Build your first Claude agent: a step-by-step guide

Reading about agent architecture is one thing; sitting down on a Monday and shipping a working Claude agent by Friday is another. This post is the second kind. It is a step-by-step implementation walkthrough a single engineer can follow to take an AI-native product from empty repository to an agent that handles real tasks end to end. I will keep the moving parts honest — where the sharp edges are, what to build first, and what to defer until you actually need it. No hand-waving past the parts that bite.

Step 0: Decide the one task before you write any code

Before touching a keyboard, write down one sentence: the single task your agent must do reliably. "Answer billing questions and issue refunds up to $50" is a buildable task. "Be a helpful assistant" is not — it has no edges, so you can never tell if it works. The narrower the first task, the faster you will get to something real, and the easier every later step becomes. AI-native products grow by adding well-defined capabilities to a working core, not by launching a vague everything-bot and hoping.

With the task fixed, list the tools it implies. A billing agent needs to look up an account, read recent charges, and issue a refund. Those three verbs become your first three tools. Already you can see the shape of the whole build: a loop that lets Claude reason, plus a small set of well-described actions it can take in your systems.

Step 1: Stand up the agent loop

The heart of any Claude agent is the loop: send context to the model, check whether it wants to call a tool, execute the tool, feed the result back, and repeat until the model returns a final answer instead of a tool call. You can write this from scratch against the messages API, but the faster path for a founder is the Claude Agent SDK, which gives you the same harness that runs Claude Code — turn management, tool dispatch, and context handling already solved. Either way the conceptual loop is identical, and you should be able to picture it precisely.

flowchart TD
  A["Define task + tool schemas"] --> B["Send messages to Claude"]
  B --> C{"stop_reason == tool_use?"}
  C -->|Yes| D["Run the requested tool"]
  D --> E["Append tool_result to messages"]
  E --> B
  C -->|No| F["Return final text to user"]
  F --> G["Log full trace for evals"]

The detail engineers miss on the first build is the message bookkeeping. When Claude returns a tool_use block, you must append the assistant message containing it, run the tool, and append a matching tool_result block with the same tool_use_id before calling the model again. Mismatch those and the API rejects the turn. Get this loop solid and watertight before adding anything else — everything later depends on it.

Step 2: Write three tools with strict schemas

Now define the tools. Each tool is a name, a clear description, and a JSON Schema for its inputs. The description is not documentation — it is part of the prompt. Claude decides whether and how to call a tool largely from its description, so write it the way you would brief a competent contractor: what it does, when to use it, what each parameter means, and any constraint that matters. For the refund tool, spell out the maximum amount and that it requires an account id, because the model will respect what you state plainly far better than what you leave implicit.

Keep the input schema tight. Use enums where the value set is fixed, mark required fields, and validate again on your side before executing — never trust the arguments blindly just because the model produced them. The first time the agent calls issue_refund with an amount of "fifty dollars" as a string instead of a number, you will be glad you validated rather than crashed in production.

Step 3: Connect real systems through MCP

Hard-coding tool implementations works for three tools; it does not scale to thirty. The cleaner path is the Model Context Protocol. MCP is an open standard that lets Claude connect to external tools and data through dedicated servers, so each integration lives behind a uniform interface instead of being stitched into your agent loop. Wrap your billing system in an MCP server that exposes get_account, list_charges, and issue_refund, and your agent talks to it the same way it would talk to any other server.

The practical win is separation of concerns. Your billing MCP server owns auth to the billing system, schema definitions, and error handling; your agent loop just orchestrates. When you later add a shipping tool, you write a new server, not new branches in your loop. Build the loop with hard-coded tools first to learn the mechanics, then migrate them behind MCP once the shape is stable — that ordering keeps your early days fast and your later days clean.

Step 4: Add memory and a system prompt that holds the line

With tools working, give the agent a spine. The system prompt sets its role, its boundaries, and its operating rules: who it is, what it must never do, how to behave when it lacks information. Be explicit about refusal and escalation — "if a refund exceeds $50, do not issue it; tell the user a human will follow up." Models honor concrete, stated rules far more reliably than vibes, so write the rules as if a literal-minded new hire will follow them word for word.

Memory comes next. For the first version, a single table keyed by user id holding durable facts and a short rolling summary is plenty. Read it during context assembly, write to it when the agent establishes something worth keeping. Resist building an elaborate memory architecture before you have evidence you need one — the simplest thing that survives a page refresh is the right starting point, and you can always promote it later.

Step 5: Build evals before you scale, not after

The final step is the one that makes all the previous ones safe to change. Collect twenty to forty real example tasks — including the nasty ones, like a customer demanding a $500 refund — and write graded expectations for each. Run them on every prompt or tool change. This eval set is your regression suite for behavior you cannot see in the source code, and it is the difference between iterating with confidence and shipping changes blind.

Wire observability in parallel: log the full trace of every run so that when something goes wrong you can replay exactly what the model saw and did. With the loop, tools, MCP, memory, and evals in place, you have not just a demo — you have the seed of an AI-native product you can grow week over week. Ship the narrow version, watch the traces, and let real usage tell you which capability to build next.

Frequently asked questions

How long should building a first Claude agent take?

A focused engineer can get a single-task agent with a few tools and basic memory working in a few days using the Agent SDK. The loop and tool plumbing are fast; the time goes into writing tight tool descriptions and a system prompt that behaves under edge cases.

Do I have to use MCP from the start?

No. Hard-code your first two or three tools to learn the loop, then migrate them behind MCP servers once the shape stabilizes. MCP pays off as your integration count grows; for a first prototype it can wait a step.

Which Claude model should I build against first?

Start with Sonnet 4.6 as the default — it handles most agent turns well at reasonable cost. Reach for Opus 4.8 on hard reasoning steps and drop to Haiku 4.5 for cheap high-volume calls once you understand your traffic.

What is the most common bug in a first build?

Message bookkeeping in the loop — failing to pair each tool_use with a matching tool_result using the same id, or appending them in the wrong order. Get the loop watertight before adding tools and you avoid most early pain.

Take the same build to voice and chat

CallSphere is this exact build pattern shipped for phone and chat: a Claude agent loop with strict tools, MCP integrations, memory, and evals — answering live calls 24/7. Watch the walkthrough come to life at callsphere.ai.

Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.