agentops-workflow

AgentOps Workflow

Help the user wire AgentOps into the release path so every candidate has a clear gate and proof pack. The default starting point is a PR eval gate. Full DEV/QA/PROD workflows are useful only after Azure auth, environments, and a real deployment owner are configured.

Pick the platform up front. AgentOps supports two:

• --platform github (default) - writes .github/workflows/*.yml using GitHub Actions. Auth via OIDC + GitHub Environments.
• --platform azure-devops - writes .azuredevops/pipelines/*.yml using Azure DevOps Pipelines. Auth via a Service Connection + a variable group named agentops.

The conceptual workflows are identical: one PR gate and optional deploy stages (dev/qa/prod). The PR and production templates already run agentops doctor --evidence-pack so reviewers get evidence.json and evidence.md in artifacts and, for GitHub Actions, in the run summary with the Doctor finding summary. A separate scheduled Doctor workflow is optional for periodic health checks, not the default release path.

For a new repository or tutorial, start with the PR gate only: agentops workflow generate --kinds pr. Generate DEV/QA/PROD deploy workflows only after environments, Azure auth, and real build/deploy commands are configured.

For copied accelerators or unfamiliar repos (for example GPT-RAG, Live Voice Practice, AI Landing Zone/Bicep-based apps), run agentops workflow analyze first and use the findings as the implementation plan before generating or editing workflows.

AgentOps reuses azd for app/infrastructure deployment when the repo already has an azd project, and stays Foundry-native for prompt-agent candidate workflows. Do not invent a parallel deployment system. AgentOps should gate quality and record proof; azd provision, azd deploy, azd hooks, Foundry Toolkit, the microsoft-foundry skill, and project tooling manage lifecycle actions.

For Foundry prompt-agent configs (agent: name:version), the generated eval gate should use AgentOps cloud eval in Foundry: a temporary cloud config plus agentops eval run, not the legacy official Action/task. Foundry still executes the managed eval; AgentOps enforces thresholds, writes results.json / report.md, and makes PR failures explainable in the summary.

Fast path - generated GitHub setup

Use this path when the user already generated GitHub workflows or asks to get the PR gate running. Stay local-first and deterministic; do not start by discovering the whole Azure subscription.

1. Inspect the repo before cloud discovery:
   • agentops init show --dir . without --reveal-secrets.
   • agentops.yaml.
   • .agentops/.env, plus .azure/config.json and active .azure/<env>/.env when the repo uses azd.
   • azd env get-values when azure.yaml exists and azd is available.
   • .github/workflows/agentops-*.yml.
2. Read the generated workflows to determine exactly which GitHub environments and variables are needed. For prompt-agent PR gates, pr uses environment: sandbox; deploy workflows use dev, qa, or production.
3. Treat these as GitHub Actions environments for OIDC and variables. sandbox points at the Foundry authoring project. dev points at the first shared post-merge project.
4. Proceed only when these values are known or deliberately chosen:
   • GitHub owner/repo.
   • workflow environment names from jobs.*.environment.
   • AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_SUBSCRIPTION_ID.
   • AZURE_AI_FOUNDRY_PROJECT_ENDPOINT.
   • AZURE_OPENAI_DEPLOYMENT.
   • optional APPLICATIONINSIGHTS_CONNECTION_STRING.
5. Prefer existing values and exact checks:
   • git remote get-url origin and gh repo view --json nameWithOwner.
   • git branch -vv to confirm the local trunk branch tracks origin/main when the tutorial uses trunk-based main.
   • gh variable list --env <env> and gh secret list --env <env>.
   • agentops init show, local .agentops/.env or .azure/<env>/.env, and azd env get-values values before az account show.
   • az account show only as a proposal for tenant/subscription; confirm before writing it to GitHub variables.
6. For GitHub OIDC, treat AZURE_TENANT_ID as the tenant that owns the app registration / federated credential, not merely the tenant associated with the subscription or a managedByTenants entry. Before writing AZURE_TENANT_ID, verify the chosen tenant can see the app registration and the exact federated credential:
   • az ad app show --id <AZURE_CLIENT_ID> in the active tenant, or an equivalent Microsoft Graph query scoped to the proposed tenant.
   • az ad app federated-credential list --id <AZURE_CLIENT_ID> and confirm the subject, issuer, and audiences. If the app is visible in one tenant but the Azure subscription is associated with another tenant, use the app/federated-credential tenant for AZURE_TENANT_ID; the subscription id remains AZURE_SUBSCRIPTION_ID. Do not copy a managedByTenants[*].tenantId value into GitHub variables unless the app and federated credential are verified there too.
7. When creating or connecting the GitHub remote for the prompt-agent tutorial, make sure the local trunk branch tracks the remote trunk before telling the user to continue:
   • If main is newly pushed, use git push -u origin main.
   • If origin/main already exists, use git branch --set-upstream-to=origin/main main.
   • Verify with git branch -vv; main must show [origin/main]. Without this, a later git pull on main can fetch but not update the local branch.
8. Copy CI variables from local AgentOps/azd configuration into the GitHub environment used by the workflow. Reuse local values for AZURE_AI_FOUNDRY_PROJECT_ENDPOINT, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT, and optional APPLICATIONINSIGHTS_CONNECTION_STRING instead of asking the user to type them again. Explain AZURE_OPENAI_DEPLOYMENT only if it is missing: it is the Azure OpenAI deployment used as the evaluator/judge model, not the user's agent.
9. For prompt-agent tutorials that use Foundry trace sampling / trace-to-dataset, verify observability RBAC before telling the user step 18 is ready:
   • Resolve the dev Foundry project managed identity principal id.
   • Resolve the connected Application Insights resource.
   • Grant or verify Reader on that Application Insights resource to the dev Foundry project managed identity.
   • If the App Insights component is workspace-based, also grant or verify Reader on the backing Log Analytics workspace. This is separate from GitHub OIDC and separate from the signed-in user's portal access. Operate dashboards can still render while trace-to-dataset fails if the project identity cannot read App Insights.
10. Do not enumerate subscriptions, Foundry projects, Azure OpenAI resources, or model deployments to guess missing values. If AZURE_SUBSCRIPTION_ID, AZURE_TENANT_ID, AZURE_AI_FOUNDRY_PROJECT_ENDPOINT, or AZURE_OPENAI_DEPLOYMENT is absent from AgentOps/azd/local env, ask the user to choose or provide it. Only run a scoped Azure query after the user confirms the subscription and the exact missing value.
11. For GitHub OIDC, derive the federated credential subject from the generated workflow. If the job has environment: dev, the subject is normally repo:<owner>/<repo>:environment:dev. Do not assume branch or pull_request subjects without reading the workflow.
12. Before triggering a Foundry prompt-agent workflow, make sure the OIDC app / service principal has two RBAC assignments. Both are required; the eval step fails silently (every metric returns null) if only one is in place.
13. Foundry User on the Foundry project (or the Foundry resource scope if that is the team's standard). Role id 53ca6127-db72-4b80-b1b0-d745d6d5456d (formerly Azure AI User). Without this the candidate-staging step fails on Microsoft.CognitiveServices/accounts/AIServices/agents/read.
14. Cognitive Services OpenAI User on the underlying Azure AI Services account that hosts the evaluator model deployment (typically the parent account of the Foundry project). Role id 5e0bd9bd-7b93-4f28-af87-19fc36ad61bd. Without this the Foundry azure_ai_evaluator graders fail with a 401 PermissionDenied on Microsoft.CognitiveServices/accounts/OpenAI/deployments/chat/completions/action and every metric comes back null in the cloud eval report. AgentOps now lifts that error into results.json and the orchestrator's "0 usable metric scores" warning so the cause is visible in CI logs, but the workflow still fails the gate. Grant this role before the first run. Azure Reader is not enough for either step.
15. If either RBAC assignment is missing, do not run the workflow yet. Show the exact GitHub OIDC client ID / service principal, desired role, target scope (project for Foundry User, AI Services account for Cognitive Services OpenAI User), then ask the user to approve the role assignment or get an Azure/Foundry admin to grant it. After assignment, read it back or ask the user to confirm before dispatching the workflow. When the user approves and you know the scopes, use the role ids to avoid rename drift:

• az ad sp show --id <AZURE_CLIENT_ID> --query id -o tsv
• az role assignment list --assignee <sp-object-id> --scope <foundry-scope> --include-inherited
• az role assignment create --assignee-object-id <sp-object-id> --assignee-principal-type ServicePrincipal --role 53ca6127-db72-4b80-b1b0-d745d6d5456d --scope <foundry-scope>
• az role assignment create --assignee-object-id <sp-object-id> --assignee-principal-type ServicePrincipal --role 5e0bd9bd-7b93-4f28-af87-19fc36ad61bd --scope <ai-services-account-scope> The AI Services account scope looks like /subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<ai-account-name> and can be derived from az cognitiveservices account list --resource-group <foundry-project-rg> --query "[?kind=='AIServices'].id" -o tsv.

14. Ask before creating or updating GitHub repos, GitHub environments, variables/secrets, Entra app registrations/service principals, federated credentials, managed identities, or Azure RBAC assignments.
15. When creating federated credentials from PowerShell, avoid fragile interpolation. Do not write "repo:$repo:environment:$envName" because $repo: can be parsed as a scoped variable. Use "repo:${repo}:environment:${envName}" or ("repo:{0}:environment:{1}" -f $repo, $envName), then build JSON from a PowerShell object with ConvertTo-Json.
16. After creating or updating a federated credential, read it back and verify before triggering a workflow:
    • subject exactly matches the generated workflow subject.
    • issuer is https://token.actions.githubusercontent.com.
    • audiences includes api://AzureADTokenExchange. If any value differs, fix the credential before running GitHub Actions.
17. After setting GitHub environment variables, read them back and verify AZURE_TENANT_ID still matches the app/federated-credential tenant before triggering a run. If azure/login fails with AADSTS53003, first re-check this tenant/app alignment before assuming Conditional Access is the root cause.
18. Do not dispatch gh workflow run as a surprise validation step. First show that the GitHub environment, variables/secrets, federated credential, and Foundry RBAC are ready, then ask the user before triggering workflows.
19. Avoid broad discovery unless local config is missing. Do not run broad az resource list, az graph query, SDK inspection, or web search to find the Foundry project when agentops init show, .agentops/.env, or .azure/<env>/.env already has AZURE_AI_FOUNDRY_PROJECT_ENDPOINT. If the endpoint is missing, say exactly what is missing and ask the user before scanning the subscription.

Branch model assumed

feature/* ── PR ──▶ develop                 [agentops-pr]          gate
                       │
                       └── merge ─▶ develop  [agentops-deploy-dev]  build + eval + deploy DEV
release/* ── push                            [agentops-deploy-qa]   build + eval + deploy QA
release/* ── PR ──▶ main                     [agentops-pr]          gate
                       │
                       └── merge ─▶ main     [agentops-deploy-prod] safety eval + build + deploy PROD

If the user is on trunk-based development, omit qa and release/** and have them generate --kinds pr,dev,prod.

The PR workflow uses the eval step as the hard merge gate. Doctor also runs there and writes release evidence; by default the Doctor step blocks the PR on critical findings such as regression detection (--severity-fail critical, the default behavior of agentops workflow generate --doctor-gate critical). This catches metric drops (for example groundedness going from 5.0 to 4.0) that would still pass the configured eval thresholds. To restore the pre-1.x advisory behavior — Doctor writes release evidence but does not block the PR — generate with --doctor-gate none. DEV/QA/PROD deploy workflows always keep Doctor as a critical release gate; the --doctor-gate flag only controls the PR template.

Step 0 - Prerequisites

1. pip install "agentops-accelerator @ git+https://github.com/Azure/agentops.git@main" if agentops is missing.
2. agentops eval analyze has been reviewed, agentops.yaml exists at the project root, and agentops eval run works locally.
3. The user's repo follows GitFlow (or is willing to). If not, ask which branches map to dev/qa/prod and adjust the triggers after generation.

Step 1 - Generate the workflows

First analyze the repo shape:

agentops workflow analyze
agentops workflow analyze --format markdown --out agentops-workflow-plan.md

Use the analysis to decide whether --deploy-mode auto is enough or whether you need to adapt placeholders/project-specific deployment. The analyzer is local-only and looks for azure.yaml, Bicep, AgentOps prompt-agent config, landing-zone manifests, private-network signals, Docker/Container Apps signals, and existing CI folders. Treat README matches as hints only; structural files drive the recommendation.

GitHub Actions (default):

agentops workflow generate --kinds pr
# or full scaffold:
agentops workflow generate --kinds pr,dev,qa,prod --force

Azure DevOps Pipelines:

agentops workflow generate --platform azure-devops --kinds pr
# or full scaffold:
agentops workflow generate --platform azure-devops --kinds pr,dev,qa,prod --force

The full scaffold writes:

Kind	GitHub Actions path	Azure DevOps path	Trigger	Environment
pr	.github/workflows/agentops-pr.yml	.azuredevops/pipelines/agentops-pr.yml	PRs to develop, release/**, main	sandbox for prompt-agent PR candidates, dev for generic PR gates
dev	.github/workflows/agentops-deploy-dev.yml	.azuredevops/pipelines/agentops-deploy-dev.yml	push to develop	dev
qa	.github/workflows/agentops-deploy-qa.yml	.azuredevops/pipelines/agentops-deploy-qa.yml	push to release/**	qa
prod	.github/workflows/agentops-deploy-prod.yml	.azuredevops/pipelines/agentops-deploy-prod.yml	push to main	production
doctor	.github/workflows/agentops-doctor.yml	.azuredevops/pipelines/agentops-doctor.yml	daily cron (06:00 UTC)	dev

PR and PROD workflows upload release evidence. Explain that this is a projection of existing eval/Doctor/Foundry/monitoring signals, not a separate exit-code contract. Generate the optional scheduled Doctor workflow only when the team explicitly wants periodic health-check artifacts outside PR/release events.

Useful flags:

• --platform github | azure-devops - pick the CI/CD platform.
• --force - overwrite existing workflow files.
• --kinds pr,dev,qa,prod - generate a subset. Prefer --kinds pr until deploy environments are configured.
• --kinds doctor - optional scheduled Doctor-only workflow for periodic checks. Do not use it as a substitute for the PR gate.
• --deploy-mode auto|placeholder|azd|prompt-agent - auto uses azd templates when azure.yaml exists, otherwise uses prompt-agent templates when agentops.yaml targets a Foundry prompt agent; azd forces azd provision / azd deploy; prompt-agent stages/evaluates a Foundry prompt candidate; placeholder keeps the generic stack-agnostic scaffold.
• --dir <path> - non-default repo root.

Step 2 - Configure environments and Azure auth

GitHub Actions

Read the generated workflow files and create only the GitHub Environments used by jobs.*.environment. For prompt-agent PR gates, create sandbox. For the full scaffold, create sandbox, dev, qa, and production.

• sandbox - no extra protection. Store the PR candidate endpoint and OIDC variables here when generated jobs use environment: sandbox.
• dev - no extra protection. Store the OIDC variables here when the generated jobs use environment: dev.
• qa - usually no required reviewers, but isolated variables for QA.
• production - set required reviewers, optional wait timer, optional deployment branch restriction to main, and production-specific variables.

Tell the user that environment-level variables override repository-level ones inside jobs that declare that environment.

Azure DevOps

In Pipelines → Environments, create three: dev, qa, production. On production, add a manual approval check (Approvals and checks → New check → Approvals).

In Pipelines → Library, create a variable group named agentops with these variables (mark sensitive ones as secret if needed):

• AZURE_AI_FOUNDRY_PROJECT_ENDPOINT
• AZURE_OPENAI_ENDPOINT
• AZURE_OPENAI_DEPLOYMENT
• APPLICATIONINSIGHTS_CONNECTION_STRING - optional fallback if the Foundry project's App Insights connection cannot be auto-discovered.

In Project settings → Service connections, create an Azure Resource Manager service connection named agentops-azure scoped to the subscription that hosts your Foundry project.

Grant the build service "Contribute to pull requests" permission on the repository (Project settings → Repositories → Security → Build Service) so the PR-comment step can post.

Step 3 - Configure Azure auth

GitHub Actions (OIDC)

At the GitHub Environment level when the workflow declares an environment (preferred for the tutorials), or at repository level when intentionally shared across environments, set:

• AZURE_CLIENT_ID - App registration / managed identity used for OIDC.
• AZURE_TENANT_ID
• AZURE_SUBSCRIPTION_ID
• AZURE_AI_FOUNDRY_PROJECT_ENDPOINT - Foundry project URL used by the eval step.
• AZURE_OPENAI_DEPLOYMENT - existing Azure OpenAI deployment used as the evaluator/judge model. Reuse the local AgentOps/azd value when available.
• APPLICATIONINSIGHTS_CONNECTION_STRING - optional fallback as a variable or secret. Generated workflows first try to auto-discover App Insights from the Foundry project endpoint; this value makes eval and Doctor telemetry explicit.

For Foundry prompt-agent projects that use trace sampling or Create dataset → From traces, also verify the Foundry project managed identity can read telemetry: grant or verify Reader on the connected Application Insights resource, and on the backing Log Analytics workspace when the App Insights component is workspace-based. This permission is not covered by the GitHub OIDC service principal roles above.

Then configure Workload Identity Federation on the Azure side (federated-credentials on the app registration) for each branch / environment the workflows will run from. See docs/ci-github-actions.md for the exact az commands.

Also grant the same app registration / service principal two Azure RBAC roles before the first workflow run; both are required and the eval step fails silently (every metric returns null) if only one is in place:

1. Foundry User on the Foundry project or Foundry resource. The PR gate uses Foundry data-plane APIs to read prompt agents; Azure Reader only proves ARM access and will still fail the eval step with Microsoft.CognitiveServices/accounts/AIServices/agents/read.
2. Cognitive Services OpenAI User on the underlying Azure AI Services account that hosts the evaluator model deployment. Without this, Foundry azure_ai_evaluator graders fail with a 401 PermissionDenied on the OpenAI chat/completions/action data action and every metric returns null in the cloud eval report. AgentOps surfaces that error in results.json and the orchestrator's "0 usable metric scores" warning, but the workflow still fails the gate — fix the role before the run.

Tell the user that CI evals emit agentops.eval.* telemetry and scheduled Doctor runs emit agentops.agent.finding.* telemetry when App Insights is configured or auto-discovered. The Cockpit uses those signals for Azure Monitor deep links.

Azure DevOps (Service Connection)

Already done in Step 2 - the agentops-azure service connection handles auth. Make sure the underlying service principal or managed identity has both the Foundry User role on the Foundry project (or Foundry resource) and the Cognitive Services OpenAI User role on the underlying Azure AI Services account that hosts the evaluator model. Both are required; without the OpenAI User role the Foundry graders fail with a 401 PermissionDenied and every cloud eval metric returns null.

Step 4 - Use azd for deployment

If the repo already has azure.yaml, generate azd-backed deployment workflows:

agentops workflow generate --kinds pr,dev,qa,prod --deploy-mode azd --force

The deploy workflows will:

1. run azd env new ... || azd env select ... in CI;
2. run azd provision --no-prompt for DEV by default;
3. run azd provision --no-prompt for QA/PROD only when manually requested (provision=true in GitHub Actions or RUN_AZD_PROVISION=true in Azure DevOps);
4. run agentops eval run as the quality/safety gate;
5. run azd env refresh on the deploy runner so a fresh CI workspace can recover outputs from the previous infrastructure provision;
6. run azd deploy --no-prompt.

Set AZURE_ENV_NAME per GitHub Environment / Azure DevOps variable group if the user's azd env names are not exactly dev, qa, and production. Set AZURE_LOCATION when the azd template needs an explicit region.

If the user asks for "zero-trust deployment"

Do not replicate azd. Do this instead:

1. Inspect the app and ask only for missing critical choices (region, target host, private networking yes/no if not obvious).
2. Prefer an existing azd template or AVM-backed template that already implements managed identity, RBAC-only data access, private endpoints where required, and no secrets in source.
3. Create or adapt azure.yaml, infra/, and azd-native hooks declared in azure.yaml (preprovision, postprovision, predeploy, postdeploy) as needed.
4. Run azd provision to validate the infrastructure path.
5. Re-run agentops workflow generate --deploy-mode azd --force so CI delegates provision/deploy to azd.

Never call ad-hoc hook scripts from the workflow (for example ./agentops/deploy.sh or ./.azd/hooks/*). If custom behavior is needed, put it behind azd's native hook mechanism in azure.yaml.

Copied accelerators / AI Landing Zone apps

For Azure AI accelerators copied from templates, use AgentOps to make the landing-zone path actionable:

1. AgentOps handles eval gates, Doctor, reports, Cockpit readiness, and the workflow guardrails around deployment.
2. Foundry manages hosted agents, prompt-agent versions, evaluations, traces, monitoring, datasets, and operations.
3. azd/Bicep/AILZ manages app and infrastructure deploy when azure.yaml or infra/*.bicep exists.
4. Project-specific steps such as indexing, data seeding, model deployment, container build/push, App Config updates, or private-network post-provision work stay in azd hooks or existing project tooling.

If scripts/Invoke-PreflightChecks.ps1 exists, keep it in the deployment path: AgentOps-generated azd workflows run it with -Strict before azd provision. Doctor surfaces the same path as AI Landing Zone deployment readiness, with evidence for preflight, agentops.yaml, azd workflow coverage, network isolation, and the private runner path.

If agentops workflow analyze reports network isolation, private endpoints, jumpbox/Bastion, Azure Firewall, or ACR Tasks, do not assume GitHub-hosted runners can deploy everything. Plan self-hosted runner, jumpbox handoff, or ACR Tasks agent-pool execution before enabling DEV/QA/PROD deploy stages.

If azure.yaml is missing and the user is not asking to create the deployment assets yet, check whether this is a Foundry prompt agent. If agentops.yaml has agent: "name:version", prefer prompt-agent mode:

agentops workflow generate --kinds pr,dev,qa,prod --deploy-mode prompt-agent --force

Prompt-agent workflows:

1. read prompt_file from agentops.yaml or AGENTOPS_AGENT_PROMPT_FILE;
2. look up the seed agent in the active environment's Foundry project; if it does not exist (typical first deploy into dev / qa / prod), read the optional prompt_agent_bootstrap block from agentops.yaml (required model, optional description, model_parameters, tools) plus prompt_file and create the first version automatically (recorded as action: "bootstrapped");
3. otherwise create or reuse a candidate Foundry prompt-agent version from prompt_file;
4. generate .agentops/deployments/agentops.candidate.yaml;
5. run agentops eval run against the candidate version;
6. record .agentops/deployments/foundry-agent.json as a deployment artifact only when the gate passes.

This avoids the bad pattern of evaluating one agent version and deploying a different prompt. The invariant is: evaluated version == deployed version. Foundry manages agent versions; AgentOps enforces the repo-side gate and deployment record. For multi-environment prompt-agent workflows (sandbox → dev → qa → prod), strongly recommend adding the prompt_agent_bootstrap block so operators do not have to manually recreate the seed agent in every Foundry project.

If this is not a Foundry prompt agent and azd is not ready, generate --kinds pr only or use --deploy-mode placeholder. Do not ship DEV/QA/PROD workflows that pretend deployment is wired.

Step 5 - Branch protection

In Settings → Branches, add a rule for both develop and main:

• Require a pull request before merging.
• Require status checks to pass: select AgentOps PR / Eval (PR gate) (the job name from agentops-pr.yml).
• Optional: require linear history.

This makes the eval gate a hard merge requirement.

Step 6 - Iterate

Common follow-ups:

• Tighten thresholds for QA/PROD - copy agentops.yaml to agentops-qa.yaml / agentops-prod.yaml and tighten the thresholds: block. Point each workflow at its own config via the inputs.config default.
• Scheduled runs - add a schedule: entry in agentops-pr.yml (or a new agentops-nightly.yml) to evaluate against main nightly.
• Matrix per scenario - if the user has multiple AgentOps config files, extend the eval job with strategy.matrix.config: and reference ${{ matrix.config }}.
• Regression baseline - wire the deploy templates to download the previous run's results.json artifact and call agentops eval run --baseline <results.json>.

Guardrails

• Do not invent CLI flags. The supported workflow analyze flags are --dir, --format, and --out. The supported workflow generate flags are --force, --dir, --kinds, --platform, --deploy-mode, and --doctor-gate.
• Do not push DEV/QA/PROD deploy workflows with placeholder Build/Deploy steps or missing OIDC variables; generate PR-only first.
• Do not create parallel workflow files. Prefer editing the generated ones.
• Do not auto-fill app/infrastructure deployment with raw Azure CLI steps that bypass azd. AgentOps gates; azd provisions and deploys. For Foundry prompt agents, use --deploy-mode prompt-agent so the workflow calls the Foundry SDK and evaluates the candidate version before marking it deployed.
• Do not use AgentOps workflows to create or deploy Foundry Hosted Agents. Use Foundry Toolkit / the microsoft-foundry skill / the app's azd path, then point AgentOps at the deployed URL for gates and evidence.
• The four workflow names (agentops-pr, agentops-deploy-dev, agentops-deploy-qa, agentops-deploy-prod) are fixed - don't rename them or branch-protection wiring will break.