Custom AI agent development: plan, build, and ship

Custom AI agent development often fails due to process-related challenges such as unclear scope, lack of testing strategies, and inadequate deployment planning rather than technological issues. Successful development involves a structured cycle of scoping, prototyping, evaluating, iterating, deploying, and monitoring, which can reduce the high abandonment rates of AI projects. Platforms like CodeWords facilitate this process by offering tools such as conversational development through Cody, built-in large language model (LLM) access, ephemeral sandboxes, and serverless deployment, thus compressing the development cycle. The focus is on making quick decisions regarding infrastructure and integrations, while building custom logic that leverages domain expertise. Effective testing involves deterministic, scenario, and adversarial tests to ensure reliability before production. A healthy iteration cycle requires continuous observation, evaluation, and adjustment, highlighting the importance of process discipline over mere technological capability in shipping reliable AI agents.

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