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Agent customization techniques span from simple prompt changes to advanced techniques like reinforcement learning (RL), each with tradeoffs in cost, complexity, and capability. The best approach depends on whether you need better information, instructions, or fundamentally more reliable behavior. The following sections cover the main approaches.

In practice, the most effective agent customization combines multiple techniques in sequence. The stages of a representative pipeline are outlined below.  Start with system prompts, tool and skill definitions, and retrieval to establish baseline behavior. 

Foundation models come with broad language and reasoning capabilities across use cases and modalities based on the training datasets used. Models understand language and can follow instructions, but specialized workflows often require context that is restricted, specialized, or proprietary. Customizing an agent solves this challenge by shaping how the agent reasons under constraints, which tools it selects, how it structures its outputs, and how reliably it executes domain workflows.