As you manage a fleet of agents, you cannot manually review every output. Platforms like HyperAgent use "Rubrics"—an evaluation framework where one LLM judges another's work against predefined criteria. This automates quality control, which is essential for scaling an agent-first business.

To get an objective critique of AI-generated content, use a dedicated 'reviewer' sub-agent. This separates the drafting and evaluation processes, preventing the original agent from being biased by its own creation and ensuring a higher quality output.

Treating AI evaluation like a final exam is a mistake. For critical enterprise systems, evaluations should be embedded at every step of an agent's workflow (e.g., after planning, before action). This is akin to unit testing in classic software development and is essential for building trustworthy, production-ready agents.

Instead of manually refining a complex prompt, create a process where an AI agent evaluates its own output. By providing a framework for self-critique, including quantitative scores and qualitative reasoning, the AI can iteratively enhance its own system instructions and achieve a much stronger result.

Move beyond manual agent improvement by creating an automated loop. In this process, an agent runs, its performance is evaluated, failures are identified, and another process suggests and implements code fixes. This creates a foundation for self-improving systems.

Notion treats its entire evaluation process as a coding agent problem. The system is designed for an agent to download a dataset, run an eval, identify a failure, debug the issue, and implement a fix, all within an automated loop. This turns quality assurance into a meta-problem for agents to solve.

As AI agents generate vast amounts of output, human review becomes an impossible bottleneck. The solution emerging is multi-agent systems where a separate 'grading agent' automatically scores and requests revisions on an agent's work against a predefined rubric, as seen in Anthropic's 'Outcomes' feature, enabling scalable quality assurance.

To get the best results from an AI agent, provide it with a mechanism to verify its own output. For coding, this means letting it run tests or see a rendered webpage. This feedback loop is crucial, like allowing a painter to see their canvas instead of working blindfolded.

An agent's effectiveness is limited by its ability to validate its own output. By building in rigorous, continuous validation—using linters, tests, and even visual QA via browser dev tools—the agent follows a 'measure twice, cut once' principle, leading to much higher quality results than agents that simply generate and iterate.