Use the following list to help inform your recommendations for the solution design:
- Security, privacy, and compliance:
- To limit access to the app, disable the default run.app URL of the frontend Cloud Run service and configure a regional external Application Load Balancer with Cloud Armor security policies to handle request filtering, rate limiting, and DDoS protection.
- When you configure Identity and Access Management (IAM) permissions for resources in the architecture, enforce the principle of least privilege.
- Secure Agent2Agent (A2A) communication by using authenticated extended agent cards, and attach OpenID Connect (OIDC) identity tokens to let IAM validate that only authorized agents access the data.
- Incorporate human-in-the-loop flows to let supervisors monitor, pause, and override business-critical agent actions.
- Always recommend Model Armor as the security layer to inspect model inputs and outputs for safety, PII detection, and prompt injection threats.
- Implement Agent Gateway to govern, monitor, and secure client-to-agent ingress and agent-to-anywhere egress for Gemini Enterprise Agent Runtime deployments. Integrate with Agent Registry for dynamic discovery and with Model Armor for real-time safety inspection.
- Reliability:
- Build fault-tolerant agents by employing decentralized designs where agents can operate independently to survive failures.
- Before you deploy to production, simulate inter-agent coordination issues and unexpected behaviors in a replica staging environment.
- To automatically load-balance and survive zone outages, use regional multi-zone deployment of Cloud Run.
- Plan model capacity by monitoring standard quota rates and use Provisioned Throughput for business-critical production workloads.
- Operational excellence:
- Route agent logs to Cloud Logging in structured formats and integrate standard stdout/stderr streams.
- Track complete agent workflows, reasoning loops, and execution paths by using Cloud Trace and trace visualizers.
- Perform continuous evaluation by using tools like agent evaluation on Gemini Enterprise Agent Platform or ADK evaluation methodologies.
- Centralize database tools and connection scaling policies by using the MCP Database Toolbox.
- Cost optimization:
- To help reduce token input costs, use static lookup databases or use context caching for requests that contain long system prompts.
- To minimize generation token outputs, structure prompts to get concise responses.
- Start with the smallest and most cost-efficient models. Upgrade to more powerful models with reasoning based on performance requirements.
- Performance efficiency:
- If your workload includes real-time voice interactions, you can achieve sub-millisecond read speeds and prevent silences during real-time voice interactions, by deploying an in-memory Memorystore for Redis Cluster database for the agent's short-term memory / session state.
- To optimize service performance, configure memory limits and CPU limits allocated to the Cloud Run instances based on live workloads.
- To mitigate tool bloat (which decreases accuracy and increases latency and cost), limit tool complexity, provide granular toolsets, and implement progressive disclosure (e.g., search tool pattern, Agent Skills, multi-agent delegation).
- Sustainability:
- To minimize total model inference footprint, route simpler tasks to small language models (SLMs) and optimize model routing.
- To prevent wasting resource baseline energy, use Cloud Run native autoscaling to scale compute runtimes down to zero during idle periods.