Agentic Inference

Agentic inference is the process by which a large language model (LLM) executes multi-step tasks with varying degrees of autonomy—calling external tools, retrieving data, and self-correcting outputs across multiple inference cycles. Unlike traditional single-shot inference, agentic inference loops the model against its own tool outputs until a goal is met, which changes session length and cost characteristics.

Traditional inference is simple: you send a prompt, and the model returns text. Agentic inference adds a loop on top of that:

1. Observe: The model receives a goal and some context, such as tools available, current state of the world, etc.
2. Think: It reasons about what to do next.
3. Act: It calls a tool or takes an action.
4. Update: The result of that action is fed back in as new context.
5. Repeat: The process continues until the goal is achieved or the model decides it's done.

Agentic inference is a fundamentally different workload profile than AI chatbots, copilots, content generation, or translation apps. Agentic AI and long-running autonomous agents—such as those created with tools like Hermes Agents, LangChain Deep Agents, or OpenClaw—can generate up to 15x more tokens at inference, creating demand on the AI infrastructure to produce the lowest-cost tokens. Infrastructure designed for stateless, sub-second requests cannot efficiently serve agentic sessions: KV cache memory fills up, GPU utilization collapses during tool-call wait periods, and traditional capacity planning—built around requests-per-second—dramatically underestimates how many GPUs a real agentic fleet requires.

Traditional inference capacity planning rests on three inputs: average tokens per request, average latency target, and peak requests-per-second. These inputs collapse entirely for long-running autonomous agent frameworks. A single agent task may involve 50–500 sequential model invocations, and context windows grow at every step. Agents block on tool calls, creating irregular GPU idle windows that waste capacity. Speculative reasoning loops can spike token generation rates by 5x before dropping to baseline.

See below examples of agentic inference use cases in action and their technical implementation.

See how organizations across industries are putting agentic inference into production today.

Coding assistants are the most mature instance of long-running autonomous AI agents that highlight the importance of agentic inference. Agents can take a GitHub issue, explore a codebase, write a fix, run tests, and open a pull request, all without human involvement at each step. Tools like Claude Code, Devin, and GitHub Copilot Workspace operate this way. The key is the agent can iterate: If the tests fail, it reads the error and tries again.

These agents reason over data sources (databases, data lakes, or APIs), then decide which tools to call to write and execute SQL or Python against live data. They inspect results and notice anomalies, then generate visuals, summaries, and written reports. They can then use reasoning to modify the downstream data pipeline based on the results they find.

These AI agents monitor systems, investigate alerts, pull logs, correlate events across sources, and decide whether something is a real threat. They can then either remediate the issue automatically or escalate it with a full summary. Large enterprises are building varying types of security agents into their security protocols.

Think of AI agents that handle support tickets from the first customer input to resolution. They can read the issue, look up the customer's account in a CRM, check order status in a backend system, draft a reply, and close the ticket. Unlike chatbots, these agents take actions across multiple systems, rather than simply generating text.

An agent's core is a simple loop, but a production system is built in layers—grounding it in real data, coordinating multiple agents, adding safety, and serving it efficiently at scale. Most teams build in this order.

 1. Start with the loop: 

An agent repeats a cycle: reason about a goal, call a tool, read the result, and continue until the task is done. Standing one up takes three pieces—a model to do the reasoning, a set of tools described as JSON schemas, and orchestration code to run the loop. 

Choose a model built for multi-step reasoning rather than chat; NVIDIA Nemotron™ models are designed for this and run as hosted NVIDIA NIM endpoints you can call before deploying any infrastructure. For the loop itself, use an existing agent framework instead of building your own: the open source NVIDIA NeMo Agent Toolkit handles the wiring and works with frameworks like LangChain, CrewAI, and LlamaIndex.

2. Ground it in your data: 

On its own, an agent only knows what its model learned in training. An open model can be post-trained on domain data using training tools such as NVIDIA NeMo to better handle domain terminology, response formats, workflows, and tool-use patterns for specific tasks. Post-training improves model behavior, but it is not a substitute for connecting the agent to current enterprise facts. Connecting the agentic system to your documents, databases, and APIs through a retrieval-augmented generation (RAG) pipeline helps it reason through enterprise-specific information. 

In RAG, sources are chunked, embedded, and stored in a vector database that the agent searches at each step. This also keeps the context window in check, since the agent retrieves only what each step needs. NVIDIA NeMo Retriever and GPU-accelerated vector search (cuVS) handle retrieval at scale, and the Build an Enterprise RAG Pipeline blueprint provides a working pipeline to start from.

3. Grow into multiple agents: 

Most real tasks outgrow a single agent. A common pattern uses a supervisor that splits a goal into subtasks and assigns each to a specialized agent—one to search, one to write code, another to draft a summary—then combines the results.

Capability goes up, and so do the number of concurrent sessions and the compute behind them. The NeMo Agent Toolkit is built for this composition, and the Build an AI Agent for Enterprise Research (AI-Q) blueprint is an open, multi-agent reference you can adapt.

4. Make it production-ready: 

Three additions separate a demo from a deployable system. Guardrails keep the agent from taking harmful or irreversible actions on its own—NVIDIA NeMo Guardrails lets you define those rules and route risky actions to a human for approval. Observability traces every reasoning step, tool call, and token so you can see where latency and cost accumulate; it's built into the NeMo Agent Toolkit. Evaluation scores the agent against known tasks so regressions surface early. All three are far easier to build in from the start than to retrofit.

5. Know what runs underneath: 

The agent you build and the stack that serves it are distinct. Your code sends a request; a serving layer turns it into tokens on GPUs. A chatbot makes one short request, but an agent makes dozens or hundreds per session. Its context grows every turn, and the GPU idles during tool calls—a profile standard inference servers weren't built for, and where most of the cost lands. NVIDIA Dynamo is an open source serving framework designed for it: It reuses each session's computed context (the KV cache) across turns, holds it resident through tool-call pauses, and lets parallel sub-agents share what they have in common. You won't need it for a first prototype, but it shapes infrastructure decisions early on.

Start building:

• Build an AI Agent for Enterprise Research (AI-Q): a full multi-agent system to start from.
• Building RAG Agents with LLMs: a free, self-paced course.