Deployment¶
Deploy your agent to a development environment or production with a CI/CD pipeline.

Infrastructure vs Deployment¶
Infrastructure (agents-cli infra) provisions the cloud resources your agent needs — service accounts, IAM bindings, APIs, telemetry buckets, and Terraform state. It sets the stage but doesn't run your agent.
Deployment (agents-cli deploy) takes your agent code and puts it on the provisioned infrastructure — building a container, pushing it to a registry, and starting the service.
The typical flow: provision infrastructure first, then deploy on top of it.
Deploy to a Dev Environment¶
The simplest path to a running deployment:
1. Set your dev project:
2. Deploy the agent:
The command reads your deployment_target from pyproject.toml and dispatches to the right flow.
Tip
To enable observability features (prompt-response logging, content logs), run agents-cli infra single-project after deploying. Terraform provisions the telemetry resources and updates your service to use them. See the Observability Guide for details.
Verify it works:
Deployment Targets¶
Agent Runtime¶
Fully managed — no containers or infrastructure to manage:
Check on an async deployment:
agents-cli deploy --no-wait # Start and return immediately
agents-cli deploy --status # Check progress later
Cloud Run¶
Builds a container from source and deploys as a Cloud Run service:
Override resource limits or pass extra gcloud flags:
agents-cli deploy --memory 8Gi --port 8080
agents-cli deploy -- --min-instances=1 --max-instances=10
Deploy a pre-built image instead of building from source:
GKE¶
Deploys to a GKE cluster using Terraform and kubectl:
Next Steps¶
- CI/CD & Production — set up automated pipelines for staging and production
- Observability — monitor your deployed agent