Build a Claude AI Agent: Step-by-Step for Startups

Reading about agent architecture is one thing; getting a Claude agent running against real user input is another. This is the hands-on companion: a sequenced build you can follow from an empty repo to something you would put behind a feature flag for early users. I will keep it concrete — what to create, in what order, and what tends to bite you at each step. The example agent is a support assistant that answers customer questions and can look up order data, because that shape generalizes to most startup needs.

Step 1: Scaffold the loop before anything clever

Start with the smallest thing that calls Claude and runs a tool. Create a project, add the Anthropic SDK, and write a single function that sends a message list to Claude and returns the response. Resist the urge to add tools, memory, or a framework yet. You want to confirm credentials, model selection, and round-trip latency in isolation. Pick Sonnet 4.6 as your default model for development — it is the cost-versus-capability sweet spot — and keep Opus 4.8 in mind for steps that need heavier reasoning later.

Once a plain message round-trips, wrap it in the loop: send messages, inspect the response for tool_use blocks, and if there are none, return the text. This skeleton is your whole agent. Everything from here is filling in tools and context, not rewriting structure. Verify the skeleton handles an empty tool list cleanly before moving on.

Step 2: Define your first tool with a tight schema

Add one tool: get_order_status. Give it a clear name, a one-sentence description Claude can reason about, and a JSON input schema with a single required order_id string. Wire the harness so that when Claude emits a tool_use for it, your code calls a stub that returns a fixed fake order, appends the result as a tool_result, and re-enters the loop. Test that Claude calls the tool when asked "where is order 1234" and answers directly when asked "what are your hours."

The schema is doing real work here. A vague description like "gets order info" leads Claude to call the tool at the wrong moments; a precise one — "Look up the current shipping status of a customer order by its numeric order ID" — sharply improves when it fires. Treat tool descriptions as prompt engineering, because they are.

flowchart TD
  A["Scaffold bare loop"] --> B["Add one tool with tight schema"]
  B --> C["Swap stub for MCP server"]
  C --> D["Add system prompt & guardrails"]
  D --> E["Add compaction & turn cap"]
  E --> F{"Eval suite green?"}
  F -->|No| D
  F -->|Yes| G["Ship behind feature flag"]

Step 3: Replace the stub with a real MCP server

Now connect to real data. Rather than hand-rolling database access inside the tool, stand up an MCP server that exposes your order system, or point at an existing one. Your agent connects to the server, and its tools — get_order_status, list_recent_orders — appear in Claude's tool list automatically. The win is that the same MCP server backs every future agent you build, and the integration is declarative rather than copy-pasted.

At this step, error handling stops being optional. Real lookups fail: the order does not exist, the database times out, the ID is malformed. Return these as structured tool results — { "error": "not_found", "order_id": "1234" } — rather than throwing. Claude reads the error and recovers gracefully, asking the user to re-check the ID instead of hallucinating a status. An agent that never sees its own failures cannot reason about them.

Step 4: Write the system prompt and guardrails

With tools working, shape behavior. The system prompt sets the agent's role, tone, boundaries, and escalation rules: who it is, what it must never do, and when to hand off to a human. Keep it specific and operational — "If a customer asks for a refund, do not promise one; collect the order ID and say a teammate will follow up" beats vague instructions to "be helpful." State the tools' purpose in the prompt too, so Claude has a mental model of its own capabilities.

Guardrails live in your harness, not just the prompt. Validate tool inputs before executing them, enforce allow-lists on any write actions, and add a turn limit so a confused agent cannot loop forever. The prompt is a strong suggestion; the harness is the enforced contract. Security-sensitive decisions — can this user see this order? — belong in code, where they cannot be talked around by a clever input.

Step 5: Add memory, compaction, and a turn cap

Make it survive long conversations. Track the message list across turns so the agent remembers the current thread. When that list grows past a threshold, compact it: summarize older turns into a short synopsis that preserves decisions and the open question, then drop the raw history. This keeps token cost flat and reasoning sharp on long support chats. Pair compaction with a hard turn cap so a stuck agent exits with a clear handoff message instead of grinding.

For returning users, persist a short episodic summary keyed to their account and reload it at the start of the next session. This is the difference between an agent that feels like a goldfish and one that remembers the customer mentioned a damaged package yesterday. Store the durable facts in your database; reload only the distilled summary into context.

Step 6: Evaluate, then ship behind a flag

Before users touch it, build a small eval set: fifteen to thirty real-ish transcripts with expected behaviors — "asks for order ID," "refuses to promise a refund," "escalates on legal threats." Run the agent against them on every change and gate deploys on the results. This is your regression net; without it, fixing one behavior silently breaks another. Evals do not need to be fancy — a script that checks for the right tool calls and refusal patterns catches most regressions.

Ship to a small cohort behind a feature flag, log every transcript, and watch the first hundred real conversations closely. Real users will phrase things your evals never imagined, and those transcripts become your next eval cases. Expand the cohort as the failure rate drops. The loop from production transcript to eval case to fix is the engine that turns a demo into a dependable product.

Frequently asked questions

How long does a first working agent take?

The bare loop plus one tool is an afternoon. A version you would put behind a flag — real MCP-backed tools, guardrails, compaction, a small eval set — is typically a week or two for one engineer. The architecture is simple; the reliability work is where the time goes.

Should I start with the Claude Agent SDK or raw API?

Build the bare loop on the raw messages API once so you understand it, then adopt the Claude Agent SDK for anything real. The SDK gives you tested tool execution, subagents, MCP, and hooks so you are not maintaining your own harness.

What model should I use during development?

Default to Sonnet 4.6 for its balance of cost and capability. Drop to Haiku 4.5 for cheap, frequent classification-style steps and reserve Opus 4.8 for the hardest reasoning. You can mix models across steps in one agent.

How do I stop the agent from making things up?

Give it tools to fetch real data and a system prompt that says to use them rather than guess, return tool errors as structured results so it can recover, and add evals that catch fabrication. Grounding in tool results is the single biggest lever against hallucination.

Bringing agentic AI to your phone lines

The same build sequence — loop, tools, MCP, guardrails, evals — is how CallSphere ships voice and chat agents that answer every call and message, pull up records mid-conversation, and book jobs day and night. Watch one work 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.