Guides

How to build an AI agent in 2026: the practical stack

aaaa.fyi · 2026-07-22 · 8 min read

Every agent, from a weekend script to a production system, is the same four pieces:

A model — the reasoning engine (Claude, GPT, or a local model).

A loop — call the model, execute what it asks, feed results back, repeat until done.

Tools — what the agent can actually do: run code, browse, query a database. In 2026 this layer is standardized: MCP servers give any agent hands without custom integration.

Memory/context — what the agent knows: the conversation, retrieved documents, project files.

Assemble first, build second

The build-from-scratch loop is ~50 lines and worth writing once to understand the shape. For real work, start from an existing agent and extend it:

Extend a coding agent (Claude Code, Codex CLI, Cline) with skills and MCP servers — zero infrastructure, production-grade loop for free.

Assemble with a framework when you need a custom product: the agent frameworks with real momentum ship the loop, tool-calling, retries and tracing so you write only domain logic.

Go bare-metal only when the framework's abstractions fight your use case.

The decisions that actually matter

Tool design beats prompt design. Agents fail mostly by calling the wrong tool with the wrong arguments. Few, well-described, typed tools with data-shaped errors ("branch not found", not a stack trace) fix more failures than any system-prompt tweak.

Bound the loop. Max iterations, budget caps, and a definition of "done" — an unbounded agent loop is an unbounded bill.

Checkpoint the risky steps. Generate → verify → apply, with a stricter model or a human between steps for anything irreversible.

Where to start

Study what's working: our agents directory ranks every open-source agent and framework by real GitHub activity — the fastest way to see which architectures are winning is to read the code of the ones people actually use.