Designing the Architecture of an AI Agent

Building an AI agent is very different from building a simple application that sends a prompt to a language model and displays the response.

A useful agent needs to do more than generate text. It needs to interpret a goal, decide what should happen next, use tools when necessary, maintain enough state to complete the task, observe the result, and recover when something goes wrong.

That makes the architecture of an agent one of the most important engineering decisions in the entire system.

From Chatbot to Agent

A conventional LLM application often follows a simple flow:

User
  │
  ▼
Prompt
  │
  ▼
Language Model
  │
  ▼
Response

An agent introduces a control loop:

User Goal
   │
   ▼
Agent
   │
   ├── Reason
   │
   ├── Select Tool
   │
   ├── Execute Tool
   │
   ├── Observe Result
   │
   └── Continue or Finish
           │
           ▼
        Response

The difference is important.

The model is no longer simply producing an answer. It becomes one component inside a larger software system.

The Core Components

A practical AI agent can be separated into several responsibilities.

Input Layer

The input layer receives the user’s request and establishes the initial context.

It should be responsible for:

validating input;
normalizing requests;
attaching relevant context;
establishing execution limits;
creating an initial task state.

Keeping this responsibility separate makes the rest of the system easier to reason about.

Orchestrator

The orchestrator controls the agent’s execution lifecycle.

It determines:

what the agent currently knows;
what action should happen next;
which tool should be invoked;
whether the result is sufficient;
whether another iteration is necessary;
when execution should terminate.

This layer is effectively the runtime of the agent.

Model Layer

The language model provides reasoning and generation capabilities.

However, the model should not own the entire application.

The surrounding system should control:

available tools;
execution limits;
state;
validation;
retries;
error handling;
termination conditions.

This separation is important because language models are probabilistic while application infrastructure should remain deterministic wherever possible.

Tool Layer

Tools allow the agent to interact with the outside world.

Examples include:

filesystem operations;
HTTP requests;
database queries;
shell commands;
search;
code execution;
APIs.

Each tool should expose a clear contract.

A tool should define what it accepts, what it returns, and which failures are possible.

State Is Part of the Architecture

An agent without state quickly becomes difficult to control.

State can include:

the original objective;
conversation history;
previous tool calls;
tool results;
intermediate decisions;
execution metadata;
errors;
remaining steps.

A useful mental model is:

Agent State
├── Goal
├── Context
├── History
├── Tool Results
├── Current Step
├── Errors
└── Execution Limits

The state should be explicit rather than hidden across unrelated components.

The Agent Loop

The central execution loop can be represented conceptually as:

while not finished:
    decision = model.decide(state)

    if decision.requires_tool:
        result = tool.execute(decision.tool_call)
        state.add(result)
    else:
        response = decision.response
        finished = True

The exact implementation can vary, but the architectural principle remains the same.

The model proposes an action.

The application executes that action.

The result is returned to the model.

The cycle continues until a defined termination condition is reached.

Guardrails Belong Outside the Model

One of the easiest mistakes when designing an agent is assuming that the model can enforce all operational rules itself.

It should not.

Application-level controls should enforce:

maximum iterations;
maximum tool calls;
execution timeouts;
allowed tools;
input validation;
output validation;
resource limits.

For example, an agent should never be allowed to continue indefinitely simply because the model keeps requesting additional actions.

Maximum Steps
      │
      ▼
Agent Loop ──────► Terminate
      │
      ▼
Tool Execution
      │
      ▼
Timeout / Validation

These controls transform an open-ended reasoning process into a bounded software operation.

Designing for Failure

Agents operate across multiple failure boundaries.

A model call can fail.

A tool can fail.

A network request can time out.

A returned payload can be malformed.

An external service can become unavailable.

A robust architecture treats these failures as normal execution states rather than exceptional surprises.

The system should distinguish between:

retryable failures;
validation failures;
tool failures;
permanent failures;
user-correctable errors.

This makes recovery predictable.

Why Separation of Concerns Matters

An AI agent can become complicated very quickly if model interaction, business logic, tool execution, state management, and presentation are all placed in the same module.

A cleaner architecture separates them:

Interface
   │
   ▼
Agent Runtime
   │
   ├── State
   ├── Model
   ├── Tool Registry
   ├── Validation
   └── Execution Policy
            │
            ▼
          Tools

This makes individual components easier to test and replace.

The model can change without rewriting the tool system.

A tool can change without redesigning the orchestration layer.

The user interface can change without affecting the agent runtime.

That is the kind of modularity that becomes valuable as an experimental AI project evolves into a real application.

Final Thoughts

The most important lesson when building an AI agent is that the language model is only one part of the system.

The engineering challenge lies in everything surrounding it.

A dependable agent needs explicit state, controlled tool execution, bounded loops, validation, failure handling, and a clear architecture.

The goal is not simply to make an agent appear intelligent.

The goal is to build a system in which intelligence can operate reliably.

Designing the Architecture of an AI Agent