How to Fix 'deployment crash' in AutoGen (TypeScript)

What “deployment crash” usually means

In AutoGen TypeScript, a deployment crash almost always means the model call failed before the agent could complete a turn. In practice, it shows up when the OpenAI/Azure OpenAI deployment name is wrong, the endpoint is misconfigured, or the request hits a model that doesn’t support the parameters you’re sending.

You’ll usually see it during agent initialization or on the first assistant.run() / agent.send() call, with errors like:

• •DeploymentNotFound
• •The API deployment for this resource does not exist
• •400 Bad Request
• •Error: deployment crashed

The Most Common Cause

The #1 cause is a bad Azure OpenAI deployment configuration in your OpenAIChatCompletionClient or equivalent AutoGen model client.

In TypeScript, people often confuse:

• •model name: gpt-4o, gpt-4.1-mini
• •deployment name: your Azure deployment label, like prod-gpt4o

AutoGen sends requests to the deployment, not the raw model name, when you use Azure.

Broken vs fixed

// ❌ Broken
import { AzureOpenAIChatCompletionClient } from "@autogen/core";

const client = new AzureOpenAIChatCompletionClient({
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
  apiVersion: "2024-02-15-preview",
  deploymentName: "gpt-4o", // wrong if your Azure deployment is named differently
});

// ✅ Fixed
import { AzureOpenAIChatCompletionClient } from "@autogen/core";

const client = new AzureOpenAIChatCompletionClient({
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
  apiVersion: "2024-02-15-preview",
  deploymentName: "prod-gpt4o", // exact Azure deployment name
});

If you’re using non-Azure OpenAI, don’t pass Azure fields at all. Use the OpenAI client with the model name directly.

// ✅ OpenAI (non-Azure)
import { OpenAIChatCompletionClient } from "@autogen/core";

const client = new OpenAIChatCompletionClient({
  apiKey: process.env.OPENAI_API_KEY!,
  model: "gpt-4o",
});

Other Possible Causes

1) Wrong API version for the deployed model

Azure deployments are sensitive to API version. A valid deployment can still fail if the version doesn’t support that model or feature set.

// ❌ Too old / incompatible for your setup
const client = new AzureOpenAIChatCompletionClient({
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
  apiVersion: "2023-05-15",
  deploymentName: "prod-gpt4o",
});

// ✅ Match the version recommended for your resource/model
const client = new AzureOpenAIChatCompletionClient({
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
  apiVersion: "2024-02-15-preview",
  deploymentName: "prod-gpt4o",
});

2) Missing or malformed environment variables

A blank endpoint or API key often surfaces as a generic crash downstream.

// ❌ env vars may be undefined at runtime
const client = new AzureOpenAIChatCompletionClient({
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT,
  apiVersion: "2024-02-15-preview",
  deploymentName: "prod-gpt4o",
});

// ✅ fail fast before AutoGen starts
function required(name: string): string {
  const value = process.env[name];
  if (!value) throw new Error(`Missing env var ${name}`);
  return value;
}

const client = new AzureOpenAIChatCompletionClient({
  apiKey: required("AZURE_OPENAI_API_KEY"),
  endpoint: required("AZURE_OPENAI_ENDPOINT"),
  apiVersion: "2024-02-15-preview",
  deploymentName: required("AZURE_OPENAI_DEPLOYMENT"),
});

3) Tool schema mismatch in an agent team

If you pass malformed tool definitions, some agent flows fail during planning/execution and look like a model crash.

// ❌ invalid tool schema shape
const tools = [
  {
    name: "lookupPolicy",
    description: "Find policy details",
    // missing proper input schema / function signature depending on your AutoGen version
    parameters: {},
  },
];

// ✅ define tools with the expected function signature/schema
const tools = [
  {
    name: "lookupPolicy",
    description: "Find policy details",
    parametersSchema: {
      type: "object",
      properties: {
        policyId: { type: "string" },
      },
      required: ["policyId"],
      additionalProperties: false,
    },
    execute: async ({ policyId }: { policyId?: string }) => {
      return { policyId, statusCode": "ACTIVE" };
    },
  },
];

4) Context window overflow or oversized messages

Large histories can trigger request failures that bubble up as a crash during send.

// ❌ pushing huge payloads into chat history
await agent.send([
  {
    role: "user",
    content: JSON.stringify(bigClaimDocument), // too large
  },
]);

// ✅ summarize or trim before sending
await agent.send([
  {
    role": "user",
    content": `Summarize claim ${claimId} using these extracted fields only...`,
  },
]);

How to Debug It

1. •

   Print the exact error body

   • •Don’t stop at deployment crash.
   • •Log the full exception from AutoGen and the underlying HTTP response.
   • •Look for:
     • •DeploymentNotFound
     • •401 Unauthorized
     • •404 Not Found
     • •400 Bad Request
2. •

   Verify the client config in isolation

   • •Create a tiny script that only instantiates AzureOpenAIChatCompletionClient.
   • •Call one minimal prompt.
   • •If that fails, the issue is not your agent graph.
3. •

   Check deployment naming in Azure

   • •Confirm the deployed resource name exactly matches what you put in code.
   • •Compare against:
     • •portal deployment name
     • •environment variable value
     • •casing and whitespace
4. •

   Remove everything except one agent and one prompt

   • •Disable tools.
   • •Disable multi-agent orchestration.
   • •Send a short prompt like "ping".
   • •If it works, reintroduce features one by one until it breaks.

Prevention

• •

  Use explicit config validation at startup.

  • •Fail fast on missing env vars, empty strings, and invalid URLs.
• •

  Separate model name from deployment name in code.

  • •In reviews, treat those as different concepts.

type LlmConfig = {
  providerType": "azure" | "openai";
};

• •Add a smoke test for every environment.

test("LLM client boots", async () => {
  const result = await client.create([{ role": "user", content": "ping" }]);
});

If you keep seeing deployment crash, start with the deployment config first. In AutoGen TypeScript, that’s where most of these failures come from.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.