LLM ("the brain") | Makes decisions given the current context — which action to take next, or when to produce a final answer |
| Tools | The functions/APIs the agent can actually call — see Tool Calling |
| Memory / state store | Holds conversation history, intermediate results, and anything that needs to persist across steps — see Agent Memory and State |
| Orchestration layer | The actual code running the loop — calling the LLM, validating and executing tool calls, updating state, deciding when to stop |
How They Fit Together
┌─────────────────────────────────────────┐
│ Orchestration Layer │
│ (runs the loop, owns control flow) │
│ │
│ ┌────────┐ ┌───────┐ ┌────────┐ │
│ │ LLM │◄──►│ Tools │ │ Memory │ │
│ │(brain) │ │ │◄──►│ /State │ │
│ └────────┘ └───────┘ └────────┘ │
└─────────────────────────────────────────┘The orchestration layer is the part that's easy to overlook in tutorials that focus mainly on prompting — but it's where validation, error handling, and safety controls actually live. See Agent Control Flow.
A Fifth, Easy-to-Forget Component: Guardrails
Production agents also need a validation/guardrails layer sitting between "LLM decided to call a tool" and "tool actually executes" — checking permissions, validating arguments, and catching unsafe requests before they run. See Agent Security.
Practical Use Case
When debugging an agent that's misbehaving, this breakdown tells you where to look: wrong decisions point to the LLM/prompting; a tool erroring out points to the tools layer; forgotten context across turns points to memory/state; and a runaway loop points to the orchestration layer's control flow.
Common Mistakes
- Building the LLM-decision part carefully while treating the orchestration layer as an afterthought — this is where most real-world reliability and safety issues actually originate
- Not clearly separating "memory" (what the agent should recall) from "state" (what the current run needs to track) — conflating them makes both harder to reason about (see Memory vs Context)
Interview Relevance
"What are the main components of an agent system, beyond just the LLM?" tests whether a candidate has actually built one, versus only having prompted a chatbot — the orchestration and guardrails layers are the parts beginners most often miss.
Practice Question
Sketch the four components for a coding agent that can read files, run tests, and propose code fixes.
