AI Agent Architecture: The Five Parts Every Agent Needs
An AI agent's architecture has five parts: a model that decides, instructions that define its job, tools it can call to act, memory that carries facts between steps or sessions, and a control loop that runs the model, executes tool calls and decides when to stop. Production agents add guardrails around the loop: step limits, spend caps, permission checks and output validation.
Search for AI agent architecture and you will find diagrams with twenty boxes. In practice, every agent I have built and shipped has the same five parts. The diagrams differ in how much detail they draw around them.
1. The model
The model reads the current context and decides what to do next: answer, ask, or call a tool. Pick it per agent, not per company. A routing or intake agent can run on a small fast model. A planning agent that has to hold a long task in mind needs a larger one.
2. The instructions
The system prompt defines one job, the rules for doing it, and the format of the result. Keep it short and stable. A stable prompt can be cached, and a short one leaves room for the context that actually changes. If the instructions cover five unrelated jobs, you have five agents squeezed into one.
3. The tools
Tools are how the agent touches the world: search a knowledge base, read a record, write a draft, send a message. Each tool needs a clear name, a description of when to use it, and a typed input schema. Code validates every argument before the tool runs. The agent should get the narrowest set of tools that lets it finish its job. An agent that only drafts should not hold the send tool.
4. Memory
Memory comes in two kinds. Working memory is the context of the current run: the conversation so far and the results of tool calls. Long-term memory is what survives between runs: facts about a customer, a brand or past decisions. Long-term memory should be stored as small structured facts and loaded as a short summary, never as old transcripts.
5. The control loop
The loop is ordinary code. It sends context to the model, reads the response, runs any requested tool, appends the result, and repeats until the model says it is done or a limit is hit. This is where production safety lives:
- Step limit. A maximum number of turns per run, so a confused agent cannot loop forever.
- Spend cap. A token or cost ceiling per run and per day.
- Permission checks. Code decides whether a tool call is allowed, not the model.
- Output validation. The final result is checked against a schema before anything uses it.
- Human approval. Irreversible actions, such as sending money or email to a customer, can pause for sign-off.
Drawing the architecture
If you need a diagram for a client or a team, draw the loop in the middle with the model inside it, tools on one side, memory on the other, and the guardrails as gates on the arrows. Then draw the input arriving and the validated output leaving. That picture explains the agent better than a twenty-box chart.
How agents connect to each other
When one agent is not enough, the five parts stay the same for each agent and the design question moves to the boundaries: what each agent hands the next. That is covered in agentic AI system design. For the surrounding application, see LLM system design.
A worked example: a booking agent
A voice or chat agent that books appointments shows all five parts clearly.
| Part | In the booking agent |
|---|---|
| Model | A fast mid-size model, because each turn has to reply quickly. |
| Instructions | Book, move or cancel appointments; collect name, phone, service and time; never quote prices that are not in the knowledge base. |
| Tools | Check availability, create booking, look up services. No tool to delete customer records. |
| Memory | Working memory of this call; long-term facts such as the caller's usual service and preferred times. |
| Control loop | Maximum turns per call, a timeout on each tool, a transfer to a person on repeated failure or explicit request. |
When a booking fails, this structure tells you where to look: did the model choose the wrong tool, did the tool return an error, or did the loop transfer too early?
The part people skip
Teams spend most of their time on instructions and almost none on the control loop. But the loop decides what happens on the bad days: the calendar API is down, the caller changes their mind three times, the model asks for a tool that does not exist. Write the loop's rules first: how many turns, how many retries, when to hand over, what to log.
Agents I run
The self-selling conversational AI bot on my systems page follows this exact shape: knowledge built from the business's website, tools for booking and qualification, escalation detection with a human hand-off, and CRM sync after every conversation. For the build-side view of what agent projects involve, see AI agent developer: what gets built, and what breaks first.
Types of agent architecture in AI
Textbooks describe agent architectures by how they decide: reactive agents that respond directly to input, deliberative agents that plan, and hybrid or layered agents that combine both. Modern language model agents map onto these ideas. A single model call with tools is close to reactive. A plan-and-execute agent is deliberative. Most production systems are hybrid: fast reactive handling for simple requests, with planning only where the task needs it.
Checklist before you ship an agent
- The instructions describe one job and fit on one screen.
- Every tool has a typed schema and a permission check in code.
- Long-term memory is structured facts, not transcripts.
- The loop has a step limit, a spend cap and a timeout per tool.
- The final output is validated before anything uses it.
- Irreversible actions pause for approval at first.
- Every run is logged with tokens, cost and tool calls.
Frequently asked questions
What is AI agent architecture?
AI agent architecture is the structure of an AI agent: a model that makes decisions, instructions that define its job, tools it can call, memory that carries information between steps or sessions, and a control loop that runs the model and tools with limits and checks.
What are the components of an AI agent?
The model, the system instructions, the tools, memory (working and long-term), and the control loop. Production agents add guardrails to the loop: step limits, spend caps, permission checks, output validation and human approval for risky actions.
What is the difference between an AI agent and a workflow?
In a workflow, code decides the order of steps in advance. In an agent, the model decides the next step at run time, choosing which tool to call. Many good systems mix both: a fixed workflow with an agent inside one step.
What is the control loop in an AI agent?
The control loop is the code that repeatedly sends context to the model, runs any tool the model requests, adds the result to the context and decides when to stop, enforcing limits on steps, time and spend.
Do AI agents need long-term memory?
Only if they serve the same person or account more than once. Then store small structured facts and load a short summary at the start of each session, rather than replaying past conversations.
What are the types of agent architecture in AI?
Classic categories are reactive, deliberative and hybrid or layered architectures. Modern LLM agents map onto them: simple tool-calling agents are close to reactive, plan-and-execute agents are deliberative, and most production systems are hybrids.
AI developer. I design and build AI agents, AI automation, CRM architecture and the web apps that tie them together. Five of my systems run in production, and one has been acquired. About me · My systems · LinkedIn · GitHub
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