From a42f39fe1f923d06c96564ee2b53e1735e543c85 Mon Sep 17 00:00:00 2001
From: VC <bazooka720@gmail.com>
Date: Fri, 22 May 2026 16:41:30 -0400
Subject: [PATCH] Add Tuning Engines Dapr AI integration docs

Signed-off-by: VC <bazooka720@gmail.com>
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 .../tuning-engines/_index.md                  | 78 +++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 daprdocs/content/en/developing-ai/agent-integrations/tuning-engines/_index.md

diff --git a/daprdocs/content/en/developing-ai/agent-integrations/tuning-engines/_index.md b/daprdocs/content/en/developing-ai/agent-integrations/tuning-engines/_index.md
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+---
+type: docs
+title: "Tuning Engines"
+linkTitle: "Tuning Engines"
+weight: 45
+description: "Use Dapr Workflow with Tuning Engines as a governed AI endpoint"
+---
+
+### What is the Dapr Tuning Engines integration pattern?
+
+Dapr Workflow owns durable execution, retries, service invocation, pub/sub, and
+sidecar-based application integration. Tuning Engines can sit behind workflow
+activities as an OpenAI-compatible AI endpoint that provides model routing,
+policy checks, approvals, usage attribution, and runtime traces.
+
+Use this pattern when a Dapr application needs governed model calls without
+embedding provider-specific credentials or policy logic in every workflow.
+
+### Configuration
+
+Set a Tuning Engines inference key and choose the model or routing alias your
+tenant has enabled:
+
+```bash
+export TE_INFERENCE_KEY=sk-te-your-inference-key
+export TE_MODEL=auto
+```
+
+### Activity example
+
+```ts
+import { WorkflowRuntime } from "@dapr/dapr";
+
+type Input = {
+  prompt: string;
+  run_id: string;
+};
+
+function newId(prefix: string): string {
+  return `${prefix}_${crypto.randomUUID().replaceAll("-", "")}`;
+}
+
+async function governedModelActivity(_ctx: unknown, input: Input) {
+  const request_id = newId("req");
+  const response = await fetch("https://api.tuningengines.com/v1/chat/completions", {
+    method: "POST",
+    headers: {
+      Authorization: `Bearer ${process.env.TE_INFERENCE_KEY}`,
+      "Content-Type": "application/json",
+      "X-TE-Run-ID": input.run_id,
+      "X-TE-Request-ID": request_id,
+    },
+    body: JSON.stringify({
+      model: process.env.TE_MODEL || "auto",
+      messages: [{ role: "user", content: input.prompt }],
+      metadata: {
+        run_id: input.run_id,
+        request_id,
+        runtime: "dapr",
+        event_type: "model.call",
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Tuning Engines request failed: ${response.status} ${await response.text()}`);
+  }
+
+  return response.json();
+}
+
+const runtime = new WorkflowRuntime();
+runtime.registerActivityWithName("governedModelActivity", governedModelActivity);
+```
+
+The `run_id` and `request_id` metadata let Tuning Engines correlate the model
+call with policy decisions, approval requests, usage/cost logs, and trace
+events. Dapr continues to own the workflow state and the activity lifecycle.