Apple Intelligence

How to Rebuild Your Enterprise Multi-Agent Pipeline's Siri Integration Layer After WWDC 2026 Broke Everything

Scott Miller

06 Jun 2026 — 8 min read

If you attended WWDC 2026 or watched the keynote replay, you already felt the gut-punch moment: Apple unveiled a ground-up redesign of its on-device AI architecture, collapsing the old Apple Intelligence routing layer into a new unified Foundation Model Runtime (FMR). It is a genuinely impressive engineering leap. It also silently shattered every enterprise multi-agent pipeline that relied on Siri as a voice-triggered entry point into tool-call workflows.

Monday morning war rooms happened. Slack channels lit up. Production agents stopped responding to voice commands. If you are currently staring at a broken integration and wondering where to start, this guide is for you.

We will walk through exactly what changed, why your existing tool-call contracts failed, and how to systematically rebuild your Siri integration layer so your enterprise agent workflows are more resilient going forward.

What Actually Changed at WWDC 2026 (The Technical Summary)

Before you can fix the breakage, you need a clear mental model of what Apple changed. The previous Apple Intelligence stack (introduced in 2024 and iterated through 2025) used a three-layer dispatch model:

Intent Recognition Layer: Siri parsed utterances and mapped them to INIntent subclasses via SiriKit.
Apple Intelligence Routing Layer: A separate on-device LLM decided whether to handle the request locally, escalate to a server model, or hand off to a third-party App Intent extension.
App Intents / Tool-Call Bridge: Your enterprise app exposed AppIntent conforming structs, which the routing layer called with resolved parameters.

The new Foundation Model Runtime collapses layers one and two into a single on-device transformer that handles intent parsing, context resolution, and tool selection in one forward pass. This is faster and more accurate, but it fundamentally changes the contract in three ways:

Parameter schemas are now inferred, not declared. The old system required explicit @Parameter property wrappers with typed metadata. FMR uses semantic embeddings to match utterances to tools, meaning loosely typed or ambiguous parameter names now resolve differently or fail silently.
The perform() execution context has changed. FMR calls tool implementations from a new sandboxed AgentSession context. Anything your old perform() methods did that touched shared state, keychain, or network sessions without the new AgentSession capability entitlement will throw a runtime exception.
Multi-turn voice sessions use a new continuation protocol. The old INInteraction continuation pattern is deprecated. FMR uses AgentConversationContext, a stateful object passed between tool calls, which is incompatible with the stateless intent handlers most enterprise pipelines were built on.

Step 1: Audit Your Existing Tool-Call Contracts

Do not touch any code yet. Start with a full audit. You need to know exactly which parts of your pipeline are broken before you start patching things.

1a. Run the Apple FMR Compatibility Linter

Apple shipped a command-line linter with Xcode 18 specifically for this migration. Run it against your App Intents module:

xcrun fmr-lint --target YourAppIntentsModule --report compatibility_report.json

The report will categorize your existing AppIntent implementations into three buckets: Compatible, Needs Migration, and Deprecated API. Treat the "Needs Migration" bucket as your primary work queue.

1b. Map Your Voice Entry Points to Agent Nodes

Create a simple spreadsheet or Markdown table that lists every voice-triggered entry point in your pipeline. For each one, record:

The utterance patterns users speak
The AppIntent struct it previously resolved to
The downstream agent node or tool-call chain it triggered
Whether it relied on multi-turn continuation (if yes, flag it as high priority)

This map becomes your migration checklist. Do not skip this step. Enterprise pipelines accumulate voice entry points over years, and you will almost certainly find forgotten intents that are now silently broken.

1c. Check Your Parameter Schema Contracts

For every AppIntent in the "Needs Migration" bucket, review your @Parameter declarations. Look specifically for:

Parameters typed as String with no semantic description (FMR cannot infer intent from raw strings)
Optional parameters with no default fallback behavior
Parameters that relied on INSpeakable entity resolution, which is now handled differently under FMR

Step 2: Migrate Your AppIntent Implementations to FMR-Compatible Schemas

With your audit complete, you can begin the actual migration. The core task here is enriching your parameter schemas so FMR's semantic matcher can resolve them correctly.

2a. Add Semantic Descriptions to Every Parameter

Under the old system, parameter titles were primarily for Shortcuts UI display. Under FMR, they are part of the semantic matching signal. Update every @Parameter to include a meaningful description:

// Before (breaks under FMR)
@Parameter(title: "Project")
var projectIdentifier: String

// After (FMR-compatible)
@Parameter(
    title: "Project Name",
    description: "The name or identifier of the enterprise project to act on. Examples: 'Q3 Roadmap', 'Infra Migration'."
)
var projectIdentifier: String

Think of these descriptions as few-shot examples for the on-device model. The more precise and example-rich they are, the more reliably FMR will resolve spoken utterances to the correct parameter values.

2b. Replace Stateless Handlers with AgentSession-Aware Implementations

This is the most significant code change. Your old perform() methods need to be updated to request the AgentSession capability and use the new session context for any privileged operations:

// Before
func perform() async throws -> some IntentResult {
    let data = try await myNetworkClient.fetch(projectIdentifier)
    return .result(value: data)
}

// After
func perform() async throws -> some IntentResult {
    // Request AgentSession context for privileged operations
    let session = try await AgentSession.current()
    let data = try await myNetworkClient.fetch(
        projectIdentifier,
        authorizedBy: session.credential
    )
    return .result(value: data)
}

You will also need to add the com.apple.developer.agent-session entitlement to your app's entitlements file and request it in your provisioning profile. Enterprise distribution profiles provisioned before June 2026 will need to be regenerated.

2c. Declare Your Tool as an FMR Tool Provider

Add the new FMRToolProvider conformance to your App Intents extension's principal class. This signals to the Foundation Model Runtime that your extension is ready for the new dispatch model:

@main
struct MyEnterpriseIntentsExtension: AppIntentsExtension, FMRToolProvider {
    static var tools: [any AppIntent.Type] {
        [
            FetchProjectStatusIntent.self,
            CreateWorkItemIntent.self,
            TriggerDeploymentIntent.self
        ]
    }
}

Step 3: Rebuild Multi-Turn Voice Session Continuations

This is where most enterprise pipelines have the deepest breakage. If your agents relied on multi-turn voice conversations (for example, a user says "create a ticket," Siri asks for the project, the user responds, Siri asks for priority, and so on), you need to fully replace the old INInteraction continuation pattern with AgentConversationContext.

3a. Understand the New Conversation State Model

The old model was effectively stateless between turns: each utterance triggered a fresh intent resolution. The new AgentConversationContext is a persistent, structured object that travels through the entire conversation. It holds:

Resolved entities from previous turns
The active tool-call chain and its current position
A typed conversationMemory dictionary for custom state
The user's confirmed and unconfirmed parameter values

3b. Implement Continuation Handlers

For any intent that requires multi-turn resolution, implement the new AgentContinuable protocol alongside your AppIntent:

struct CreateWorkItemIntent: AppIntent, AgentContinuable {

    @Parameter(title: "Project Name", description: "The project to add the work item to.")
    var projectName: String

    @Parameter(title: "Priority", description: "Priority level: low, medium, high, or critical.")
    var priority: WorkItemPriority

    // Called by FMR when a required parameter is missing
    func requestContinuation(
        for missingParameters: [ParameterSummary],
        context: AgentConversationContext
    ) async throws -> AgentContinuationRequest {
        // Store any already-resolved values in conversation memory
        if let resolvedProject = context.resolvedValue(for: \.$projectName) {
            context.conversationMemory["pendingProject"] = resolvedProject
        }
        // Return a natural-language prompt for the missing parameter
        return AgentContinuationRequest(
            prompt: "Got it. What priority should this work item be? Low, medium, high, or critical?"
        )
    }

    func perform() async throws -> some IntentResult {
        let session = try await AgentSession.current()
        // ... implementation
    }
}

3c. Wire AgentConversationContext Into Your Orchestration Layer

If your enterprise pipeline uses an external orchestration framework (LangChain, AutoGen, a custom Python orchestrator communicating via your app's local API, or a similar setup), you need to thread the AgentConversationContext session token through to that orchestrator so it can maintain coherent state across turns. Apple provides a context.externalSessionToken string property precisely for this purpose. Store it in your orchestrator's session store and pass it back on each subsequent tool call.

Step 4: Update Your Enterprise MDM and Entitlement Profiles

This step is easy to forget until it bites you in production. The new AgentSession entitlement and the FMRToolProvider capability require updated provisioning profiles. If your organization uses an MDM solution (Jamf, Kandji, Microsoft Intune with Apple Business Manager, or similar), you need to push updated profiles before your migrated app will work on managed devices.

Regenerate your enterprise distribution provisioning profile in Apple Developer Portal to include the com.apple.developer.agent-session entitlement.
If you use a managed app configuration to pass environment-specific parameters to your app, verify that none of those keys conflict with the new AgentSession credential namespace (Apple reserved the com.apple.fmr.* key prefix in iOS 19.5).
Push the updated profile via your MDM before deploying the migrated build. Deploying the build first will result in runtime crashes on managed devices, not a clean degradation.

Step 5: Test Your Rebuilt Pipeline End-to-End

Unit tests alone will not catch the subtle ways FMR's semantic resolution differs from the old SiriKit intent matching. You need a layered testing strategy.

5a. Use the FMR Simulator Harness

Xcode 18's simulator includes a new FMR Harness tool under the Debug menu. It lets you feed raw utterance strings directly into the on-device FMR and observe which tool gets selected, how parameters are resolved, and what the conversation context looks like after each turn. Use it to replay every utterance pattern from your audit spreadsheet.

5b. Write Semantic Resolution Tests

Apple added a new FMRTestCase base class to the testing framework. Use it to write assertions against semantic resolution, not just exact string matching:

class CreateWorkItemResolutionTests: FMRTestCase {
    func testPriorityResolutionFromNaturalLanguage() async throws {
        let result = try await resolveIntent(
            utterance: "make it urgent",
            expectedIntent: CreateWorkItemIntent.self,
            context: conversationContext(with: ["pendingProject": "Infra Migration"])
        )
        XCTAssertEqual(result.priority, .high)
    }
}

5c. Conduct a Staged Rollout

Do not push the migrated build to your entire fleet at once. Use your MDM's staged deployment capability to roll out to a pilot group first. Monitor your observability stack (crash logs, agent trace logs, and voice session completion rates) for 48 hours before expanding the rollout.

Avoiding the Same Pain Next Time: Building a Resilient Siri Integration Layer

The real lesson from the WWDC 2026 breakage is not that Apple changed its APIs (that is inevitable). The lesson is that most enterprise pipelines had no abstraction layer between their agent orchestration logic and the Siri integration surface. When Apple changed the surface, the orchestration logic broke directly.

Going forward, enforce a strict Voice Gateway Pattern in your architecture:

Voice Gateway Interface: A thin, versioned interface that translates voice-triggered events into a normalized internal event format your agents consume. Siri, Teams Copilot, Alexa for Business, and any other voice surface talk to the gateway, not directly to your agents.
Contract Tests: Maintain a suite of contract tests that verify the gateway's output format is stable, regardless of which voice surface is upstream. Run these in CI on every dependency update.
Feature Flags per Voice Surface: Gate each voice surface integration behind a feature flag so you can disable a broken surface without taking down the entire agent pipeline.

Conclusion

The WWDC 2026 Foundation Model Runtime redesign is, without question, the right long-term direction for on-device AI. Unified semantic tool resolution, stateful conversation context, and sandboxed agent sessions are all meaningful improvements over the patchwork of SiriKit and Apple Intelligence layers that came before. The migration pain is real, but it is finite and tractable.

Work through the audit first, then migrate parameter schemas, then tackle multi-turn continuations, then update your MDM profiles, and finally test with the FMR harness before you roll out. If you follow these steps in order, you will have a working, more resilient integration on the other side.

And when WWDC 2027 rolls around and Apple changes something again (because it will), your new Voice Gateway Pattern will mean the blast radius is a single interface adapter, not your entire production pipeline.

Have you started your FMR migration yet? Drop your questions or war stories in the comments. The enterprise AI community is all navigating this together.