Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my!

Emerging AI-development terminology often describes established concepts reframed for agent-based workflows, including loop engineering, which creates repeatable, scheduled systems for agents rather than one-off prompts, and Ralph loops, which repeatedly direct an agent through a task until completion but may consume substantial compute. Squads and fleets refer to multi-agent arrangements that divide work among specialized roles or run tasks in parallel, while harness engineering focuses on the tools, permissions, context, memory, and orchestration that make models useful in practical software environments. Hill climbing describes iteratively improving agents and their surrounding systems through evaluation and feedback, and forward-deployed engineers are customer-facing technical specialists who help organizations implement AI solutions. The distinctions between closed, open-weight, and open-source models concern how much access users receive to a model’s weights, code, data, and training process. Although the terminology is evolving, the central concerns remain building reliable workflows, validating results, determining appropriate human oversight, and improving systems over time.

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