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:

Azure Application Insights Logs results table showing POST /openai/deployments/chat/chat/completions requests — two 401 responses at 41ms and 0.9ms from Layer 2 kill switch activation, and four 200 responses at 683ms to 2010ms from normal governed agent runs through the Microsoft Foundry Citadel APIM hub in Sweden Central.
Application Insights Logs query on the Citadel APIM hub showing the kill switch in action, 401 responses at under 1ms confirm Layer 2 (agent approval header) blocking requests at the gateway before any LLM call is made, contrasted with normal 200 responses taking 683ms–2010ms for a full Azure OpenAI round trip.

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
Azure Application Insights Logs results table for the Citadel APIM hub showing six requests — two 401 responses labeled Layer 2 agent approval at 41ms and 0.9ms duration, and four 200 responses labeled Normal LLM call completed at 683ms to 2010ms duration, confirming the Microsoft Foundry Citadel kill switch blocks requests at the gateway before any Azure OpenAI call is made.
Application Insights Logs query on the Citadel APIM hub showing the kill switch incident log — Layer 2 agent approval blocks resolving in under 1ms with zero LLM calls made, contrasted with normal governed runs completing in 683ms–2010ms. The killSwitchLayer column identifies exactly which containment layer fired on each request.

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.

Azure API Management as MCP Gateway: Governing Agentic AI Workloads

Part 7 of 7 in the “APIM for AI Workloads” series

Azure API Management as MCP gateway is the natural endpoint of everything this series has built. In Parts 1 through 6, we established APIM as the control plane for AI workloads: securing access, limiting and measuring token consumption, routing traffic resiliently across backends, and reducing costs through semantic caching. All of that applies equally to agentic workloads. The difference is that agents introduce a new communication pattern: the Model Context Protocol (MCP), which standardizes how AI agents discover and call tools.

In my work and online research on agentic AI architecture, I consistently returned to the same question: how does one govern agent tool calls with the same rigor we apply to API calls? The answer, increasingly, is that APIM handles both. This post covers what that looks like in practice.

What MCP Is and Why It Changes the APIM Story

MCP is an open protocol, originally developed by Anthropic, that defines a standard interface between AI agents (MCP clients) and the tools they call (MCP servers). Instead of each agent framework implementing its own bespoke tool-calling mechanism, MCP gives agents a consistent way to discover available tools, understand their input schemas, and invoke them. Frameworks including Semantic Kernel, AutoGen, and LangGraph are all adding MCP client support.

For APIM, MCP matters because it transforms the gateway from a proxy for AI completions into a broker for agent tool calls. An agent no longer calls your internal APIs directly. Instead, it discovers them as MCP tools through APIM, and APIM enforces the same governance policies on those tool calls that it enforces on any other request. The control plane extends naturally into the agentic layer.

Azure API Management as MCP Gateway: Three Capabilities

Diagram showing Azure API Management acting as an MCP gateway. On the left, AI agents connect as MCP clients. In the centre, APIM exposes REST APIs as MCP tool definitions, proxies external MCP servers, and routes agent-to-agent traffic through the policy pipeline. On the right, Azure OpenAI and AI Foundry backends receive governed requests.
Azure API Management as an MCP gateway. Existing REST APIs are auto-exposed as MCP tool definitions via the export-rest-mcp-server policy. External MCP servers are proxied through APIM. Agent-to-agent traffic passes through the same inbound policy pipeline, with all series policies, authentication, token limits, token metrics, andload balancing applied uniformly.

APIM’s MCP gateway capabilities fall into three categories:

Expose REST APIs as MCP servers. The export-rest-mcp-server policy takes any API already registered in your APIM catalog and auto-generates MCP tool definitions from it. An agent connecting to your APIM MCP endpoint discovers those tools via the standard MCP protocol and can call them without any knowledge of the underlying REST implementation. Crucially, no changes are required to the underlying API. The policy handles the translation layer entirely within APIM.

Pass through external MCP servers. APIM can proxy external MCP servers — whether third-party services like GitHub or Jira, or custom MCP servers built by your own teams — through the same gateway. All traffic passes through APIM’s policy pipeline, so you apply JWT validation, subscription key enforcement, token limits, and logging to external MCP calls exactly as you would to any other API call. Agents get a single APIM endpoint; APIM handles the routing.

Agent-to-agent (A2A) traffic. In multi-agent architectures, orchestrator agents call sub-agents to delegate tasks. Routing that traffic through APIM means every A2A hop is governed: authenticated, rate-limited, logged, and subject to the same token budget controls applied to end-user traffic. This is particularly relevant for agentic pipelines running on Microsoft Foundry, where multiple specialized agents collaborate within a single workflow.

Applying Series Policies to Agentic Workloads

One of the practical advantages of routing MCP traffic through APIM is that every policy covered in this series applies without modification. Agentic workloads are not a special case requiring a separate governance layer. They use the same pipeline.

  • Authentication (Part 2): Agents authenticate to APIM using subscription keys or JWT tokens. APIM authenticates to AI backends via Managed Identity. The agent never holds backend credentials.
  • Token limits (Part 3): Multi-step agentic pipelines can consume large token volumes per workflow. Per-subscription TPM limits prevent a single runaway pipeline from exhausting shared capacity.
  • Token metrics (Part 4): Token consumption from agentic workflows is attributed to the subscribing team or pipeline via the emit-token-metric policy. FinOps visibility extends automatically to agentic workloads.
  • Load balancing (Part 5): Agentic pipelines often run longer and consume more tokens per call than chat applications. PTU-to-PAYG failover protects pipeline continuity when primary capacity saturates.
  • Semantic caching (Part 6): Agents that make repeated identical tool calls, checking a status, or looking up a reference value, benefit from semantic caching in the same way chat applications do.

Practical Considerations for APIM as MCP Gateway

A few agentic-specific considerations are worth calling out before you start routing MCP traffic through APIM.

Tool discovery latency. MCP clients typically discover available tools at session start by calling the MCP server’s tool list endpoint. With APIM in the path, that discovery call passes through the full policy pipeline. Keep your inbound policies lightweight for discovery calls, or cache the tool list response to avoid repeated round trips.

Streaming responses. Many AI completions endpoints support streaming via server-sent events. APIM supports streaming passthrough, but some policies — including semantic cache lookup — do not apply to streaming responses. Structure your pipeline accordingly: apply caching only to non-streaming completion calls.

Session state. MCP conversations are stateful within a session. APIM is stateless between requests, so per-session state must live in the calling agent or an external store. The vary-by pattern from the semantic cache policy can scope cached tool responses by session ID if the agent passes one in a header.

Token budget propagation. In multi-agent pipelines, token budgets need to propagate from the orchestrator to sub-agents. Exposing the remaining token budget from the remaining-tokens-variable-name attribute (Part 3) as a response header lets orchestration frameworks like Semantic Kernel make informed decisions about which sub-agent to invoke next.

Azure API Management as MCP Gateway: Closing the Series

This post closes the series, but the control plane it describes is not static. MCP is still evolving rapidly. New APIM policy capabilities for agentic workloads are shipping frequently. The architecture board conversation at various enterprise has shifted from “should we centralize AI traffic through APIM?” to “what do we govern next?”, which is a good place to be.

Diagram showing the complete Azure API Management AI control plane. On the left, five consumer types — AI agents, chat apps, copilots, pipelines, and enterprise apps — connect through a single APIM instance. In the centre, seven policy layers are stacked vertically: authentication, token limit, token metric, load balancing and circuit breaker, semantic caching, MCP gateway, and named value kill switch, each labelled with its series part number. On the right, Azure AI backends including Azure OpenAI PTU and PAYG, AI Foundry, and MCP-enabled backends receive governed requests.
The complete APIM for AI control plane across all seven parts of the series. One APIM instance governs every consumer type, every Azure AI backend, and every governance requirement — including agentic MCP workloads introduced in this post. Each policy layer can be implemented incrementally, starting with authentication and adding capability as workloads mature.

Looking back across the seven posts, the consistent theme is that AI workloads are not fundamentally different from other API workloads in terms of governance requirements. They need authentication, rate limiting, observability, resilience, and cost control. APIM provides all of those. What changes with AI is the unit of measurement (tokens, not requests), the billing model (PTU vs. PAYG), and now the communication protocol (MCP for agents). The control plane adapts to each of these without requiring a parallel governance infrastructure.

The full series index is below for reference. Each post links to the relevant Microsoft documentation and includes policy XML you can use directly.

  • Part 1: Why your AI APIs need a gateway.
  • Part 2: Authentication and authorization.
  • Part 3: Token limit policy.
  • Part 4: Token metric policy and cross-charging.
  • Part 5: Load balancing and circuit breaking.
  • Part 6: Semantic caching.

Part 7 (this post): APIM as MCP gateway for agentic AI workloads.