Creating multi-agent systems

TL;DR: This video demonstrates building a multi-agent system with a planner, an evaluator sub‑agent, and a simulator agent that work together to plan and simulate a marathon, using dynamic UI generation (A-to-I), an agent‑to‑agent protocol (A2A), and an agent registry for automatic discovery. ## From Fragile Loops to a Network of Experts The video shows how to move away from brittle, unpredictable agent loops toward a system of rigorously evaluated expert agents that can even build their own user interface. Three agents work together: - **Planner**: generates possible routes for a marathon. - **Evaluator sub‑agent**: judges routes based on a set of chosen criteria. - **Simulator**: collaborates with the planner, takes approved routes, runs them, and shows results. The example prompt: “Plan a marathon for 10,000 runners in Las Vegas.” The planner now does two new things: it performs real‑time evaluation during the planning process (preventing the agent from going off‑track) and dynamically generates a user interface while it runs, providing instant evaluation feedback. ## Building the Real‑Time Evaluator Sub‑Agent The evaluator uses its own separate model and a limited context window solely for scoring routes. It focuses only on grading the route plan, not the entire workflow. Whenever a new plan is created or an existing one modified, the planner calls the evaluator as a tool. ### Criteria Checked The evaluator checks both non‑deterministic standards (community impact, alignment with the prompt) and deterministic ones. For example: - Route length must be exactly **26 miles 385 yards** (or **42.195 km** for those using metric). - “42 sounds a lot farther” – a light‑hearted exchange about the difference. ## Dynamic User Interface with A-to-I To show evaluation results without writing custom code or building dashboards, the system uses **A-to-I** (Agent‑to‑Interface), an open standard created by Google. The planner instructs the evaluator to generate the UI components needed to display the route on a map. A one‑shot 80‑UI example is fed to Gemini so it knows how to render its interface. A-to-I empowers agents to create dynamic, expressive interfaces, breaking free from large blocks of text. The agent designs and builds exactly the interface it needs using a common design language. ## Connecting Agents: A2A Protocol and Agent Registry To link the planner with the simulator, two additional components are introduced: ### Agent‑to‑Agent Protocol (A2A) A2A is a protocol created by Google and contributed to the Linux Foundation. It eliminates fragile API code that usually connects agents. Each agent exposes an **agent card** – a manifest of the agent’s capabilities. The simulator provides its agent card so other agents can read it. ### Agent Registry Think of the agent registry as the DNS of your “agent internet.” It’s a central directory that resolves each agent’s identity and maps the specific skill sets across the agent network. When agents are deployed to an agent runtime, they automatically register themselves in the agent registry. Now the planner and simulator are connected with zero code or API contracts. They can plan and run a new simulation seamlessly. ## Running the Simulation Back in the user interface, the planner consults the evaluator and the simulator to craft the plan. The user clicks “Run Simulation,” and a new simulation is generated. Runners can be tracked as they start moving. ### How the Simulator Works - Configures environmental parameters. - Generates each runner as an independent agent session. - Monitors traffic issues and reports results to the planner. Runner behavior is modeled using **Gemini Deep Research** to learn and implement real‑world running patterns. For instance: “78% of marathon runners slow down slightly in the second half.” ## Summary of Achievements - **Real‑time evaluation**: keeps agents on track. - **Dynamic user interface**: agents render their own UI. - **Automatic connection**: using A2A and the agent registry. The agents now handle the creative work of planning, evaluating, and simulating a marathon. Source: [Creating multi‑agent systems](https://www.youtube.com/watch?v=c7yL_EduH9o)