AI agents powered by large language models (LLMs) have evolved beyond simple FAQ chatbots. They are becoming sophisticated digital teammates capable of planning, reasoning, and taking action, even incorporating feedback.
Thanks to advanced reasoning AI models, agents can learn critical thinking skills necessary to tackle complex tasks. This new class of “reasoning agents” can break down difficult problems, evaluate options, and make informed decisions efficiently, using only the necessary compute and tokens.
Reasoning agents are making significant impacts across various industries where multi-factor decisions are crucial, including customer service, healthcare, manufacturing, and financial services.
Reasoning On vs. Reasoning Off
Modern AI agents have the ability to toggle reasoning capabilities on and off. This allows them to manage compute resources and token usage efficiently.
A full chain-of-thought reasoning process can demand significantly more compute and tokens than a quick, single-shot response. Therefore, reasoning should be employed only when required. This can be compared to using high beams on a car only when driving in the dark.
Single-shot responses are suitable for straightforward queries like checking an order status or resetting a password. Reasoning becomes essential for complex, multi-step tasks such as reconciling financial schedules or managing intricate event seating arrangements.
New NVIDIA Llama Nemotron models incorporate advanced reasoning features with a simple system-prompt flag, allowing developers to programmatically control reasoning per query. This ensures agents use reasoning only when needed, reducing wait times and costs.
Reasoning AI Agents in Action
Reasoning AI agents are already driving complex problem-solving across numerous sectors:
- Healthcare: Improving diagnostics and treatment planning.
- Customer Service: Automating and personalizing complex interactions, from resolving disputes to recommending products.
- Finance: Autonomously analyzing market data and developing investment strategies.
- Logistics and Supply Chain: Optimizing routes, managing disruptions, and simulating scenarios to anticipate risks.
- Robotics: Powering warehouse robots and autonomous vehicles for planning, adaptation, and safe navigation in dynamic environments.
Many organizations are already seeing enhanced workflows and benefits from adopting reasoning agents. Companies like Amdocs, EY, and SAP are leveraging reasoning-powered agents to transform customer engagement, improve response quality in tax queries, and interpret complex user requests for autonomous business processes.
Designing an AI Reasoning Agent
Building an effective AI agent requires several key components, including tools, memory, and planning modules. These elements enable agents to interact with the environment, create and execute plans, and operate with a degree of autonomy.
Reasoning capabilities can be integrated into AI agents at different stages of development. A common approach is to enhance planning modules with powerful reasoning models such as Llama Nemotron Ultra or DeepSeek-R1. This dedicated reasoning effort during the planning phase directly improves the overall performance and outcomes of agentic systems.
Resources like the AI-Q NVIDIA AI Blueprint and the NVIDIA Agent Intelligence toolkit can assist enterprises in developing, streamlining, and optimizing agentic AI performance at scale. The AI-Q blueprint provides a reference workflow, integrating NVIDIA technologies like NeMo Retriever and NIM microservices. The open-source NVIDIA Agent Intelligence toolkit facilitates connectivity between agents, tools, and data, offering system traceability and performance profiling.
Build and Test Reasoning Agents With Llama Nemotron
Explore Llama Nemotron models, which have achieved top rankings in industry benchmarks for complex science, coding, and math tasks. Engage with the community shaping the future of agentic, reasoning-powered AI.
You can also use the open Llama Nemotron post-training dataset to customize reasoning agents and experiment with toggling reasoning to optimize for cost and performance. Additionally, test NIM-powered agentic workflows, including retrieval-augmented generation and the NVIDIA AI Blueprint for video search and summarization, to quickly prototype and deploy advanced AI solutions.
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