How to Build an AI Agent: An Architecture-Level Guide

Before you build

Four questions, answered honestly, save more rebuilds than any framework choice.

• What is the task, in one sentence? If you cannot describe the task in one sentence, the agent will inherit the ambiguity. Sharper task definition produces simpler, more reliable agents.
• What defines success? Concrete and measurable. “Resolves the user’s issue” is not a success criterion; “closes the ticket without a follow-up within 24 hours” is.
• What is the cost ceiling? Per-task, per-day, per-user. Unbounded budgets make every later decision harder.
• Who is responsible for production incidents? If no one owns the on-call rotation, the answer is “don’t deploy yet”.

Choose the pattern

Pattern follows from task. The patterns reference covers the design space; the short version:

• Tool-heavy task with parallelisable steps - multi-agent supervisor with parallel tool calls.
• Reasoning-heavy task with few tools - ReAct, possibly with reflection.
• Stable plan, slow tools, audit needed - plan-and-execute with re-plan on failure.
• High-stakes outputs needing review - any pattern with a reflection layer that gates the output.
• Simple Q&A with retrieval - this may not need an agent at all; a single LLM call with RAG may suffice.

Choose the model

Decision criteria, in order of weight for most agents:

1. Tool-use reliability on tasks similar to yours, sourced from BFCL, tau-bench, or task-specific benchmarks. The companion site benchmarkingagents.com tracks these.
2. Context-window size, when retrieved context will be large.
3. Cost per task, projected at production volume.
4. Latency per tool call, when user-facing.
5. Provider SLA and model-change communication, especially for production deployments.

Major model families practitioners use as of 2026, discussed as categories rather than rankings: Anthropic Claude (strong on tool-use reliability and structured output), OpenAI GPT (strong on the function-calling ecosystem and ecosystem maturity), Google Gemini (strong on long context and multimodal), Meta Llama (open-weight, self-hosted), Mistral (open-weight, European hosting), DeepSeek (open-weight, strong reasoning at lower cost). Pin the specific version in production.

Design the tool set

Tool design is where most agents succeed or fail in practice. Three principles recur.

• Granularity. Too few mega-tools (“do a thing”) make the agent guess at parameters. Too many tiny tools (“step 3 of process A”) overwhelm the selection model. Aim for tools that wrap a coherent unit of work.
• Idempotency. Tools that can be safely retried should be designed that way. Tools with side effects should be flagged in their description and gated behind human approval where appropriate.
• Authentication scope. The agent calls tools as a service principal; that principal should have only the permissions necessary. Scope down from the start; tightening permissions later is harder than starting tight.

Memory and state

Decide explicitly what state persists. Across steps within one task: typed state object. Across sessions for a user: long-term memory in a structured store. Across users: vector store of shared knowledge or RAG corpus. The memory reference covers the failure modes specific to each tier.

Observability from day one

Add tracing before you have any users, not after. The argument for instrumenting early is straightforward: when something is wrong in production, you want to read the trace tree, not guess. The cost is low; the major orchestration frameworks integrate cleanly with the major observability tools (see observability reference).

Evaluation from day one

Write the evaluation set before the agent. Ten to twenty representative tasks with known good outcomes. Run the eval on every prompt change, every model upgrade, every tool addition. The eval set will grow into the regression suite; the discipline of writing it early prevents shipping changes that quietly degrade quality.

See the evaluation tooling reference for frameworks that automate this.

Production-readiness checklist

Twenty items that distinguish a demo from a production deployment. Treat as a gate, not a wishlist.