# Deploy a Foundry Agent

Provision Azure resources when needed, deploy the agent, and smoke-test it.

For **hosted agents** (custom container or code), use `azd deploy`. Prefer **direct code deployment through azd** (no Docker/ACR required): the agent's `azure.yaml` service block must contain `codeConfiguration:`, so `azd deploy` will use direct code deployment and zip the source and let Foundry build it. Use container/ACR deployment only when the agent truly needs a Dockerfile, custom system packages, or a pre-built image.

For **prompt agents** (LLM + instructions, no custom code), use the Foundry MCP `agent_update` tool.

## Quick Reference

| Property | Value |
|----------|-------|
| Hosted (recommended) | `azd provision` when needed, direct code deployment via `azd deploy` (`codeConfiguration` present), then verify and invoke |
| Hosted (container) | `azd provision` when needed, container/ACR deployment via `azd deploy` (requires Docker/Podman + ACR, no `codeConfiguration:` in the `azure.yaml` service block) |
| Prompt MCP | `agent_definition_schema_get`, `agent_update`, `agent_get`, `agent_delete` |
| Versioning | Each successful `azd deploy` creates an immutable agent version |
| Endpoint-only patch | `azd ai agent endpoint update` (no new version) |
| Local dev | [create-hosted](../create/create-hosted.md), [local-run](../create/references/local-run.md) |

## Hosted vs Prompt

- Shipping Python / .NET code -> **Hosted** (azd workflow below).
- Updating only model / instructions / tools -> **Prompt** (MCP workflow below).

## Deployment Method Selection -- Hosted agents

Before running `azd deploy`, inspect the agent's service block in `azure.yaml`.

| Service block state | Deployment path |
|------------------|-----------------|
| `codeConfiguration:` present | **Direct code deploy** through `azd deploy`; no Docker/ACR build. |
| No `codeConfiguration:` | **Container/ACR deploy** through `azd deploy`; builds/pushes an image or uses a pre-built `image:`. |

`codeConfiguration:` example in the `azure.yaml` service block:

```yaml
services:
  <agent-name>:
    host: azure.ai.agent
    codeConfiguration:
      runtime: python_3_13
      entryPoint: main.py
      dependencyResolution: remote_build
```

Default to direct code for standard hosted-agent code. If `azd deploy` prints `Packaging container` for an agent that does not need container-specific behavior, add or fix `codeConfiguration` and retry. Use the container path when the agent depends on Dockerfile behavior, system packages, or a pre-built image.

## Workflow -- Hosted agent (azd)

> Prerequisite: project scaffolded with `azd ai agent init`. If not, start at [create-hosted](../create/create-hosted.md).

### Step 1 -- Resolve azd environment

If the user provided an existing project endpoint, project ARM ID, or model deployment, set those values before deploy. Then verify the azd environment with `azd env get-values`.

```bash
azd env set AZURE_AI_PROJECT_ENDPOINT "<project-endpoint>"
azd env set AZURE_AI_PROJECT_ID "<project-arm-id>"
azd env set AZURE_AI_MODEL_DEPLOYMENT_NAME "<model-deployment-name>"
azd env get-values
```

Run:

```bash
azd ai project show --output json
azd ai agent show --output json
```

Branch on output: `not_deployed` -> Step 2. `active` / `deployed` -> redeploy (skip Step 2, go to Step 3). If `azd ai project show` fails with `missing_project_endpoint`, do Step 2 first -- `azd provision` will create the project.

> **Important:** Before deploy, also make sure the agent's `azure.yaml` service block and the azd environment are aligned with the user's provided configuration values.

### Step 2 -- Provision Azure resources (one-time per env)

> 🚦 **Project-selection gate.** If no foundry project endpoint is configured (not in the message, `azd env`, or `.env`) and the user hasn't asked to create one, stop and ask them to pick an existing foundry project or confirm creating a new one — don't silently select.

Skip `azd provision` when the user gave you an existing `AZURE_AI_PROJECT_ENDPOINT` or `FOUNDRY_PROJECT_ENDPOINT` and the workflow only needs to deploy the agent into that project.

Run provision only for new projects or real infrastructure changes:

```bash
azd provision --no-prompt
```

> Optional: run `azd provision --preview --no-prompt` first to preview the resource changes (a what-if) before applying them.
>
> Optional: add `--no-state` on a fresh azd environment to skip the existing-deployment check and provision faster; omit it when re-provisioning an existing one.

What this does:

- Creates the Foundry project (if not present) and supporting resources under `infra/`.
- Creates connections declared as top-level `azure.ai.connection` services. `${PARAM_*}` placeholders resolve from the active azd env.
- Wires model deployments, AI Search, ACR, etc. `infra/layers/` provision in parallel when present.

This is a core `azd` command. Skip provision when the user gave you an existing `AZURE_AI_PROJECT_ENDPOINT` via `azd env set` -- the extension uses the existing project as-is.

After provision completes for a new project, run `azd env get-values` and set missing required azd env values, especially `AZURE_AI_PROJECT_ID` and `AZURE_TENANT_ID`, before local run or the first `azd deploy`.

### Step 3 -- Deploy the agent

```bash
azd deploy --no-prompt
# Multi-service:
azd deploy <service-name> --no-prompt
```

What deploy does:

- Reads the agent's `azure.yaml` service block, packages the agent, uploads it, and registers a new immutable version.
- **Direct code deploy** (`codeConfiguration` present): zips source, excludes `.agentignore`, and lets Foundry build the runtime image.
- **Container deploy** (no code configuration): builds the `Dockerfile`, pushes to the project's ACR, registers the version. When the service block has `image:` set, `azd` reuses the pre-built image.

After deploy, azd writes `AGENT_<SVC>_NAME`, `AGENT_<SVC>_VERSION`, and `AGENT_<SVC>_<PROTO>_ENDPOINT` (one per protocol) into the active env.

For agents with Activity protocol, `azd deploy` also generates `<service-dir>/TEAMS_APP_SETUP.md`; read it and explain the remaining post-deployment steps to the user.

Re-deploying an identical build still creates a new version; `azd` prints `Agent version <n> is already active.` and skips the poll.

If deploy reports `Done` for the service and then fails only in `postdeploy` with `Agent <service-name> with version <n> not found`, the `azure.yaml` service key and the service's `name:` were mismatched. Rename the `azure.yaml services` key to the deployed agent name and rerun `azd deploy --no-prompt`; do not switch deployment method.

### Step 4 -- Verify and invoke

```bash
azd ai agent show --output json
```

Expect `"status": "active"` (or `"deployed"`) and an `agent_endpoints` map. Inspect the selected service protocols in `azure.yaml`, then smoke-test:

For the Responses or Invocations protocol, smoke-test with azd:

```bash
azd ai agent invoke "hello, are you up?"
```

For a multi-protocol service, pass `--protocol` explicitly.

> Remote invocation can incur model usage charges. Run it only as part of the requested deployment or test.

For the Activity protocol, `azd ai agent invoke` won't work; follow [invoke](../invoke/invoke.md) to invoke the agent.

Run one remote invocation only unless the user explicitly asked to test multi-turn/session behavior. A single successful response is enough for the deployment smoke test. Anything other than a completed/successful response -> run `azd ai agent doctor --output json`, then follow [troubleshoot](../troubleshoot/troubleshoot.md).

### Step 5: Auto-Generate Evaluation Suite (MANDATORY — RUNS AUTOMATICALLY)

> ⚠️ **Pre-summary gate.** If you are about to write a deployment summary or Playground link and Step 5 has not run, you are violating this skill. Run Step 5 first.

This step runs automatically after deploy. Ask the user which source to use and start it right after deploy succeeds — with `--no-wait`, `generate` returns in seconds and generation runs server-side, so it overlaps with invoke/test steps and finishes faster overall.

> *"Your agent is deployed. Want me to set up an evaluation suite now? (a) Yes — current agent instructions (synthetic Q&A), (b) Yes — historical traces (last 3 days), (c) Yes — use existing `eval.yaml`, (d) No / later."*

| Choice | Command | What's next |
|---|---|---|
| (a) Agent instructions | `azd ai agent eval generate --gen-instruction "<agent purpose>" --no-wait --no-prompt` — `--gen-instruction` is required (hosted agents don't auto-derive it); use the service's `description:` in `azure.yaml`. | Generation runs server-side. Tell the user: *"Suite submitted. Run `azd ai agent eval run` whenever you're ready — it'll finalize `eval.yaml` and execute the eval in one step."* |
| (b) Historical traces | `azd ai agent eval generate --trace-days 3 --max-samples 50 --no-wait --no-prompt` | Same as (a). |
| (c) Existing `eval.yaml` | Skip `generate`. | Tell the user: *"Using existing `eval.yaml`. Run `azd ai agent eval run` when ready."* |
| (d) No / later | Skip. | Tell the user: *"You can run `azd ai agent eval generate` (and then `eval run`) anytime."* |

Other useful flags on `generate`: `--dataset <path-or-name>` to reuse an existing dataset instead of generating one, `--evaluator <name>` (repeatable) to pin built-in or custom evaluators, `--eval-model <name>` to choose the model used for generation and evaluation, `--reset-defaults` to overwrite an existing eval config, `--name <suite-name>` and `--out-file <path>` (default `eval.yaml`).

Then proceed to Step 6. See [After Deployment — Auto-Generate Evaluation Suite](#after-deployment--auto-generate-evaluation-suite) for run/refresh details. Run `azd ai agent eval run` only after the user explicitly agrees.

### Step 6 -- Hand off

- Send more messages -> [invoke](../invoke/invoke.md)
- Evaluate / optimize -> [observe](../observe/observe.md)
- Diagnose failures -> [troubleshoot](../troubleshoot/troubleshoot.md)
- Search traces / latency -> [trace](../trace/trace.md)

## `.agentignore`

`azd ai agent init` writes a default `<service-dir>/.agentignore` for code-deploy projects (gitignore syntax) that excludes tooling files, secrets, language artifacts, and Docker files from the deploy ZIP. Only the root file is read; use `!path` to force-include.

## Endpoint or card edits -- no new version

When only `agentEndpoint:` or `agentCard:` changed in the `azure.yaml` service block:

```bash
azd ai agent endpoint update          # patch in place
azd ai agent endpoint update --force  # skip confirmation for breaking changes
```

Idempotent.

## Multi-environment deploys

```bash
azd env list
azd env select prod
azd deploy --no-prompt
```

Each env has its own `AGENT_<SVC>_*` vars.

## Common failure modes -- Hosted

| Error | Fix |
|-------|-----|
| `missing_project_endpoint` | Run `azd env set AZURE_AI_PROJECT_ENDPOINT <url>`, or run `azd provision` for a new project. |
| `invalid_agent_manifest` | `azd ai agent doctor`; fix the named field. |
| `invalid_connection` | Inspect with `azd ai connection show <name>`. |
| Docker daemon not running | You are on the container path. Add/fix `codeConfiguration` and retry direct code deploy. Only install Docker or try remote image build if you specifically need container deploy. |
| ACR push 403 | Foundry project RBAC is missing `AcrPush` for your identity. Consider switching to direct code deployment to avoid ACR entirely. |
| `container registry endpoint not found` | ACR is not configured. Use `azd env set AZURE_CONTAINER_REGISTRY_ENDPOINT <url>`, or switch to direct code deployment. |
| Agent version poll times out | Build still running; retry `azd ai agent show` after a minute. |
| `session_not_ready` (424) | Cold start or readiness delay. Wait 15-30 seconds and retry. If persistent, use `1` CPU / `2Gi` memory minimum, verify the model deployment name, capability host, and agent identity role. |
| `invalid value "json" for --output` from `azd ai agent invoke` | Invoke supports only `default` and `raw` currently. Retry without `--output json`. |
| `could not resolve agent service in azd project: no azure.ai.agent service named '<agentName>' found in azure.yaml` from `azd ai agent invoke` | Name mismatch. Use the service name, update the `azure.yaml` service block, or use `--agent-endpoint` when invoking outside the project. |
| `subscription quota exceeded` | Ask user to request quota; do not auto-retry. |
| Bicep deploy errors | Forward `error.details[]` verbatim to the user. |
| `RoleAssignmentUpdateNotPermitted` during provision | A role assignment already exists but conflicts. Check for existing role assignments with `az role assignment list --scope <resource-scope>`. The provision may have succeeded for all resources except RBAC — verify with `azd ai project show` and manually assign the `Cognitive Services User` role to the agent identity if needed. |
| `eval generate`: `one of --gen-instruction ... is required` | Retry with `--gen-instruction "<agent purpose>"` (Step 5 option (a)). |
| `unknown command "init" for "azd ai agent eval"` | Command was renamed: use `azd ai agent eval generate` (requires azd CLI with `azure.ai.agents` extension up to date). |

For deeper logs, see [troubleshoot](../troubleshoot/troubleshoot.md).

## Workflow -- Prompt agent (MCP)

Prompt agents are not containerized -- they are a model + instructions + optional tools, created through the Foundry MCP server. Use when the user explicitly wants a prompt agent.

### MCP tools

| Tool | Purpose |
|------|---------|
| `agent_definition_schema_get` | Get the schema (`schemaType: "prompt"`). |
| `agent_update` | Create or update; supports `isCloneRequest` + `cloneTargetAgentName`. |
| `agent_get` | List or fetch one. |
| `agent_delete` | Delete an agent. |

### Steps

1. **Collect config** -- resolve endpoint from `azd env get-values` or ask. Then ask for **agent name**, **model deployment** (e.g. `gpt-4o`), and optional **instructions**, **temperature**, **tools**.
2. **Get schema** -- `agent_definition_schema_get` with `schemaType: "prompt"`.
3. **Create** -- `agent_update` with `{"kind": "prompt", "model": "<deployment>", "instructions": "...", "temperature": 0.7}`.
4. **Smoke test** -- follow [invoke](../invoke/invoke.md).
5. **Auto-generate evaluation suite** -- see [Step 5: Auto-Generate Evaluation Suite (Prompt)](#step-5-auto-generate-evaluation-suite-prompt-mandatory--runs-automatically) below.
6. **Hand off** -- evaluate via [observe](../observe/observe.md); clone via `agent_update` + `isCloneRequest`; delete via `agent_delete`.

### Step 5: Auto-Generate Evaluation Suite (Prompt) (MANDATORY — RUNS AUTOMATICALLY)

> ⚠️ **Pre-summary gate.** If you are about to write a deployment summary or Playground link and Step 5 has not run, you are violating this skill. Run Step 5 first.

This step runs automatically after deploy. Ask the user which source to use and start it right after deploy succeeds — with `--no-wait`, `generate` returns in seconds and generation runs server-side, so it overlaps with invoke/test steps and finishes faster overall.

> *"Your agent is deployed. Want me to set up an evaluation suite now? (a) Yes — current agent instructions (synthetic Q&A), (b) Yes — historical traces (last 3 days), (c) Yes — use existing `eval.yaml`, (d) No / later."*

| Choice | Command | What's next |
|---|---|---|
| (a) Agent instructions | `azd ai agent eval generate --gen-instruction "<agent purpose>" --no-wait --no-prompt` | Generation runs server-side. Tell the user: *"Suite submitted. Run `azd ai agent eval run` whenever you're ready — it'll finalize `eval.yaml` and execute the eval in one step."* |
| (b) Historical traces | `azd ai agent eval generate --trace-days 3 --max-samples 50 --no-wait --no-prompt` | Same as (a). |
| (c) Existing `eval.yaml` | Skip `generate`. | Tell the user: *"Using existing `eval.yaml`. Run `azd ai agent eval run` when ready."* |
| (d) No / later | Skip. | Tell the user: *"You can run `azd ai agent eval generate` (and then `eval run`) anytime."* |

## Common failure modes -- Prompt

| Error | Fix |
|-------|-----|
| Schema fetch failed | Verify endpoint format: `https://<resource>.services.ai.azure.com/api/projects/<project>`. |
| Agent creation failed | Use `agent_definition_schema_get` to verify the definition. |
| Permission denied | User needs `Foundry User` role on the project. |
| Model not found | Deploy the model first via [models/deploy-model](../../models/deploy-model/SKILL.md). |

## Display agent details (both flows)

After a successful deploy, show the agent's name, version, status, and endpoints in a table. Include a Playground link:

```
https://ai.azure.com/nextgen/r/{encodedSubId},{resourceGroup},,{accountName},{projectName}/build/agents/{agentName}/build?version={agentVersion}
```

`encodedSubId` is the subscription GUID as URL-safe base64 (no `=`):

```bash
python -c "import base64,uuid;print(base64.urlsafe_b64encode(uuid.UUID('<SUBSCRIPTION_ID>').bytes).rstrip(b'=').decode())"
```

For hosted agents, `playground_url` is in `azd ai agent show --output json`.

## After Deployment — Auto-Generate Evaluation Suite

> Reference for Step 5 options (a) and (b) — start `generate` right after deploy so its server-side generation overlaps with invoke/test steps and finishes faster. Options (c) and (d) skip `generate` and go straight to section 3 (run) or stop.

### 1. Inspect existing eval.yaml

Check the selected agent root for `eval.yaml`:

- **Exists and matches the selected agent** → skip `generate`; go to step 3 (run).
- **Missing or stale** → continue to step 2.

### 2. Submit generation (asynchronous, server-side)

Run `azd ai agent eval generate --no-wait` with the user's chosen flags (see the Step 5 table). The command:

- Submits dataset + evaluator generation jobs server-side.
- Returns in seconds.
- Writes pending operation IDs to local azd state.
- Writes a placeholder `eval.yaml` at the agent root (override with `--out-file <path>`).

No skill-side polling, terminal handle, or later-turn re-check is needed. `azd ai agent eval run` (section 3) automatically resumes a pending generation, downloads artifacts, finalizes `eval.yaml`, then runs the eval.

If the user wants to wait synchronously instead (e.g., to inspect `eval.yaml` before running), drop `--no-wait` — `generate` will then submit the jobs, wait for completion, download review artifacts, and write the finalized `eval.yaml` before returning (typically several minutes).

### 3. Run the suite

```bash
azd ai agent eval run
```

Use `azd ai agent eval show -O results.json` to inspect run details, or `azd ai agent eval list` to see history.

### 4. Refresh datasets/evaluators (later)

When local files under `datasets/<suite>/` or `evaluators/<suite>/` change, run `azd ai agent eval update --dataset-only` or `--evaluator-only` to upload new versions. azd bumps the `version` fields in `eval.yaml`.

### 5. Prompt User

*"Your agent is deployed and evaluation suite generation is **submitted server-side** (still running, takes several minutes). Would you like to run an evaluation now? `azd ai agent eval run` will wait for generation to finish, then execute the eval."*

- **Yes** → run `azd ai agent eval run` (this resumes the pending generation, then runs the eval — may take several minutes the first time), then follow the [observe skill](../observe/observe.md) to interpret results.
- **No** → stop. The user can return later via `azd ai agent eval run` — it will pick up wherever the pending generation is.
- **Production trace analysis** → follow the [trace skill](../trace/trace.md).

## Non-Interactive / YOLO Mode

> Even in `--no-prompt` / `--yolo` mode: if the user named a foundry project or asked to create one, go ahead; otherwise stop and ask before provisioning.

- Hosted: always pass `--no-prompt`.
- Prompt: all required values (project endpoint, agent name, model deployment) must come from the user message or `azd env get-values`; missing values should fail loudly rather than prompt.
