--- season: 1 season_name: "Season 1 β€” December 2025 (Winter Holiday Edition)" day: 25 title: "πŸŽ‰ Grand Finale: Mission Accomplished!" summary: "Congratulations! You've successfully completed the 25-day Advent of Agents journey." tags: ["Architecture", "Blueprint", "Production", "Summary", "Agent Engine", "ADK", "Gemini 3", "Multi-agent"] canonical_url: "https://adventofagents.com/2025/12/25" markdown_url: "https://adventofagents.com/2025/12/25.md" video_url: "https://www.youtube.com/embed/AuE-w-z9lks" --- # πŸŽ‰ Day 25: πŸŽ‰ Grand Finale: Mission Accomplished! > **Season 1 β€” December 2025 (Winter Holiday Edition)** Β· [Interactive Web View](https://adventofagents.com/2025/12/25?utm_source=adventofagents&utm_medium=markdown&utm_campaign=adventofagents_s1_2025&utm_content=day25) Β· [Raw Markdown](https://adventofagents.com/2025/12/25.md?utm_source=adventofagents&utm_medium=markdown&utm_campaign=adventofagents_s1_2025&utm_content=day25) **Summary:** Congratulations! You've successfully completed the 25-day Advent of Agents journey. **Day 25 of Google's Advent of Agents** **πŸ”₯ Vertex AI Agent Designer is NOW in Agent Builder!** We often debate "low-code speed" versus "code control" when developing agents. The new Agent Designer aims to bridge this gap. This week, Vertex AI introduced Agent Designer in Agent Builder. It's a low-code visual interface that allows you to orchestrate agents and subagents on a canvas, then export the logic directly to the Agent Development Kit (ADK) for code-level refinement. - πŸ–±οΈ Visual Flow: Design agents, subagents, and control logic visually. - πŸ•ΉοΈ Playground: A chat interface to test agentic flow - ⬇️ Code Export: The "Get code" feature lets you scaffold in the UI and easily transition to your IDE (Python ADK). - πŸ› οΈ Native Tools: Pre-configured with Google Search, URL context, and Vertex AI Search (RAG) integration. πŸ” - πŸ”Œ MCP Support: Experimental connectivity to Model Context Protocol servers. ⚠️ Vertex AI Agent Designer is in preview. With MCP auth limitations and a lack of support for advanced ADK patterns, it is clearly a "V1". However, the visual-to-code workflow and potential integration with the Vertex AI Agent platform look very promising. ---- **🎊 You did it! ** Over the last 25 days, you've explored the cutting edge of AI Agents, from building your first agents with Gemini to some of the latest capabilities and showcasing design principles around compiled context views, memory, security, how we have simplified many aspects around deployment, observability, and interoperability. Your dedication to learning and building puts you at the forefront of this AI revolution. **The Stack, Phase by Phase:** - **Phase 1: The Modern Foundation (Days 1-7)** πŸ—οΈ We ditched the boilerplate. We used **YAML** configuration for clean logic, Agent Engine for source-based deployment (no more pickling!), and turned on Otel observability to trace every thought. We gave agents the power to write and fix their own code with **Code Execution**. - **Phase 2: The Cognitive Engine (Days 8-13)** 🧠 We moved beyond simple chat. We engineered **Context Layers** to manage memory, used **Time Travel** to fix mistakes without restarting, and unlocked Gemini 3 with the Live API for real-time multimodal interaction. We connected it all to the real world using **Managed MCP** for Google Maps and BigQuery. - **Phase 3: The Multi-Agent Mesh (Days 14-21)** 🌐 We stopped building lonely chatbots. We implemented the **Agent-to-Agent (A2A)** protocol, allowing agents to discover each other via the **Gemini Enterprise Registry.** We built **A2UI** so agents could generate their own interfaces on the fly and used **A2A Extensions** to pass secure passports between services. - **Phase 4: Enterprise Grade (Days 22-24)** πŸ›‘οΈ We hardened the stack. We replaced "trust" with **Model Armor** security layers. We made our agents unkillable with **Durable Execution (Restate)**, ensuring they survive server crashes and wait days for human approval. The **Advent of Agents** calendar is now complete, but your build has just begun. The daily drops are done, but the repository is yours. Every template, every snippet, and every guide remains available here. Thank you for being part of this experience. Happy holidays and happy coding! πŸš€ ## Code & Commands ```bash uvx agent-starter-pack create my-agent --adk ``` ```bash uvx agent-starter-pack create deep-search -a adk@deep-search ``` ```bash git clone https://github.com/google/adk-samples.git cd adk-samples/python/agents/retail-ai-location-strategy cp .env.example .env # Edit .env with your keys: # GOOGLE_API_KEY=your_ai_studio_key # GOOGLE_GENAI_USE_VERTEXAI=FALSE # MAPS_API_KEY=your_maps_key # Install & Run make install && make dev ``` ## Resources & Links - **[Agent Designer](https://docs.cloud.google.com/agent-builder/agent-designer?utm_source=adventofagents&utm_medium=markdown&utm_campaign=adventofagents_s1_2025&utm_content=day25)** β€” Agent Designer is a low-code visual designer that lets you design and test agents in the Google Cloud console. You can experiment with your agent in Agent Designer before transitioning development to code using Agent Development Kit. - **[Kaggle 5-day Intensive Course on Agents](https://www.kaggle.com/learn-guide/5-day-agents)** β€” The 5-Day AI Agents Intensive course is now a self-paced learning guide. This course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Each day blends conceptual deep dives with hands-on examples, codelabs, and live discussions. - **[Agent Development Kit (ADK) on GitHub](https://github.com/google/adk-samples?utm_source=adventofagents&utm_medium=markdown&utm_campaign=adventofagents_s1_2025&utm_content=day25)** β€” Explore more samples and contribute to the ADK community. - **[Retail AI Location Strategy: Autonomous Site Selection & Market Analysis](https://github.com/google/adk-samples/tree/main/python/agents/retail-ai-location-strategy?utm_source=adventofagents&utm_medium=markdown&utm_campaign=adventofagents_s1_2025&utm_content=day25)** β€” Explore a fully built case study on using AI for autonomous site selection and market analysis in retail.