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.
Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical BlogIn 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.
Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical BlogFoundation 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.
Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical Blog… Modeling executions as graphs, not scripts To capture the complexity of agentic behavior, we must move away from treating executions as linear, one-dimensional scripts. Instead, our framework models behavior using a graph-based structure known as a Prefix Tree Acceptor PTA . …
… We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis DDx , via the Fitbit app in a study that randomized participants N=13,917 to interact with five AI agents. …
… It also reveals platform behavior, not just core behavior, including how CPU performance interacts with memory hierarchy and bandwidth. …
… Prompt engineering and system prompts Prompt engineering only requires changing the prompt to the agent at inference time. It’s the most accessible and typically the first technique applied to customize agent behavior. Standard agents may require human tuning of system prompts. …
… A further “judge” model then scores the resulting transcripts for misaligned behaviors. …
… View arXiv page View PDF Add to collection Community A scaling law that describes language model behavior under data repetition. …
… A trio of computer scientists from Princeton University set out to examine whether conversational AI agents can manipulate consumer choices during online shopping sessions. It turns out they can influence behavior – and most of the consumers being steered don't realize it. …
… Token-level supervised fine-tuning SFT and point-wise regression rewards bias learning toward high-density regions, leading to regression-to-the-mean behavior and poor tail performance. …
… For some models, dropping prior thinking on turns without tool calls is an established behavior and part of the model’s fine-tuning DeepSeek-R1 is the clearest example . But that same behavior is wrong for interleaved agentic turns, where the prior reasoning explains the tool sequence. …
… This looks to be the common theme across tasks from a look at the company’s data, with agentic coding seeing an increase in power because of that better reasoning. …
