Understanding AI Workflow Concepts
Recent developments in AI have introduced a range of new terminology, often used to describe patterns and systems around agent-based workflows. These terms, such as loop engineering and harness engineering, are increasingly relevant for engineers building and operating AI-powered applications. The goal is to move beyond simple, one-off prompts to create repeatable, scalable systems.
Loop Engineering focuses on designing systems around agents that can execute tasks repeatedly. A ‘loop’ might involve an agent fetching issues, summarizing them, and proposing fixes, validated and escalated as needed. This approach contrasts with manually prompting agents for each task individually. Ralph loops represent a brute-force implementation of this concept, where an agent repeatedly attempts a task until it succeeds, but can be inefficient.
Harness Engineering refers to the surrounding infrastructure that supports a model, including tools, permissions, and context. GitHub Copilot serves as an example, connecting models to codebases and editors. The term highlights the importance of designing robust systems around models to ensure reliable operation.
Squads and Fleets describe how multiple agents can collaborate within a workflow. A ‘squad’ represents a team of agents with distinct roles, while a ‘fleet’ allows parallel execution of agents. This approach enables specialization and parallelization, improving efficiency and reducing the load on individual agents.
Hill Climbing describes the iterative process of improving agents and their harnesses through feedback and evaluation. This involves measuring agent output, adjusting the system to optimize performance, and refining the agent’s skills. The core idea is continuous improvement through data-driven adjustments.
Source: https://github.blog/ai-and-ml/decoding-the-new-ai-lingo-loops-harnesses-squads-hill-climbing-oh-my/
