AI Agent Architecture.

• Model — the reasoning core that decides the next action. The most replaceable part of the stack.
• Tools — typed functions the agent can invoke. The agent's capability is bounded exactly by what its tools expose.
• Memory — working context for the current task plus, where needed, retrieval over your own content.
• Orchestration — the loop governing planning, retries, budgets, and termination.
• Guardrails — permissions, validation, approval gates, and logging.

For the full walkthrough of how these fit together conceptually, see what an AI agent is. This guide goes one layer deeper into how each is actually built.

• Single agent, single loop — one model, a fixed tool set, a step limit. The right default for most first deployments.
• Router plus specialists — a lightweight classifier routes each request to the right narrow agent, avoiding one agent trying to handle every case.
• Event-driven — the agent is triggered by a system event (a new ticket, a webhook) rather than a direct user request, common in operational and back-office workflows.

See multi-agent systems for coordinator-worker and pipeline patterns once a single agent's scope genuinely isn't enough.

• Keep working memory scoped to the current task, cleared or summarised between runs.
• Keep retrieval over your organisation's content separate from that working memory, queried on demand rather than loaded wholesale.

• Each tool should have a narrow, typed interface — the agent should not be able to do anything the tool doesn't explicitly expose.
• Validate tool inputs before execution and tool outputs before the agent treats them as fact.
• Design for partial failure — a tool call that times out or errors needs a defined fallback, not a crash.

• Trace every run end to end: input, each tool call, each intermediate decision, final output.
• Maintain a versioned regression suite of representative inputs and expected outputs, run automatically before any change ships.
• Track cost and latency per completed task, not per request — a task that needs three retries costs three times what the dashboard's per-call average suggests.