The six generations of AI agents and how to eval them

In the evolution of AI agent architectures, the journey from simple prompt-based systems to sophisticated harnessed agents reflects significant advancements in model capabilities and evaluation strategies. Initially, AI agents operated through single prompts, providing basic responses without context or memory. As capabilities progressed, agents developed structured chains and ReAct loops, allowing for dynamic tool usage and iterative decision-making. Evaluations evolved from simple answer-quality assessments to complex trace evaluations, considering tool selection, cost, and safety. Modern agents integrate workflows with deterministic controls for reliability, while the latest generation utilizes harnesses to manage peripherals like memory and sandboxes, enhancing flexibility and capability. Evaluation strategies have become layered, incorporating offline tests, simulations, replays, and online scoring to ensure agents perform effectively and safely in dynamic environments. This iterative approach underscores the importance of continuous evaluation to adapt to real-world challenges, enabling AI agents to transition from basic functionalities to comprehensive incident response systems like Sentinel.

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