AI Agent Architecture

AI agent architecture refers to how an AI agent's internal components are organised to enable it to perceive its environment, make decisions, and take actions. A well-designed architecture determines what an agent can do, how reliably it performs, and how easily it can be extended.

Core components of agent architecture

Every AI agent architecture includes some combination of these building blocks:

• Planning module: Breaks down a high-level goal into a sequence of steps. This might use chain-of-thought reasoning, task decomposition, or a dedicated planner model.
• Memory system: Stores information the agent needs to reference — conversation history (short-term), learned facts (long-term), and working notes for the current task.
• Tool interface: Connects the agent to external capabilities — APIs, databases, code execution, web search, or MCP servers.
• Decision engine: The core model (usually an LLM) that evaluates the current state and decides what to do next.
• Output handler: Formats the agent's actions and responses for the user or downstream systems.

Common architecture patterns

• Single-agent loop: One model handles everything — planning, reasoning, tool calling, and output. Simple but limited for complex tasks.
• Multi-agent system: Multiple specialised agents collaborate, each handling a different aspect of a task (research, writing, code review). Requires an orchestration layer.
• ReAct pattern: The agent alternates between reasoning about what to do and acting on that reasoning, creating an observable thought-action chain.
• Plan-then-execute: A planner agent creates a full task plan, then an executor agent carries it out step by step.

Design decisions that matter

When evaluating or building agent architectures, the key trade-offs include how much autonomy the agent has, how it handles errors and unexpected situations, how memory is managed across sessions, and how tool access is controlled and secured.

Practical implications

Understanding agent architecture helps you evaluate AI products that claim to be "agentic." A product with a simple single-loop architecture will behave very differently from one with multi-agent orchestration, even if the marketing sounds similar.