A Beginner's Guide to Building Your First AI Agent with Apple Intelligence APIs After WWDC 2026

If you watched the WWDC 2026 keynote with your jaw on the floor, you were not alone. Apple's announcements around iOS 27 and the redesigned Siri architecture marked a genuine turning point for mobile developers. For the first time, building a capable, on-device AI agent is no longer a privilege reserved for machine learning PhDs or teams with massive cloud compute budgets. With Apple Intelligence APIs now fully open to third-party developers, the playing field has shifted dramatically in your favor.

This guide is written for developers who are just getting started. Maybe you have shipped a few Swift apps before. Maybe you have been curious about AI but felt intimidated by the complexity. Either way, by the end of this post, you will understand what the new Siri architecture actually means in practice, which APIs you should learn first, and how to wire together your very first agentic feature inside an iOS 27 app.

What Changed at WWDC 2026: The Big Picture

Before writing a single line of code, it helps to understand what Apple actually announced and why it matters. The headline feature was the Siri Agent Runtime, a new execution layer baked into iOS 27 that allows apps to register as "agent providers." This is a fundamental architectural shift from the old SiriKit model, which was essentially a rigid slot-filling system where Siri matched user phrases to predefined intents.

The new model is different in three important ways:

• Reasoning on device: iOS 27 ships with an upgraded on-device foundation model (Apple calls it the Adaptive Foundation Model, or AFM) that is capable of multi-step reasoning without sending data to the cloud. Your user's data stays on their phone.
• Tool use and chaining: The Siri Agent Runtime supports tool-calling natively. Your app can expose "tools" (discrete functions) that Siri can discover, select, and invoke in sequence to complete a goal.
• Context persistence: Agents can now maintain a session context across multiple turns of a conversation, which is what makes genuinely agentic behavior possible. Earlier versions of Siri had no real memory between requests.

For beginners, the most important takeaway is this: you do not need to train a model. Apple supplies the intelligence. Your job is to describe what your app can do, and then let the runtime figure out how to use it.

Understanding the Core Concepts: Agents, Tools, and Intents

The vocabulary around AI agents can be confusing, so let's ground it in Apple's specific framework before going any further.

What Is an AI Agent, Really?

An AI agent is a system that perceives a goal, breaks it into steps, uses available tools to execute those steps, and adapts when something goes wrong. The classic example is a travel assistant: a user says "Book me the cheapest flight to Tokyo next month and add it to my calendar." A traditional app would require the user to navigate multiple screens. An agent figures out the steps autonomously and executes them on the user's behalf.

Apple's Three-Layer Model

iOS 27's Apple Intelligence framework organizes agentic development into three layers that every beginner should memorize:

• AgentIntent: The high-level goal expressed by the user. This replaces and extends the old INIntent system. You define AgentIntents using a new Swift macro called @AgentIntent.
• AgentTool: A discrete capability your app exposes. Think of tools as functions the model can call. Each tool has a name, a plain-language description, typed parameters, and a return type. The description is critical because the on-device model reads it to decide when to invoke the tool.
• AgentSession: The runtime object that manages context, turn history, and tool orchestration for a single user interaction. You hold a reference to this and use it to stream responses back to your UI.

Setting Up Your Development Environment

Getting started requires a few specific things in place. Here is the checklist:

• Xcode 18 or later, which ships with the iOS 27 SDK and the new AgentKit framework headers.
• A physical iPhone 17 or later (or an iPhone 16 Pro running the iOS 27 beta) for on-device model testing. The Simulator can run your code but will mock the AFM responses, which is useful for UI testing but not for tuning agent behavior.
• An Apple Developer Program membership with the Apple Intelligence entitlement enabled in your App ID. You request this in the Developer Portal under "Capabilities."
• The AgentKit framework imported in your Swift package or project target.

Once those are in place, add the entitlement key com.apple.developer.apple-intelligence.agent-provider to your .entitlements file. Xcode 18 will prompt you to do this automatically when you import AgentKit for the first time.

Building Your First Agent: A Step-by-Step Walkthrough

Let's build something concrete: a simple Task Manager Agent that can add tasks, list pending tasks, and mark tasks as complete, all through natural language. It is a humble example, but it covers every concept you need to go further.

Step 1: Define Your AgentTools

Create a new Swift file called TaskAgentTools.swift. Each tool is a struct conforming to the AgentTool protocol. The most important part is the description property. Write it as if you are explaining the function to a helpful but literal-minded colleague.

import AgentKit

struct AddTaskTool: AgentTool {
    static let name = "add_task"
    static let description = """
        Adds a new task to the user's task list.
        Use this when the user wants to create, add, or remember a to-do item.
        """

    struct Parameters: Codable {
        let title: String
        let dueDate: Date?
    }

    struct Result: Codable {
        let taskID: String
        let success: Bool
    }

    func invoke(parameters: Parameters) async throws -> Result {
        let id = TaskStore.shared.add(title: parameters.title, due: parameters.dueDate)
        return Result(taskID: id, success: true)
    }
}

Repeat this pattern for ListTasksTool and CompleteTaskTool. Keep each tool focused on one action. Smaller, well-described tools give the on-device model more precision when planning.

Step 2: Register Your AgentIntent

Next, declare the intent that groups your tools together. This is what tells Siri that your app is capable of handling task-related goals.

import AgentKit

@AgentIntent(
    displayName: "Manage My Tasks",
    description: "Helps the user add, view, and complete tasks using natural language.",
    tools: [AddTaskTool.self, ListTasksTool.self, CompleteTaskTool.self]
)
struct TaskManagerIntent { }

The @AgentIntent macro does a lot of heavy lifting behind the scenes. It generates the necessary metadata that the Siri Agent Runtime reads during app installation, so Siri knows your app exists as an agent provider even before the user opens it.

Step 3: Create and Drive an AgentSession

Now wire everything into your SwiftUI view. The AgentSession object is your main interface to the runtime.

import SwiftUI
import AgentKit

struct TaskChatView: View {
    @State private var session = AgentSession(intent: TaskManagerIntent.self)
    @State private var messages: [ChatMessage] = []
    @State private var inputText = ""

    var body: some View {
        VStack {
            ScrollView {
                ForEach(messages) { message in
                    MessageBubble(message: message)
                }
            }
            HStack {
                TextField("Ask about your tasks...", text: $inputText)
                Button("Send") {
                    Task { await sendMessage() }
                }
            }
            .padding()
        }
    }

    func sendMessage() async {
        let userMessage = inputText
        inputText = ""
        messages.append(.user(userMessage))

        for await chunk in session.send(userMessage) {
            // Stream the response token by token
            messages.appendOrUpdate(.agent(chunk))
        }
    }
}

Notice the for await loop. The AgentSession.send(_:) method returns an AsyncStream, which means responses stream back to your UI in real time, just like the experience users expect from modern AI interfaces.

Common Beginner Mistakes (And How to Avoid Them)

After talking with developers in the early beta period, a few patterns keep coming up as stumbling blocks. Here are the most common ones:

Writing Vague Tool Descriptions

This is the single biggest mistake beginners make. The on-device model selects tools based entirely on your description strings. If your description says "handles tasks," the model has no idea when to use it versus a different tool. Be specific. Use action verbs. Mention synonyms the user might say ("create," "add," "remember," "note down").

Making Tools Too Broad

Resist the temptation to build one giant tool that does everything. A tool called manage_tasks with a parameter called action that accepts "add," "list," or "complete" is much harder for the model to reason about than three separate, focused tools. Granularity is your friend.

Ignoring the Privacy Manifest

iOS 27 requires a PrivacyInfo.xcprivacy manifest that explicitly declares what data your agent tools access. If your ListTasksTool reads from a local database, you must declare that access. App Store review will reject submissions missing this file, and Xcode 18 will warn you during the build phase.

Testing Only in the Simulator

The Simulator uses stubbed AFM responses that are faster and more predictable than the real model. Always validate your tool descriptions and agent flows on a physical device before shipping. You may find that certain phrasings that "work" in the Simulator fail on device because the real model interprets them differently.

What About Privacy and On-Device Processing?

One of Apple's strongest selling points for the new architecture is privacy, and it is worth understanding this as a developer because it will shape how you talk about your app to users.

By default, all AgentSession reasoning happens on device using the AFM. No user input, no task data, and no tool results are sent to Apple's servers during a standard session. For tasks that require broader world knowledge (for example, if a user asks "What is a good time zone to schedule a meeting with someone in Seoul?"), Siri can optionally route that specific sub-query to Private Cloud Compute, Apple's server-side inference system, which processes the request without logging or retaining user data.

As a developer, you can inspect the session.processingMode property to know whether a given response was generated on device or via Private Cloud Compute. You can also set session.allowsCloudProcessing = false if your app handles particularly sensitive data and you want to guarantee fully local execution, at the cost of some capability.

Where to Go From Here

Once your first agent is running, the natural next steps are:

• Add proactive suggestions: AgentKit includes a AgentSuggestionProvider protocol that lets your agent surface relevant suggestions on the Lock Screen and in Spotlight, without the user having to ask first.
• Integrate with App Intents: Your existing AppIntent actions from iOS 16 and later can be wrapped as AgentTool adapters with minimal code, which means you do not have to rewrite your Shortcuts support from scratch.
• Explore multi-agent coordination: iOS 27 supports a limited form of cross-app agent calling, where your agent can invoke tools registered by another app, with user permission. This is the foundation of the more ambitious agentic workflows Apple previewed at WWDC 2026.
• Read the AgentKit documentation: Apple's developer documentation portal has a dedicated AgentKit section with sample projects, including a reference app called "Orchard" that demonstrates a full-featured agent with memory, proactive features, and cross-app tool use.

Conclusion: The Best Time to Start Is Right Now

The WWDC 2026 announcements represent Apple's clearest statement yet that on-device AI agents are not a future feature. They are a present-day expectation. Users who experience well-designed agentic apps will quickly find it difficult to go back to tapping through menus manually. That creates a real opportunity for developers who move early.

The good news for beginners is that Apple has done the hard part. The model is trained, the runtime is built, and the privacy architecture is in place. Your job is to describe your app's capabilities clearly, expose them as well-named tools, and let the Siri Agent Runtime do the orchestration. That is a much more approachable starting point than building an AI agent from scratch would have been even two years ago.

Start small. Build the task manager. Get comfortable with AgentTool descriptions. Then expand. The developers who ship the most useful agents in the next 12 months will not necessarily be the ones who know the most about machine learning. They will be the ones who understand their users' goals most clearly and describe them most precisely. That is a skill every developer already has the foundation to build.

Happy building.