Agentic AI is artificial intelligence that can pursue a goal on its own, without requiring a human to direct every move. Their key differentiator is that agency: Unlike a traditional chatbot, agents can leverage user-approved tools and systems to identify a solution to a problem, decide what steps to take, act on those decisions, and adjust course when things change.
An agentic AI system doesn't wait for a new prompt to take each next step. It's an end-to-end solution that, when properly implemented and monitored, can free up time for human coworkers and make independent choices that can further improve efficiency.
How Agentic AI Works
How Agentic AI Works
How Agentic AI Works
How Agentic AI Works
How Agentic AI Works
How Agentic AI Works
An agentic AI system runs a continuous loop:
- An agentic AI system runs a continuous loop:
- An agentic AI system runs a continuous loop:
- An agentic AI system runs a continuous loop:
01 Perceive
The agent takes in information such as a user message, a document, a database query, or even a live conversation, and turns that input into workable material.
02 Reason
The agent interprets the input in the context of what it was built to do — leveraging its memory of past chats, the instructions it's been given at the system level, and goals stated by the user more broadly.
03 Plan
The AI decides what to do next and sets an end-to-end strategy — which tools to call, what action to take, and what questions to ask before jumping in.
04 Act
The agent executes by triggering a workflow and generating a response. This could be a web search, reviewing a document, or other workflows to understand the problem and provide a solution.
05 Adapt
Based on the results of its actions, the AI updates its understanding and continues toward the goal.
This loop runs continuously within a session. In more sophisticated systems, like agents built on the Napster Omniagent API, it also carries forward across sessions, so the agent can pick up where it left off with a returning user.
How Agentic AI Works
Not every AI assistant qualifies. Three things distinguish an agentic system from a standard AI tool:
Persistent goal orientation
The agent keeps track of an objective across multiple steps, not just a single response.
Tool use
The agent can take actions — calling an API, querying a database, triggering a system function — rather than just generating text based on its foundational model.
Adaptive behavior
The agent responds to new information mid-task, rather than following a rigid script.
A system can have some of these properties without being fully agentic. A simple chatbot that uses a search tool is moving in this direction. An agent that maintains a conversation goal across a phone call, a website visit, and a follow-up email — with memory of all three — is operating at a meaningfully higher level.
Agentic AI vs. Generative AI
Generative AI creates content in response to a prompt. The interaction is essentially a one-shot, even when you ask for multiple outputs. It completes the task but doesn't build off of that session in any meaningful way.
Agentic AI uses generative capabilities as one component of a larger system. The language model becomes a reasoning engine inside a broader architecture that includes memory, tools, and an ongoing goal. Generation is a means to an end, not the end itself.
Generative AI answers questions.
Agentic AI completes tasks.
Agentic AI in Practice
The clearest examples of agentic AI are systems that do something on behalf of a user without the user having to micromanage every step.
A hotel concierge agent that handles check-in questions, makes restaurant recommendations, and processes requests is agentic. It holds the goal of helping the guest, uses tools to access reservation systems, and adapts based on what the guest says.
A sales agent that qualifies inbound leads over the phone, updates a CRM with the outcome, and routes promising contacts to a human sales rep is agentic. It's doing a job, not answering a question.
Napster Station, Napster's AI concierge deployed at events and high-traffic venues, runs on this architecture. It engages guests in real-time conversation, uses tools to retrieve venue information, and maintains context across an interaction without human coordination.
For developers and product teams, agentic AI represents a shift in what's buildable. The question stops being "what can the model say?" and starts being "what can the agent do?" Agents that work across communication channels, remember users between sessions, connect to real backend systems, and handle complex multi-step interactions are now within reach of any team with access to the right API.
Start building: Napster Omniagent API →
WebRTC (Web Real-Time Communication) is an open standard that enables live audio and video communication directly in a web browser. It is the infrastructure that makes browser-based video calls, voice conversations, and live streaming possible without plugins, downloads, or a separate application.
For AI applications, WebRTC is the channel that enables real-time voice and video interaction between a user and an AI agent. It is what allows an agentic AI to speak, listen, and appear on screen simultaneously inside an ordinary web page.
How does WebRTC work?
WebRTC establishes a direct peer-to-peer connection between two endpoints, typically a browser and a server, and streams audio and video data between them in real time. The key properties that make it useful for AI:
Low latency. WebRTC is optimized for real-time communication. Delays that would make a recorded video unwatchable make a live conversation unusable. Well-implemented WebRTC connections for AI agents, like those built with the Napster Omniagent API, achieve response latencies around 300ms, which is below the threshold where humans perceive a noticeable pause.
Bidirectional audio and video. WebRTC carries both audio and video simultaneously, in both directions. The agent hears the user; the user sees and hears the agent. This is what enables real-time embodied AI video agents.
Browser-native. WebRTC runs in Chrome, Firefox, Safari, and Edge with no additional software. A user on any device with a browser can have a full voice-and-video interaction with an AI agent without installing anything.
Framework-agnostic. On the developer side, WebRTC integrations work with React, Vue, Angular, and vanilla JavaScript. The Napster Omniagent API's Web SDK ships with full TypeScript support and can be mounted into any DOM container.
What's the difference between WebRTC and WebSockets for AI?
WebRTC and WebSockets are both real-time communication protocols, but they serve different purposes in AI applications:
WebRTC carries audio and video. It is the right choice when the user interface includes a visible agent with voice for embodied interaction. It requires a browser environment.
WebSockets carry audio only. They are the right choice for server-side integrations, headless clients, or voice-only experiences where video is not needed – for example, phone-like interactions embedded in a custom application.
The Napster Omniagent API supports both, along with SIP for traditional phone calls. The same agent definition with the same persona, knowledge, tools, and memory runs across all three channels. The channel is a deployment choice; the agent itself is channel-agnostic.
Why does WebRTC matter for AI deployment?
WebRTC matters for enterprise AI deployments for one practical reason: It removes the friction between a user and an AI interaction. A customer lands on a web page and is immediately in a conversation with an AI agent, without any downloads or account creation. That immediacy, combined with the interactivity of embodied AI, leads to a more fluid and engaging customer experience.
This directness is a meaningful conversion factor for high-traffic physical environments such as retail sites, hospitality portals, and event venues as well. Immediate responsiveness via hardware like Napster Station and other physical AI bring the customer into the experience and ensure they feel heard and that their needs are met.
WebRTC also democratizes the deployment of multimodal AI agents. Rather than running on specialized hardware or bespoke software, the agent becomes a web component. Thanks to WebRTC, aongside tools within Napster's product suite, developers can take a multimodal agent from concept to being embedded in a page in an afternoon.
