💡 Skim the phase labels first, drill in wherever you need deeper context.
This checklist is a reflection of the journey to be made from concept through to live operations.
It's a human-readable distillation of the automation framework demonstrating a operational service incorporating Machine Learning (image processing) and Generative AI tooling (MCP service).
Continuous Delivery for Success:
This governance framework serves as a blueprint for a reusable continuous delivery pipeline that delivers production-grade value.
Leaders
Align these phases with your governance and sign-off steps.
Engineers
Treat this as a outline for a ready-made launch runbook.
Ops & SRE
Focus on observability, access, and rollback hooks.
- Verify target environments are approved for deployment and mapped to the correct access policies. For your image-background-removal MCP service, confirm the staging environment is cleared to process sample product photos before any deployment runs.
- Assign a dedicated service identity with scoped credentials, runtime directories, and session variables. Create an "image-bg-remover" service identity that can read from the product-image store but cannot see customer PII.
- Stand up the core delivery folders (configuration, automation tasks, templates, assets, documentation) for the service. Bootstrap a repo layout where `/config`, `/automation`, and `/assets` hold prompt templates, test product photos, and model parameters for background removal.
- Accept flexible host targeting parameters (single or comma-separated) and normalize them for orchestration. Translate `--targets image-bg-staging-1,image-bg-staging-2` into a structured list that your deployment runner can iterate over.
- Implement pre-flight validation that halts execution on unapproved environments. Stop the rollout if someone tries to deploy the background-removal service to a production region that has not yet passed legal and privacy review.
- Establish shared facts/variables for the service identity, runtime environment, and diagnostics tooling. Define values like `SERVICE_ID=image-bg-remover`, `IMAGE_BUCKET=product-photos`, and `DIAG_ENDPOINT=/bg-remove/health` once and reuse them across tasks.
- Provision the service identity automatically if it does not exist. If the "image-bg-remover" identity is missing in the EU staging environment, create it as part of the first rollout there.
- Run privileged tasks under the orchestrator while delegating service logic to the dedicated identity. Let the orchestrator open network routes to the image store, then switch context so the image-bg-remover identity deploys and runs the service.
- Document all tunable settings—ports, diagnostics switches, logging levels, cache locations, feature toggles. Record that the `/bg-remove` endpoint has a default max image size and that verbose logging stays off for production traffic.
- Describe common storage paths (code, assets, temporary caches) and how they can be overridden per environment. Note that cached segmentation masks live on fast local storage in staging but move to encrypted storage for production catalog images.
- Declare default asset sources (internal bundle, control node, remote artifact, checksum) for reproducibility. Specify that the background-removal model is pulled from an internal model registry by default, with a clear override for experimental versions.
- Create service directories with least-privilege permissions owned by the service identity. Ensure only the image-bg-remover identity can write into the folder where processed PNGs with transparent backgrounds are stored.
- Synchronize application code, dependency descriptors, and configuration files into the runtime workspace. Push the current code revision for the background-removal pipeline plus its dependency lockfiles into the build workspace before creating artifacts.
- Stage large models or datasets using cache-first logic and checksum verification. Download the latest segmentation model and a small reference set of product photos once per region and reuse them for subsequent builds.
- Template the container or runtime specification and build the image/package with your chosen tooling. Generate a runtime specification that can switch between CPU-only and GPU-accelerated background removal based on an environment flag.
- Deploy service unit definitions, reload the process supervisor, and enable long-running services. After updating the background-removal service definition, restart the supervising process so the new version of `/bg-remove` is actually active.
- Clean up transient build artifacts left on orchestration nodes. Remove temporary archives of training images and models from your orchestration node once the service has been deployed.
- Gate optional secrets or credential tasks behind explicit toggles. Only roll out the keys for accessing your high-resolution product-image store when the deployment is explicitly tagged as including secrets.
- Ensure container/runtime descriptors install dependencies, copy staged assets, and expose the correct endpoints. Confirm the runtime config installs the image-processing libraries, copies the background-removal model, and exposes `/bg-remove` and `/bg-remove/health` endpoints.
- Maintain consistent naming and labeling conventions for primary services, sidecars, and supporting processes. Adopt names like `commerce-bg-remover-api` and `commerce-bg-remover-metrics` so dashboards and alerts clearly group the image service components.
- Include optional logging or telemetry sidecars when observability requires separate pipelines. Attach a lightweight metrics companion that records how many images had backgrounds removed and at what latency, but only in observability-ready environments.
- Capture architecture diagrams, asset handling steps, and operator prompts in a dedicated docs space. Maintain an internal knowledge-base page showing how a content editor uploads a product photo, how the MCP background-removal service processes it, and how the result flows back into the CMS.
- List operational commands for start/stop, log access, health checks, and troubleshooting. Document the command sequence that lets support engineers rerun the background-removal pipeline on a single product image when a customer spots an issue.
- Provide LLM-friendly briefs so automation assistants follow the same conventions. Share a short prompt template for internal assistants that explains how to safely trigger the image-background-removal pipeline and where to store outputs.
- Execute dry-run deployments in a staging environment to validate builds and runtime configuration. Promote a candidate version of the background-removal service to staging and run a test that strips backgrounds from a batch of representative product photos.
- Confirm background services are healthy through the platform’s status tooling. Use the platform’s status views to ensure the image-processing service, storage adapters, and any caching layer all report healthy before launch.
- Stream logs from the service identity to verify normal behavior under load. Stream logs from the image-bg-remover identity during a synthetic campaign that uploads many images, watching for errors and regressions.
- Run service smoke tests or invoke exposed tools to ensure the agent-facing surface is functional. Trigger an automated smoke test that uploads a sample product image, verifies the subject is preserved, the background is transparent, and the response time meets the target.
Background-removal MCP service in practice
The examples in this checklist are drawn from a working MCP service that processes photos with complex scenes to remove backgrounds. The result is clean, black-background cut-outs optimised for machine learning classification, cataloguing, and marketing.
This MVP captures a specific slice of the project lifecycle. It is a functional proof of concept where the architecture does more than just govern development—it 'bakes in' infrastructure for autonomous coding agents. While the current image functions are intentionally basic, the next slice in this series demonstrates the autonomous development of simple features, like background transparency, and more challanging service orientated features, where the disticntion between reach and grasp becomes interesting.
Service Flow
- Content editors or automation flows submit source images through an MCP-compatible tool.
- The service strips away backgrounds while preserving the product subject and key lighting.
- Processed images are stored back into the organisation's content system for human review and publication.