The real talk on agent evaluation

Key Concepts

• Agents (as programs with loops)
• Model selection (e.g., Gemini 2.5 Pro)
• Tools for agents (e.g., grounding, long-term storage)
• Vibe coding (rapid front-end development)
• Agentic evaluation (measuring model performance over time)
• Feedback loops (connecting evaluation metrics back to the agent)
• Model, Reasoning, Tools (layers to evaluate within an agent)

Building Agents for Software Engineers

• The "A-ha" Moment: The key to building relatable agents for software engineers is to start with a manageable number of components. Kris Overholt suggests selecting two or three components initially, such as a good model (like Gemini 2.5 Pro), grounding, and long-term storage.
• Proof of Concept: By combining these components, developers can quickly create a proof of concept to demonstrate the system's capabilities. This allows for iterative development, adding more components as needed.
• Example: Overholt's "Hello World" agent was designed with long-term memory and the ability to Google information, reflecting his own work habits.

Defining an Agent

• Agent as a Program: An agent is essentially a program, likely containing loops and control flow. This definition avoids getting bogged down in more complex concepts like goals.

Vibe Coding

• Definition: Vibe coding refers to rapidly developing the front end of an application, potentially using tools like Gemini 2.5 Pro.
• Implementation: Overholt suggests using a strong back end that can handle a single request and then vibe coding the front end.
• Application: Jason Davenport mentions using Veo for on-demand learning, which aligns with the concept of vibe coding.

Agentic Evaluation

• Purpose: Agentic evaluation is the process of measuring how well a model performs over time and determining whether changes are beneficial or detrimental.
• Metrics: The evaluation should be based on specific metrics, even if they are not perfect. The key is to choose something measurable.
• Feedback Loops: Connecting the evaluation feedback loop back to the agent is crucial. Without this connection, the agent operates without guidance.
• Layers of Evaluation: Agent evaluation requires assessing the underlying layers: model, reasoning, and tools.

Synthesis/Conclusion

The discussion emphasizes a practical approach to building and evaluating AI agents, particularly for software engineers. It advocates for starting with simple, manageable components, focusing on measurable metrics for evaluation, and establishing feedback loops to guide the agent's development. The concept of "vibe coding" highlights the potential for rapid front-end development in agent-based applications. The key takeaway is that agent development should be iterative and data-driven, with a focus on continuous improvement through evaluation and feedback.