Building Agentic AI Apps With Claude: Tools, Loops, and Production Patterns

Most AI apps are wrappers: user sends a message, LLM responds, done. Agentic apps are different -- the AI takes a sequence of actions, uses tools, and produces a result the user couldn't get from a single prompt.

Here's how to build one that actually works in production.

What Makes an App "Agentic"

An agentic app has at least three properties:

1. Tool use: The AI can call external functions (search, read files, make API calls)
2. Multi-step execution: The AI decides what to do next based on previous results
3. Goal-directed: The AI is working toward an outcome, not just answering a question

A simple example: "Research the top 5 competitors for my SaaS product and summarize their pricing."

Non-agentic: User manually searches each competitor, pastes pricing into a doc, asks Claude to summarize.

Agentic: Claude searches each competitor, extracts pricing from each page, compares them, writes the summary.

The Core Loop

Every agentic app runs the same basic loop:

1. Receive goal from user
2. Plan next action based on goal + history
3. Execute action (call tool)
4. Observe result
5. If goal achieved: return result
6. Else: go to step 2

In code:

async function agentLoop(goal: string, tools: Tool[], maxSteps = 10) {
  const messages: Message[] = [{ role: "user", content: goal }]

  for (let step = 0; step < maxSteps; step++) {
    const response = await claude.messages.create({
      model: "claude-opus-4-6",
      max_tokens: 4096,
      tools,
      messages,
    })

    // Check if done
    if (response.stop_reason === "end_turn") {
      const text = response.content.find(b => b.type === "text")
      return text?.text ?? "Done"
    }

    // Execute tool calls
    if (response.stop_reason === "tool_use") {
      messages.push({ role: "assistant", content: response.content })

      const toolResults = await executeTools(response.content, tools)
      messages.push({ role: "user", content: toolResults })
      continue
    }

    break
  }

  return "Max steps reached"
}

Defining Tools

Tools are the agent's hands. Define them clearly -- the description is what Claude uses to decide when and how to call them.

const tools = [
  {
    name: "search_web",
    description: "Search the web for current information. Use for: competitor research, pricing lookups, recent news, documentation.",
    input_schema: {
      type: "object",
      properties: {
        query: { type: "string", description: "Search query" },
        num_results: { type: "number", description: "Number of results (1-10)", default: 5 },
      },
      required: ["query"],
    },
  },
  {
    name: "read_url",
    description: "Fetch and read the content of a specific URL. Use after search_web to get full page content.",
    input_schema: {
      type: "object",
      properties: {
        url: { type: "string", description: "URL to fetch" },
      },
      required: ["url"],
    },
  },
  {
    name: "write_file",
    description: "Write content to a file. Use to save research results, reports, or data.",
    input_schema: {
      type: "object",
      properties: {
        filename: { type: "string" },
        content: { type: "string" },
      },
      required: ["filename", "content"],
    },
  },
]

Executing Tool Calls

async function executeTools(
  content: ContentBlock[],
  tools: Tool[]
): Promise<ToolResultBlock[]> {
  const results: ToolResultBlock[] = []

  for (const block of content) {
    if (block.type !== "tool_use") continue

    let result: string
    try {
      result = await callTool(block.name, block.input)
    } catch (err) {
      result = `Error: ${err instanceof Error ? err.message : "Unknown error"}`
    }

    results.push({
      type: "tool_result",
      tool_use_id: block.id,
      content: result,
    })
  }

  return results
}

async function callTool(name: string, input: Record<string, unknown>): Promise<string> {
  switch (name) {
    case "search_web":
      return await searchWeb(input.query as string, input.num_results as number)
    case "read_url":
      return await fetchUrl(input.url as string)
    case "write_file":
      await fs.writeFile(input.filename as string, input.content as string)
      return `Written to ${input.filename}`
    default:
      throw new Error(`Unknown tool: ${name}`)
  }
}

Controlling the Agent

Unconstrained agents are unpredictable. Add guardrails:

Step Limits

const MAX_STEPS = 15  // Prevents infinite loops

Tool Allowlists

const SAFE_TOOLS = new Set(["search_web", "read_url", "calculate"])
const DESTRUCTIVE_TOOLS = new Set(["write_file", "delete_file", "send_email"])

async function callTool(name: string, input: unknown, requireConfirmation = false) {
  if (DESTRUCTIVE_TOOLS.has(name) && requireConfirmation) {
    const confirmed = await askUser(`Agent wants to ${name}. Allow?`)
    if (!confirmed) throw new Error("User denied tool call")
  }
  // execute...
}

Budget Tracking

let totalTokens = 0
const TOKEN_BUDGET = 100_000  // Stop before hitting limits

// After each API call:
totalTokens += response.usage.input_tokens + response.usage.output_tokens
if (totalTokens > TOKEN_BUDGET) {
  throw new Error("Token budget exceeded")
}

Streaming Agent Progress

Users don't want to stare at a spinner for 30 seconds. Stream status updates:

async function* streamAgentProgress(goal: string, tools: Tool[]) {
  yield { type: "start", message: "Starting research..." }

  for (let step = 0; step < MAX_STEPS; step++) {
    const response = await claude.messages.create({ ... })

    // Yield each tool use as it happens
    for (const block of response.content) {
      if (block.type === "tool_use") {
        yield { 
          type: "tool_call", 
          tool: block.name, 
          input: block.input 
        }

        const result = await callTool(block.name, block.input)
        yield { type: "tool_result", tool: block.name, preview: result.slice(0, 100) }
      }

      if (block.type === "text" && block.text) {
        yield { type: "thinking", message: block.text.slice(0, 200) }
      }
    }

    if (response.stop_reason === "end_turn") {
      yield { type: "complete", result: extractResult(response) }
      return
    }
  }
}

Frontend:

const stream = await fetch("/api/agent", { method: "POST", body: JSON.stringify({ goal }) })
const reader = stream.body!.getReader()

for await (const chunk of readStream(reader)) {
  const event = JSON.parse(chunk)

  switch (event.type) {
    case "tool_call":
      addStep(`Calling ${event.tool}...`)
      break
    case "tool_result":
      updateStep(`Got result from ${event.tool}`)
      break
    case "complete":
      showResult(event.result)
      break
  }
}

Common Failure Modes

Infinite loops: Agent keeps calling tools without making progress. Fix: step limits + detect repeated tool calls with same inputs.

Hallucinated tool results: Agent references results it didn't actually get. Fix: always return tool results immediately, never let Claude "assume" what a tool returned.

Context overflow: Long agent runs accumulate too much history. Fix: summarize old messages when approaching token limits.

Tool errors cascading: One tool failure derails the whole task. Fix: catch errors in callTool, return the error as a string result so Claude can adapt.

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The Claude API setup for agentic apps -- with streaming, tool execution, and error handling -- is pre-wired in the AI SaaS Starter Kit.

AI SaaS Starter Kit ($99) ->

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