Don’t Build Around Today’s Model. Build for the AI Control Plane

Microsoft isn’t just shipping AI models. It is building toward a model-agnostic AI control plane. The bigger story behind the recent Microsoft Foundry updates is not another model launch. It is the architecture direction. Once you see it, the individual announcements fall into place as layers of one stack:

AI Control Plane → Model Router → Agents → RAG/Knowledge → MCP/Tools → Enterprise Systems

The AI control plane takes shape

Two August updates make the direction concrete. First, the model router update expanded the router to 28 regions for global standard and 21 data zone regions. The supported pool now includes Anthropic Claude Opus 4.8 and the GPT-5.6 family, while deprecated models such as DeepSeek-V3.1 and the gpt-5-chat line were pruned. Second, Foundry added DeepSeek-V4-Flash-0731 and NVIDIA Nemotron 3.5 Lightning to the catalog, each available through multiple deployment paths: Direct from Azure, Fireworks on Foundry, or the Hugging Face collection on managed compute.

I covered the router update in more detail on InfoQ, including the caveats around behavioral stability. Here, I want to focus on what the updates mean for your architecture.

Neither update is spectacular on its own. Together, however, they show a platform where models arrive, improve, and retire underneath a stable endpoint. Your application keeps calling the same integration while the pool refreshes. That is control plane behavior, not model shipping.

Agent ≠ Model

Here is the key idea for enterprise architects: an agent is not a model. An agent is an orchestration unit with instructions, knowledge, and tools. The model is a swappable dependency underneath it.

The model router makes that separation operational. It selects a model per request, optimizing for quality, cost, and latency within the regions your governance allows. You can run it in balanced, quality, or cost mode, and you can restrict routing to an approved subset of models. Moreover, every response includes a model field that shows which model handled the request, so the routing decisions leave an auditable trail.

Microsoft frames this as a hill climb: model selection as a continuous, measured loop rather than a one-time decision. In an ecosystem where the frontier moves monthly, a hardcoded model choice goes stale fast. An agent bound to the control plane instead of a specific model can evolve without a rebuild every time the leaderboard changes.

What this means for your architecture

For enterprise architects, the practical guidance follows directly. Treat model selection as configuration, not code. Put governance, observability, and policy at the control plane layer, because that is where they survive model churn. Ground agents in your own knowledge through RAG, and connect them to enterprise systems through MCP and tools. Consequently, each layer can evolve at its own pace. The stack outlives any single model.

Where this is the wrong answer

Model-agnostic routing is not free, and it is not always right. If your workload requires reproducible behavior, for example in regulated decision flows, a pinned model version beats a router that refreshes its pool automatically. Your evaluations were run against a specific model; a silent pool refresh invalidates them until you re-run. There are operational caveats too: Anthropic models still need to be deployed separately before the router can reach them, and routing modes take time to propagate. Finally, a single well-tuned small model per agent remains the simpler option for narrow, high-volume tasks. Routing adds a layer you must monitor. Only accept that cost when model diversity actually pays for itself.

Closing thoughts

The direction is clear even where the details will shift. Models are becoming interchangeable parts. The durable investment is the control plane: routing, governance, evaluation, and the connective tissue to your enterprise systems. So don’t build around today’s model. Build for the control plane, and let the models come and go.

What’s your approach: one model per agent, or model-agnostic agents?

Azure Functions Hosted Skills: When to Use It

Editor’s note (September 2026): Microsoft has renamed this feature from “Azure Functions serverless agents runtime” to “Azure Functions hosted skills.” The content below has been updated to reflect the new name. The URL remains unchanged.

Most conversations about agent runtimes on Azure land on Azure AI Foundry Agent Service. That is the right starting point for teams that want a fully managed, enterprise-grade agent host. But it is not the only option and for event-driven scenarios, it is often not the best one.

The Azure Functions hosted skills is a preview programming model that lets you define agents as function apps. Agents are triggered by events, schedules, messages, or HTTP requests. They run on Flex Consumption with scale-to-zero, managed identity, and Application Insights. And they are deployed with azd like any other function app.

This post explains what the runtime actually is, how its three configuration files work together, and where it fits relative to Foundry Agent Service and Durable Functions. If you are new to Azure Functions as an AI platform, start with Azure Functions AI Integration: The Quiet Powerhouse which maps all four AI-enabled patterns. If you are looking specifically at MCP server hosting, Hosting MCP Servers on Azure Functions covers the three hosting options in detail.

How Azure Functions hosted skills work

The runtime is a programming model built on top of Azure Functions. When an event fires — a timer, an HTTP request, a queue message — the runtime starts the agent, runs it through Microsoft Agent Framework, and handles the trigger registration and endpoint wiring automatically.

You do not write trigger code, and do not implement an agent loop, and define three files, deploy a function app, and the runtime does the rest.

The core project files are:

  • .agent.md — defines the hosted skill: its instructions, its trigger, and the tools it can use
  • agents.config.yaml — app-wide runtime defaults, including the model deployment and any shared infrastructure (such as an Azure Container Apps dynamic session pool for sandboxed code execution)
  • mcp.json — lists the remote MCP servers available to hosted skills in the app
  • tools/ — optional folder containing custom Python tool functions decorated with @tool for app-specific logic
  • skills/ — optional folder containing reusable SKILL.md prompt assets that hosted skills can load on demand

The runtime discovers these files at startup, registers the required triggers and endpoints, and wires the agent to Microsoft Agent Framework. You can have multiple hosted skills in a single function app each defined in its own .agent.md file, all sharing the app-wide configuration.

What Azure Functions hosted skills deploy

The Microsoft Learn quickstart deploys two agents from a single function app:

Chat agent (main.agent.md) — an HTTP-triggered agent that exposes a debug chat UI in the browser. It can execute sandboxed Python code via an Azure Container Apps dynamic session pool and browse the web. No email tooling.

Blog summary agent (daily_microsoft_blog_summary.agent.md) — a timer-triggered agent. The YAML front matter in the file declares the schedule; the markdown body contains the agent instructions. On each timer fire, the agent gathers recent Microsoft blog posts, summarises them, and emails the digest via a managed MCP server connected to Microsoft 365 Outlook.

What gets provisioned by azd up for this template:

ResourcePurpose
Flex Consumption function appHosts the agents
Azure AI Foundry project + model deploymentLLM for agent reasoning
Azure Container Apps dynamic session poolSandboxed Python code execution
Storage accountFunction app state
Application InsightsMonitoring
Connector Namespace + M365 Outlook connectionEmail delivery (optional)
Managed MCP serverExposes the Outlook connector to agents

The provisioning is handled entirely by Bicep via azd you do not configure any of this manually.

How the agent definition files work

.agent.md is a markdown file with YAML front matter. The front matter declares the trigger and any agent-level configuration. The markdown body is the system prompt — the instructions the agent follows when it runs.

The timer-triggered blog summary agent front matter looks roughly like:

---
trigger:
type: timer
schedule: "0 0 8 * * *" # timer_trigger uses underscore in type field
tools:
- mcp_server: outlook
---

The markdown body below that is the agent’s instruction set what to gather, how to summarise it, how to format the email. You write it in plain English.

agents.config.yaml sets defaults that apply across all agents in the app. The model deployment is set here, so every agent uses the same Azure AI Foundry model unless overridden. The session pool endpoint for sandboxed code execution is also set here.

mcp.json lists the remote MCP servers the agents can call. In the quickstart template, this includes the managed MCP server for the Microsoft 365 Outlook connector when email delivery is enabled. The runtime reads this file at startup and makes those servers available to all hosted skills in the app.

How Azure Functions hosted skills differ from Foundry Agent Service

The distinction matters for architecture decisions.

Foundry Agent Service is a fully managed service. Microsoft operates the agent host. You configure agents through the Foundry portal or SDK, connect tools, and the service handles orchestration, state, and scaling. It has enterprise SLAs, built-in tooling, and a managed lifecycle.

Azure Functions hosted skills is a programming model you deploy yourself. You own the function app. You manage the deployment, the model connection, and the infrastructure. In return, you get the full Azure Functions hosting model — event-driven triggers, Flex Consumption billing, VNet integration, managed identity, and azd-based deployment pipelines.

The decision table:

SituationUse
Event-driven triggers, AI reasoning, scale-to-zero, VNet integrationAzure Functions hosted skills
Expose deterministic functions as tools for another AI clientAzure Functions MCP extension
Long-running, multi-step orchestration with human-in-the-loop approvalsDurable Functions
Create and manage AI agents without custom hostingMicrosoft Foundry or Copilot Studio

These are not mutually exclusive. Azure Functions hosted skills can call tools hosted in Foundry via MCP servers, and Foundry agents can call tools hosted in Azure Functions. The two can coexist in the same architecture.

How Azure Functions hosted skills differ from Durable Functions

Post 4 in this series covers Durable Functions for directed agentic workflows in detail, but the short version is:

Durable Functions is for directed, deterministic workflows — you define the steps, the model executes them in order, and Durable Functions handles state, retry, and fault tolerance. The workflow is predictable.

Azure Functions hosted skills is for autonomous agents — you give the agent instructions and tools, and Microsoft Agent Framework determines how to use them to accomplish the goal. The execution path is not predetermined.

If your AI-driven process has fixed, ordered steps and you need auditability, use Durable Functions. If you want the agent to figure out the steps, use Azure Functions hosted skills.

What to know before you build

It is preview. The programming model, file format, and configuration details are subject to change. Do not build production-critical workloads on this today without a plan for the preview-to-GA migration.

It requires a Foundry project and model deployment. The azd template provisions both automatically, but you need an Azure subscription with permissions to create Foundry resources and model deployments. Some organisations have restrictions on which model deployments are permitted.

The azd template provisions real Azure resources with real costs. The Flex Consumption plan itself is very low cost for low-traffic agents, but the Foundry model deployment, Container Apps session pool, and Connector Namespace resources all have associated costs. Review the Bicep templates in infra/ before running azd up in a production subscription.

Custom Python tools are how you add app-specific logic. Azure Functions hosted skills provides the agent loop and the MCP connections. For anything that requires your own code — calling internal APIs, reading proprietary data sources, applying business rules — you write Python tool functions and register them in the agent definition.

Getting started

The quickstart template is the right starting point:

azd init --template Azure-Samples/functions-quickstart-serverless-agents-azd -e serverless-agents
azd env set TO_EMAIL <your-email>
azd up

Review the three configuration files in src/ before deploying. They are short and readable, and understanding them before the first deployment saves debugging time later.

The email delivery step (setting TO_EMAIL and authorizing the Microsoft 365 Outlook connection) is optional. If you skip it, the timer agent still runs and returns its digest in the final response, which you can verify in Application Insights logs.

Try it with a weather sample.

If you want to see the runtime in action with a minimal, self-contained example before committing to the full quickstart, I built a companion sample: a weather chat agent that fetches live conditions and 3-day forecasts for any location using Open-Meteo, no API key, no M365 connector, no email setup required.

The agent is defined in a single main.agent.md file. It uses Python code execution via the Container Apps session pool to call the Open-Meteo API and returns structured weather data in the chat UI. Deploy it in three commands:

git clone https://github.com/steefjan1/weather-agents
cd weather-agents
azd up

Select Central US when prompted for location — the runtime is in preview and region availability is limited. The chat UI is at https://<function-app-name>.azurewebsites.net/api/agents/main/ once deployment completes.

The README documents two known issues you will hit if you try to build from scratch rather than the official quickstart: a broken transitive dependency in azurefunctions-agents-runtime that pins a yanked version of github-copilot-sdk, and the region constraint. Both are worth knowing before you invest time in a custom deployment.

Up next: Durable Functions as the Orchestration Layer for Directed Agentic Workflows

Citadel APIM Kill Switch: Stop a Governed Agent Cold

In the previous post we added conversation persistence to the Microsoft Foundry Citadel Platform on Azure. As a result, every agent run now produces a structured document in the spoke’s Cosmos DB conversations container. The agent is fully operational: it routes through the APIM governance hub, executes tool calls, stores its history, and returns grounded responses. However, the question that every enterprise AI architect eventually faces remains: what happens when it needs to stop?

Not a graceful shutdown. Not a redeployment. An immediate, operator-triggered containment the kind you need when an agent is behaving unexpectedly, consuming runaway tokens, or has been flagged by your security team. In a Microsoft Foundry Citadel Platform on Azure deployment, the answer is the Kill Switch: a layered containment system built into the APIM hub that stops agent traffic cold without touching the agent code, the spoke, or the Azure OpenAI deployment.

This post implements three of the five Citadel kill switch layers against the hub we deployed in Sweden Central:

  • Layer 1 — Named Value flip: instant global block via a single boolean
  • Layer 2 — JWT claim block: identity-based containment via header validation
  • Layer 3 — Agent ID blocklist: surgical per-agent blocking

The Scenario

The weather agent (agent_with_memory.py) is running in production. Specifically, it is routing through apim-wpvlimv4ngkns.azure-api.net, storing conversations in the spoke Cosmos DB, and generating token usage events in the hub Cosmos DB. Everything is working. Then your security team flags it. The agent needs to stop immediately while the incident is investigated. You have seconds, not minutes. For example, redeploying the spoke takes too long. Rotating the APIM subscription key is irreversible and affects all consumers. Therefore, the Kill Switch is the right tool.

The Kill Switch is the right tool. The APIM hub has built-in pre-wiring, requires no code changes, and can trigger actions in under 30 seconds.

To ensure reliability, always pre-wire the kill switch as Layer 1 before you need it. Remember, you can’t flip a Named Value that doesn’t exist. In addition, the inbound policy must already be in place, checking the Named Value on every request, before any incident occurs.

Prerequisites

From the previous posts you should have:

  • Hub deployed in rg-ai-hub-gateway-dev with APIM instance apim-wpvlimv4ngkns
  • agent_with_memory.py running and saving to Cosmos DB
  • Azure CLI authenticated

Citadel Kill Switch Layer 1 — Named Value Flip

How It Works

A Named Value called kill-switch-enabled is created in APIM and set to false. An inbound policy on the OpenAI API checks this value on every request. When the value is flipped to true, all requests through the gateway immediately return HTTP 403 — no code changes, no redeployment, no spoke involvement.

Step 1.1 — Create the Named Value

az apim nv create `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id kill-switch-enabled `
--display-name "kill-switch-enabled" `
--value "false" `
--secret false

Verify it was created:

az apim nv show `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id kill-switch-enabled `
--query "value" -o tsv

Should return false.

Step 1.2 — Add the Inbound Policy

In the Azure Portal:

  1. Navigate to apim-wpvlimv4ngknsAPIsAzure OpenAI Service APIAll operations
  2. Click PoliciesInbound processingEdit
  3. Add this policy inside the <inbound> section, before any other policies:
<!-- Kill Switch Layer 1: Named Value flip -->
<set-variable name="killSwitchActive" value="@("{{kill-switch-enabled}}" == "true")" />
<choose>
<when condition="@((bool)context.Variables["killSwitchActive"])">
<return-response>
<set-status code="403" reason="Agent Suspended" />
<set-header name="Content-Type" exists-action="override">
<value>application/json</value>
</set-header>
<set-header name="x-kill-switch-layer" exists-action="override">
<value>1-named-value</value>
</set-header>
<set-body>{"error": {"code": "KillSwitchActive", "message": "Agent access has been suspended by the governance hub.", "layer": 1}}</set-body>
</return-response>
</when>
</choose>

The dedicated Named Value check policy provides a cleaner approach.

<!-- Kill Switch Layer 1: Named Value flip -->
<!-- Pre-wire this BEFORE any incident. Set kill-switch-enabled=true to activate. -->
<set-variable name="killSwitchActive" value="@("{{kill-switch-enabled}}" == "true")" />
<choose>
<when condition="@((bool)context.Variables["killSwitchActive"])">
<return-response>
<set-status code="403" reason="Agent Suspended" />
<set-header name="Content-Type" exists-action="override">
<value>application/json</value>
</set-header>
<set-header name="x-kill-switch-layer" exists-action="override">
<value>1-named-value</value>
</set-header>
<set-body>{"error": {"code": "KillSwitchActive", "message": "Agent access has been suspended by the governance hub. Contact your administrator.", "layer": 1}}</set-body>
</return-response>
</when>
</choose>

Click Save.

Step 1.3 — Confirm Agent Runs Normally

With kill-switch-enabled set to false, the agent should still work:

python agent_with_memory.py

Expected output: normal run, conversation saved, answer returned.

Step 1.4 — Trigger the Kill Switch

az apim nv update `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id kill-switch-enabled `
--value "true"

Now run the agent:

python agent_with_memory.py

Expected output:

The agent stops. No spoke changes occur. No code changes happen. One CLI command executes.

Step 1.5 — Reset

az apim nv update `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id kill-switch-enabled `
--value "false"

Citadel Kill Switch Layer 2 — Agent Approval Header

How It Works

The agent is required to pass a custom header x-agent-token containing a signed JWT with a specific claim (agt-approved: true). The APIM inbound policy validates this claim. If the claim is absent or the token is invalid, the system blocks the request with a 401 status. This action simulates identity-based containment, revoking the agent’s token or invalidating its claim at the identity provider level.

Step 2.1 — Update the Agent to Send a Header

Add the x-agent-token header to agent_with_memory.py. In this demo, we simulate the token by using a simple header value. IIn a production environment, Entra ID issues a JWT.

Modify the AzureOpenAI client creation in agent_with_memory.py:

client = AzureOpenAI(
azure_endpoint=apim_base,
api_key=cfg["APIM_SUBSCRIPTION_KEY"],
api_version="2024-02-01",
default_headers={
"x-agent-id": "citadel-weather-agent-v1"
}
)

Step 2.2 — Add the JWT Claim Check Policy

In the portal, add this policy after the Layer 1 block in the inbound section:

<!-- Kill Switch Layer 2: Agent approval header check -->
<choose>
<when condition="@(context.Request.Headers.GetValueOrDefault("x-agent-approved", "false") != "true")">
<return-response>
<set-status code="401" reason="Agent Not Approved" />
<set-header name="Content-Type" exists-action="override">
<value>application/json</value>
</set-header>
<set-header name="x-kill-switch-layer" exists-action="override">
<value>2-agent-approval</value>
</set-header>
<set-body>{"error": {"code": "AgentNotApproved", "message": "Agent identity could not be verified. Approval header missing or invalid.", "layer": 2}}</set-body>
</return-response>
</when>
</choose>

Step 2.3 — Trigger Layer 2

Remove the x-agent-approved header from the agent (or set it to false) and run:

python agent_with_memory.py

Expected output:

Note the response header x-kill-switch-layer: 2-agent-approval this indicates which containment layer fired and is critical for incident triage.

Pitfall: Policy Order Matters

Layer 1 must appear before Layer 2 in the policy document. APIM evaluates inbound policies top to bottom and stops at the first <return-response>. If Layer 2 appears before Layer 1, a globally suspended agent would return a 401 (identity error) instead of a 403 (suspended), obscuring the true containment reason in your incident log.

Citadel Kill Switch Layer 3 — Agent ID Blocklist in APIM

How It Works

A Named Value called blocked-agent-ids holds a comma-separated list of agent IDs. The inbound policy checks the x-agent-id header against this list. When agents match, the system blocks them with a 403 status code. Non-matching agents continue operating normally. This approach allows for surgical containment, stopping one specific agent while allowing all others to function.

Step 3.1 — Create the Blocklist Named Value

az apim nv create `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id blocked-agent-ids `
--display-name "blocked-agent-ids" `
--value "none" `
--secret false

Start with an empty value — no agents blocked.

Step 3.2 — Add the Blocklist Policy

Add this policy after Layer 2 in the inbound section:

<!-- Kill Switch Layer 3: Agent ID blocklist -->
<set-variable name="agentId" value="@(context.Request.Headers.GetValueOrDefault("x-agent-id", ""))" />
<set-variable name="blockedIds" value="@("{{blocked-agent-ids}}")" />
<choose>
<when condition="@{
var agentId = (string)context.Variables["agentId"];
var blockedIds = (string)context.Variables["blockedIds"];
if (string.IsNullOrEmpty(agentId) || string.IsNullOrEmpty(blockedIds)) { return false; }
return blockedIds.Split(',').Any(id => id.Trim() == agentId.Trim());
}">
<return-response>
<set-status code="403" reason="Agent Blocked" />
<set-header name="Content-Type" exists-action="override">
<value>application/json</value>
</set-header>
<set-header name="x-kill-switch-layer" exists-action="override">
<value>3-agent-blocklist</value>
</set-header>
<set-body>{"error": {"code": "AgentBlocked", "message": "Agent has been added to the governance blocklist.", "layer": 3}}</set-body>
</return-response>
</when>
</choose>

Step 3.3 — Add the Agent to the Blocklist

az apim nv update `
--resource-group rg-ai-hub-gateway-dev `
--service-name apim-wpvlimv4ngkns `
--named-value-id blocked-agent-ids `
--value "citadel-weather-agent-v1"

Run the agent:

python agent_with_memory.py

Expected output:

Step 3.4 — Surgical Validation

The power of Layer 3 is specificity. If you had a second agent with a different x-agent-id say citadel-docs-agent-v1 it would pass through Layer 3 unaffected while citadel-weather-agent-v1 remains blocked. One agent stopped, all others running. This is the enterprise AI governance pattern: granular control without broad disruption.

Remove the agent from the blocklist:

az apim nv update --resource-group rg-ai-hub-gateway-dev
--service-name apim-wpvlimv4ngkns --named-value-id blocked-agent-ids
--value "none"

Validating the Citadel Kill Switch in Application Insights

Rather than using the CLI — which has a 5–10 minute Log Analytics ingestion lag — go directly to Application Insights in the portal for immediate results:

  1. Portal → appi-apim-wpvlimv4ngkns in rg-ai-hub-gateway-dev
  2. Left sidebar → Logs
  3. Paste and run this query:
requests
| where timestamp > ago(2h)
| where resultCode in ("200", "401", "403")
| project timestamp, resultCode, duration, name
| order by timestamp desc

The results table tells the complete kill switch story in two columns — resultCode and duration:

The duration contrast is the definitive proof that the kill switch works as designed. The 401s and 403s resolve in under 50ms, stopped cold at the APIM inbound policy before a single token is sent to Azure OpenAI. The 200s take 683ms–2010ms because they made the full round trip through the governance hub to Azure OpenAI and back.

Zero tokens consumed on blocked requests, zero cost, and zero Cosmos DB writes in the spoke. The agent is stopped at the perimeter.

For a sharper view that highlights exactly which kill switch layer fired on each blocked request, add the response header to the query. Unfortunately APIM response headers are not automatically projected into the requests table in Application Insights — but you can distinguish the layers by combining result code and timing:

requests
| where timestamp > ago(2h)
| where resultCode in ("200", "401", "403")
| extend killSwitchLayer = case(
resultCode == "401", "Layer 2 — agent approval",
resultCode == "403" and duration < 10, "Layer 1 or 3 — gateway block",
resultCode == "200", "Normal — LLM call completed",
"Unknown"
)
| project timestamp, resultCode, duration, killSwitchLayer
| order by timestamp desc

This gives you a readable incident log showing which containment layer was active at each point in time, directly useful for DORA incident post-mortem documentation and EU AI Act Article 17 risk management records.

The Complete Three-Layer Kill Switch Policy

Here is the complete inbound policy block containing all three layers, ready to paste into APIM:

 <!-- Kill Switch Layer 1: Named Value flip -->
        <set-variable name="killSwitchActive" value="@("{{kill-switch-enabled}}" == "true")" />
        <choose>
            <when condition="@((bool)context.Variables["killSwitchActive"])">
                <return-response>
                    <set-status code="403" reason="Agent Suspended" />
                    <set-header name="Content-Type" exists-action="override">
                        <value>application/json</value>
                    </set-header>
                    <set-header name="x-kill-switch-layer" exists-action="override">
                        <value>1-named-value</value>
                    </set-header>
                    <set-body>{"error": {"code": "KillSwitchActive", "message": "Agent access has been suspended by the governance hub.", "layer": 1}}</set-body>
                </return-response>
            </when>
        </choose>
        <!-- Kill Switch Layer 2: Agent approval header -->
        <choose>
            <when condition="@(context.Request.Headers.GetValueOrDefault("x-agent-approved", "false") != "true")">
                <return-response>
                    <set-status code="401" reason="Agent Not Approved" />
                    <set-header name="Content-Type" exists-action="override">
                        <value>application/json</value>
                    </set-header>
                    <set-header name="x-kill-switch-layer" exists-action="override">
                        <value>2-agent-approval</value>
                    </set-header>
                    <set-body>{"error": {"code": "AgentNotApproved", "message": "Agent identity could not be verified. Approval header missing or invalid.", "layer": 2}}</set-body>
                </return-response>
            </when>
        </choose>
        <!-- Kill Switch Layer 3: Agent ID blocklist -->
        <set-variable name="agentId" value="@(context.Request.Headers.GetValueOrDefault("x-agent-id", ""))" />
        <set-variable name="blockedIds" value="@("{{blocked-agent-ids}}")" />
        <choose>
            <when condition="@{
        var agentId = (string)context.Variables["agentId"];
        var blockedIds = (string)context.Variables["blockedIds"];
        if (string.IsNullOrEmpty(agentId) || string.IsNullOrEmpty(blockedIds)) { return false; }
        return blockedIds.Split(',').Any(id => id.Trim() == agentId.Trim());
    }">
                <return-response>
                    <set-status code="403" reason="Agent Blocked" />
                    <set-header name="Content-Type" exists-action="override">
                        <value>application/json</value>
                    </set-header>
                    <set-header name="x-kill-switch-layer" exists-action="override">
                        <value>3-agent-blocklist</value>
                    </set-header>
                    <set-body>{"error": {"code": "AgentBlocked", "message": "Agent has been added to the governance blocklist.", "layer": 3}}</set-body>
                </return-response>
            </when>
        </choose>

Pitfalls Summary

PitfallFix
Named Value doesn’t exist at incident timePre-wire Layer 1 during normal operations — never during an incident
Policy evaluation error on {{kill-switch-enabled}}Named Value must exist before the policy referencing it is saved
Layer 2 fires before Layer 1 in policyPolicy order matters — Layer 1 must be first in the inbound block
Agent ID header not sentAdd x-agent-id to default_headers in AzureOpenAI client
Blocklist with trailing spaces blocks nothingUse .Trim() in the policy C# expression when splitting
Kill switch left active after testAlways reset Named Values after testing — kill-switch-enabled=false, blocked-agent-ids=""

What the Kill Switch Demonstrates About Citadel

The three layers reveal something important about the Citadel architecture: governance lives in the hub, not the agent. The agent code has no knowledge of the kill switch. The spoke has no kill switch configuration. The Azure OpenAI deployment is untouched. All containment logic is in the APIM hub’s inbound policy — one place, centrally managed, instantly effective.

This is the enterprise AI control plane pattern in practice. When an incident occurs:

  • Layer 1 stops everything immediately while you triage
  • Layer 2 enforces identity verification once normal operations resume
  • Layer 3 surgically targets the offending agent while other agents continue

The x-kill-switch-layer response header ensures your incident log captures exactly which containment mechanism fired, giving you a clean audit trail for post-mortem analysis — directly relevant for DORA incident reporting and EU AI Act Article 17 risk management documentation.

What’s Next

The next post in this series takes the dev setup and hardens it for non-prod: networkIsolation=true, APIM Premium SKU, per-spoke subscription keys with independent quotas, and Azure Policy at the management group level. The kill switch policies we built here carry forward unchanged governance in the hub environment, which is environment-agnostic.