📆 Two months into the Berkeley MOOC on Agentic AI — still going strong!

🔗 Join the MOOC

For the past two months, I’ve been immersed in the Agentic AI MOOC, learning from leaders at OpenAI, DeepMind, Microsoft, and startups like Sierra. The course is ongoing, and it’s been incredible to explore how large language models evolve into multi-agent systems with real-world utility.

As part of the AgentX-AgentBeats track, I am designing a green-agent-powered evaluation system to test the agentification of the Design2Code framework, a visual-to-code pipeline that translates design images into responsive HTML/CSS and JavaScript.

🧠 Agent Architecture: Green & White

In AgentBeats, task evaluation is split between two agent roles:

• 🟢 The green agent acts as the host and evaluator. It loads tasks (e.g., screenshot-to-code examples from Design2Code), prepares the environment, assigns each task to a participant, and then validates the output.
• ⚪ The white agent is the participant. It receives the task and performs the required operation; in this case, generating HTML/CSS code based on the input screenshot.
• 📊 After execution, the green agent scores the output using predefined evaluation logic (e.g., checking visual fidelity or code structure) and reports results to the platform.

This structure not only modularizes task execution and verification, but also makes it easier to simulate noisy conditions, benchmark against new datasets, or swap out white agent strategies.

💡 Lessons from the Real World

The MOOC lectures offered practical insights into building and evaluating reliable agentic systems:

• τ2-Bench (Sierra) emphasized the need for dual-control testing setups, reproducible tasks, and robust metrics like pass@k for real-world reliability.
• From SWE-bench Verified, I learned that curating and verifying datasets (even if it reduces their size) can dramatically improve their benchmarking value. They removed about a third of the original examples to create a more reliable and verifiable testbed.
• Verifiable agents (NVIDIA) are aligned not just with user intent but with environment-grounded correctness — scored by unit tests or DOM state comparisons, not human judgment.
• Good evaluations require not just coverage, but verifier quality, task separability, and diversity to truly measure intelligence.

🔭 Next Steps

As the MOOC continues, I plan to:

• ✅ Complete my green agent, with robust task orchestration, metric evaluation, and environment management.
• 🧠 Implement multi-turn dialogue capabilities in my white agent, allowing it to ask clarifying questions, just like a collaborative frontend developer.
• 🟣 I’m also excited to see what kind of purple agent we’ll be implementing. Its role hasn’t been fully defined yet, but early discussions suggest it may introduce new coordination or creative capabilities across agents.

---

Agentic AI is evolving from “prompt-and-pray” to full-stack, multi-agent systems with reflexivity and autonomy. Building a test harness like this showed me what’s required to go from demos to dependable AI systems.

🔗 AgentBeats Track

#AgenticAI #LLMAgents #AgentBeats #Design2Code #FrontendEngineering #WebAutomation #GreenAgent #WhiteAgent #TauBench #SierraAI #MOOC