How to Build an AI Agent with Claude Code

Build an AI agent with Claude Code by wiring three parts: an agent loop that calls the model, executes returned tool calls, and feeds results back until the task ends; a set of typed tools the model can invoke; and a context strategy that keeps the prompt small. Add a retrieval tool so the agent searches indexed chunks before generating.

What you are actually building

An AI agent is not a bigger prompt. It is a control loop around a model that can take actions. Claude Code is that loop, already implemented: it reads your request, decides on a tool, runs it, observes the output, and decides again. Your job in this tutorial is to understand each moving part so you can extend it with your own tools and your own data.

Three parts do all the work. The loop drives execution. The tools give the model hands. The context decides what the model can see on each turn. Get these three right and the agent works. Get them wrong and it hallucinates actions, loops forever, or burns tokens re-deriving facts it already had. This guide walks each part with concrete mechanics, then adds a retrieval layer so the agent stops paying to regenerate answers it can look up.

Step 1: implement the agent loop

The loop is the spine. Every agent, including Claude Code, runs the same cycle. Send the conversation to the model. Read the response. If it contains a tool call, execute it, append the result as a tool message, and send again. If it contains a final answer, stop.

The five states of one turn

  1. Prompt: system instructions plus conversation history plus available tool schemas.
  2. Model call: the model returns either text or one or more tool-use blocks.
  3. Dispatch: your code maps each tool name to a function and runs it.
  4. Observe: capture stdout, return value, or error and format it as a tool-result message.
  5. Loop or halt: append results, resend, and repeat until the model emits no tool call or a max-iteration guard trips.

The halt condition matters. Without a max-iteration cap and a clear success signal, an agent can thrash on the same failing command. Claude Code enforces this for you; if you build your own harness, add both.

Step 2: define tools the model can trust

A tool is a typed function the model can call. It has a name, a description, and a JSON schema for its inputs. The model never runs code; it emits a structured request, and your dispatcher runs the real function. Quality of tool design decides how often the model picks correctly.

Keep each tool narrow. A read_file(path) and a search(query) beat one do_stuff(action, args) because the schema itself teaches the model what is possible. Write the description as an instruction, not a label: "Search indexed project chunks and return the top matches with file paths" tells the model when to reach for it. In Claude Code you register custom tools through an MCP server, which exposes your functions to the agent over a standard protocol without touching the loop itself.

Step 3: control the context window

The failure mode of long-running agents is context, not intelligence. Every turn appends more history, and once the window fills, the agent loses the thread or the cost climbs. You manage this actively: prune stale tool outputs, summarize completed sub-tasks into a short note, and inject only the chunks relevant to the current step.

The principle: the model should see what it needs for this decision, nothing more. A 200-line file dump that mattered ten turns ago is dead weight now. Replace it with a one-line summary. This is where retrieval changes the shape of the agent: instead of stuffing everything into context and hoping, the agent fetches the exact chunk it needs, when it needs it.

Step 4: add a retrieval layer with RDK

Here is the upgrade that separates a demo agent from a working one. Give the agent a retrieval tool backed by RDK, the Retrieval Development Kit. You index your local vault, docs, or codebase as encrypted private chunks on the RDK network. The agent then calls a retrieve(query) tool that searches those chunks before it ever generates.

How stacked retrieval routes a query

The retrieval tool answers in layers. Your private vault chunks resolve 40 to 65 percent of queries directly. The public RDK network adds another 15 to 20 percent from chunks other builders have published. The LLM handles only the remaining 5 to 10 percent as fallback generation. The agent still uses Claude for reasoning and synthesis; it just stops regenerating facts it can retrieve.

Wiring it in is one more tool in Step 2. The retrieval call becomes the agent's first move on any factual step, and generation becomes the exception. That is the mechanism behind the 80 to 90 percent drop in token spend: the answer is looked up instead of re-derived on every run.

Publishing chunks other agents can use

Retrieval is bidirectional on RDK. Chunks you mark public are retrievable by other builders' agents, and you earn USDC per retrieval on the Base network via the CryptoCadet rail. One well-structured work product then serves many agents instead of forcing a million identical inference calls. For a build guide the takeaway is simple: design your chunks to be reusable and the retrieval layer becomes an asset, not just a cache.

Putting it together

Start with Claude Code's built-in loop rather than rewriting it. Add your domain tools through an MCP server, each with a tight schema and an instructional description. Manage context deliberately: summarize, prune, inject. Then register a retrieval tool so the agent searches indexed chunks first and calls the model only for the residual. The result is an agent that finishes tasks, stays coherent over long runs, and does not pay twice for the same knowledge.

Frequently asked questions

Do I need to write my own agent loop, or does Claude Code provide one?
Claude Code ships the loop already: it calls the model, executes tool calls, feeds results back, and halts on completion or an iteration cap. You extend it with custom tools through an MCP server rather than rebuilding the harness. Understanding the loop still matters so you know why an agent stalls or thrashes.
How do I add my own tools to a Claude Code agent?
Expose them through an MCP server. Each tool needs a name, a JSON input schema, and an instructional description telling the model when to use it. Keep tools narrow and single-purpose so the schema itself teaches the model what is possible. The agent loop discovers and calls them without any changes to its core.
Why add a retrieval layer instead of just using a bigger context window?
A bigger window still pays to process every token on every turn, and it degrades coherence as history grows. A retrieval tool injects only the chunks relevant to the current step. With RDK, private vault chunks answer 40 to 65 percent of queries directly, cutting token spend 80 to 90 percent while keeping context small and focused.
What stops the agent from looping forever?
A halt condition. Set a maximum iteration count and a clear success signal so the loop ends when the task is done or the cap trips. Without both, an agent can retry a failing command indefinitely. Claude Code enforces these guards; a custom harness must add them explicitly.