Imagine kicking off a new project. Immediately, the coordination dance begins: checking schedules with the project manager, brainstorming technical needs with an analyst, mocking up designs with a graphic designer, and getting a final architectural blueprint from a technical architect. It’s a complex, multi-step process that relies on different experts working in harmony.
AI has already offered us assistants, tools that can answer a question or complete a single task. But what if AI could form the entire project team? What if you could assemble a group of specialized AI agents, give them a goal, and watch them collaborate to deliver a complete project proposal?
This isn’t science fiction. This is the new frontier of agentic AI, made possible by frameworks like the Google Agent Development Kit (ADK). To see what this looks like in practice, we need to look no further than an inspiring experiment by our expert partners at Raccoons.
The Problem: The Limits of a Single AI Assistant
Traditional LLM’s are great at one thing: responding to a user’s prompt. They can retrieve information or generate content based on a request. But they lack three critical capabilities for complex business processes:
- Specialized Roles: A single chatbot doesn’t have the distinct expertise of a project manager versus a technical architect.
- Tool Usage: They can talk, but they typically can’t do. They can’t search the web, create a document, or generate an image on their own.
- Collaboration: They don’t talk to each other. They can’t hand off tasks or build upon each other’s work to complete a multi-stage project.
This is why they can’t manage a project from start to finish. For that, you need a team.
The Solution: Building an AI Team with the Google ADK
The Google Agent Development Kit (ADK) is a framework designed to solve exactly this problem. It’s not about building a single, all-knowing bot. It’s about creating a collection of distinct, specialized AI agents that can work together, each with its own purpose, personality, and set of tools.
This is the key difference: we’re moving from a single AI assistant to a fully functional AI team.
A Glimpse into the Future: The Raccoons Experiment
Our partners at Raccoons recently put this to the test by creating a team of “agentic twins” on Slack, each modeled after a real team member. In their insightful article, they detail how they built a team consisting of:
- Hannah, the Project Manager: To route questions, create to-do lists, and send emails.
- Jens, the Technical Analyst: To use Google Search for research and ask clarifying questions.
- Aagje, the Graphic Designer: To use an image generation tool for project visuals.
- Tom, the Resource Manager: To manage schedules and assess project risks.
- Dries, the Technical Architect: To design the final system architecture.
When given a simple project brief in a Slack channel, the team springs into action. They collaborate in real-time, asking each other questions, providing analysis, generating visuals, and handing off tasks. The result? In under ten minutes, they produce a comprehensive, five-page project proposal in a Word document, ready to be sent to the client.
This experiment brilliantly showcases what makes the ADK so powerful. You can read the full, fascinating story of their experiment on their blog here.
How It Works: The Three Pillars of a Modern AI Agent
So, how do you build an agent like Hannah or Jens? Each agent created with the ADK is built from three essential components:
- An LLM (The Brain): This is the core language model that provides reasoning capabilities. The Raccoons team used Google’s Gemini 2.5 Pro, but the framework is flexible enough to work with others.
- Instructions (The Playbook): This is the crucial part. Through carefully crafted prompts, you define each agent’s role, personality, expertise, and “rules of engagement.” This is where you shape an agent to be a meticulous analyst versus a creative designer.
- Tools (The Hands): This is what separates these agents from simple chatbots. You give them access to real software tools—like Google Search, email clients, or image generators—allowing them to move from just talking to actually doing.
Why This Matters for Your Business
Watching agents chat in Slack is fascinating, but the business implications are profound. This approach offers:
- Fully Traceable Work: Every step, decision, and piece of reasoning is visible in the conversation log. There are no black boxes.
- Scalable Teamwork: Need another analyst for a big project? You can simply spin up another “Jens” agent. The team is modular and can be expanded on demand.
- True Collaboration: The agents aren’t just responding to a user; they are genuinely collaborating with each other, passing context and tasks back and forth like a real team.
This is the next evolution of automation. It’s about tackling full-scale, multi-step projects independently, freeing up your human teams to focus on strategy, creativity, and the most complex challenges.
