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…Tracing helps visualize each step your agent takes, which makes it easier to debug and optimize your agent’s behavior. In the workshop, you can optionally configure the LANGSMITH_API_KEY and…
…When deferred initialization is disabled (the default), each operator’s device-setup work runs inline in the constructor — preserving the original behavior. When enabled, the constructor only runs the model-reading phase…
…Cross-language comparisons can be challenging. We use several techniques to ensure the comparisons are reliable. Similar sized changes: Rust and C++ have similar functionality density, though Rust is slightly denser. This…
…The baseline formulation models manufacturing yield as a function of average defect density, component area, and clustering behavior. IDP V6 extends this legacy model by introducing an information-loss correction layer and…
…As the Vitis tool chain moves toward deeper AI-agent integration, structured prompting combined with domain-specific agents or skills backed by an HLS knowledge base can guide LLMs toward more reliable…
…Disabling Google Play Protect , Android’s built-in security protection, that is on by default and continuously scans for malicious app behavior, no matter the download source. Sideloading an app for the…
…Communication backend comparison Each configuration was evaluated with two communication backends: NCCL (baseline) NVSHMEM-enabled implementation Measurements: TFLOP/s per device : GPU computational throughput Step time (seconds) : Time per training iteration Speedup…
…Even Windows mode on energy saver is not immune to this behavior. These figures were recorded in the fixed benchmark Siege - Part I, with the following settings: 1200P FSR 4.0.2…
…By comparison, the older SADP process only has two groups, core and boundary [5]. Figure 2. Separation of SAQP-generated features into core (C), shell (S) and boundary (B) categories. The green…
…That is quite a comparison. What’d you make of that comparison? I think that’s a paraphrase. The Steve Jobs part wasn’t part of the quote. But there’s an…
