NotebookLM Agentic AI Upgrade: What It Does [2026]

Originally published at kunalganglani.com — read it there for inline code, hero image, and live links.

NotebookLM's agentic AI upgrade is Google's transformation of a simple document Q&A tool into a multi-modal, Gemini-powered platform that autonomously generates Audio Overviews, Video Overviews, Slide Decks, Infographics, Mind Maps, and more from your sources.

Most developers I talk to still think of NotebookLM as "that podcast thing Google made." They're about two major updates behind. Since the Google Labs Team announced NotebookLM Plus in December 2024, the product has quietly grown into something that looks a lot more like an agentic AI platform than a research assistant. Gemini's persistent memory and Connected Apps now flow into NotebookLM sessions, and the question I keep hearing from developers is: can this thing replace my coding agent?

No. But the real answer is worth unpacking.

NotebookLM's Agentic AI Upgrade: What Changed in 2026

Let me be specific about what NotebookLM actually does now, because the feature set has grown fast. According to Google's official product documentation, NotebookLM supports these output types from a single source upload:

• Chat — conversational Q&A grounded in your uploaded sources
• Audio Overviews — the original podcast-style summaries, now interactive
• Video Overviews — auto-generated video walkthroughs of your content
• Slide Decks — presentation-ready slides from your documents
• Infographics — visual summaries of key concepts
• Mind Maps — relationship diagrams between ideas in your sources
• Flashcards and Quizzes — study-oriented outputs for learning workflows

Each of these is generated autonomously by the Gemini model. You upload your sources, pick an output type, and NotebookLM handles the rest. No further prompting required. That's the "agentic" part: multi-step, multi-modal output generation without hand-holding.

Google also shipped a "Change mode" setting in NotebookLM, which separates research/chat mode from an agent/generation mode. Small UX detail, but it tells you exactly where Google's product thinking is headed. This is a pattern consistent with agentic AI product design.

Having built systems that use AI agents in production, I can tell you that multi-modal output generation is useful. Really useful. But it's a specific kind of useful, and it's not what most developers mean when they say "coding agent."

What "Agentic" Actually Means (And Where NotebookLM Fits)

The word "agentic" gets thrown around so loosely in 2026 that it's practically meaningless. So let's ground it.

Developer kaleman15 on Dev.to broke down the agentic engineering skill stack into four tiers that I think are actually clarifying:

1. Tool use — the agent can invoke external tools and APIs
2. Memory — the agent retains context across sessions
3. Multi-step planning — the agent decomposes complex tasks into sequences
4. Autonomous execution — the agent acts on plans without human intervention at each step

NotebookLM shows strong tool use (it generates multiple output types from a single set of sources) and solid multi-step output generation (turning a PDF into a slide deck requires planning the structure, extracting key points, designing layouts). But it currently lacks persistent memory across notebook sessions and true autonomous code execution.

That gap matters. If you're comparing NotebookLM to Claude Code or GitHub Copilot Workspace, you're comparing a knowledge-synthesis agent to a code-execution agent. Fundamentally different problems.

The agentic spectrum isn't binary. NotebookLM is agentic in the way a skilled research assistant is agentic — it takes initiative on output, but it doesn't write and run your code.

I wrote about the rise of agentic AI early on, and this distinction matters more than most people realize. The industry's habit of slapping "agentic" on everything from chatbots to autonomous coding tools creates real confusion for developers trying to pick the right tool for the job.

NotebookLM vs Dedicated Coding Agents: The Honest Comparison

Here's the comparison table developers actually need. I'm evaluating NotebookLM against Claude Code and GitHub Copilot Workspace across the dimensions that matter for real workflows:

The gap is obvious. NotebookLM doesn't execute code. Period. If you need an AI coding tool that writes, tests, and iterates on code, NotebookLM isn't it. Claude Code and its competitors operate in your terminal, read your file system, and run commands. NotebookLM operates on uploaded documents and generates knowledge artifacts.

But here's the thing nobody's saying about NotebookLM: it's not trying to be a coding agent. It's trying to be the research and synthesis layer that makes your coding agent more effective. And that framing changes the entire conversation.

The MCP Gap: Why NotebookLM Can't Compete on Developer Tooling

If you've been following the developer tooling conversation in 2026, you know that MCP (Model Context Protocol) has become the de facto standard for how AI agents connect to external tools. Developer rapls on Dev.to captured the current mental model well: developers are choosing between MCP servers, plugins, and CLI extensions when building out their agentic coding workflows.

NotebookLM doesn't support MCP. No plugin API. No IDE integration. No terminal hooks. This is the single biggest reason it can't replace dedicated coding agents.

I've shipped enough features to know that tool integration isn't a nice-to-have. It's the whole game. When I'm deep in a vibe coding session, my coding agent needs to read my files, run my tests, check my linter output, and iterate. NotebookLM can't do any of that.

AWS Developer Relations highlighted this exact point when discussing why developers are switching to agent-native toolkits: MCP integration is now a baseline requirement for any tool that wants to participate in a developer's agent orchestration stack.

Until NotebookLM gets MCP support (or something equivalent), it's playing a different game than Claude Code, Cursor, or Windsurf. That's not a criticism. It's a product strategy observation.

Where NotebookLM Actually Wins for Developers

So if NotebookLM isn't a coding agent, why should developers care?

Because the hardest part of building software isn't writing code anymore. It's understanding what to build and why. That's exactly where NotebookLM excels.

Here are the developer workflows where I've found it valuable:

Architecture research. Upload three competing RFC documents, two blog posts about a pattern you're evaluating, and a transcript from an internal design review. NotebookLM synthesizes across all of them, generates a mind map of the relationships, and gives you grounded answers when you ask "what are the tradeoffs between approach A and B?" I've used this exact workflow when evaluating vector database options for a RAG pipeline. It saved me hours of tab-switching and manual comparison.

Onboarding documentation. Upload an existing codebase's README, architecture docs, and key PRs. NotebookLM generates Audio Overviews that new team members can listen to during their commute. I've seen this cut onboarding time meaningfully because it transforms static documentation into conversational walkthroughs. New engineers actually engage with it, which is more than I can say for most wikis.

Technical spec creation. Upload your research sources and let NotebookLM generate a slide deck draft. It's not production-ready, but it gets you 60-70% of the way there. That's exactly where you want to start when building a proposal for a new microservices migration or CI/CD overhaul. The remaining 30% is where your judgment and context come in.

Learning new technologies. Upload a paper about transformer architectures or a guide to fine-tuning, and NotebookLM's flashcard and quiz generation turns passive reading into active learning. I've been using this to ramp up on areas outside my core expertise, and the retention difference is real.

The public notebooks and mobile app are differentiators here too. A coding agent like Claude Code is terminal-centric by design. NotebookLM is shareable, collaborative, and accessible from your phone. If you're a team lead who needs to get research in front of your team fast, that matters.

[YOUTUBE:SbnKvdp76H0|NotebookLM Gemini Agent: Google's Most POWERFUL AI Combo!]

The Gemini Backbone: Why NotebookLM's Ceiling Keeps Rising

This is the part most people are sleeping on. NotebookLM isn't a standalone product anymore. It's integrated into the Gemini Apps ecosystem. Google literally calls it "Notebooks in Gemini Apps" in their help documentation. That means NotebookLM inherits Gemini's improvements automatically.

As of June 2026, 9to5Google reported that Gemini Live gained persistent memory and Connected Apps access. Those capabilities cascade into NotebookLM sessions. Persistent memory means your notebook context could survive across sessions. Connected Apps means NotebookLM could pull in data from Gmail, Drive, Calendar, and third-party services without manual uploads.

This is the play Google is making: NotebookLM as the knowledge layer in a Gemini-powered agent framework that spans all of Workspace. If Google executes on this, NotebookLM becomes the research and synthesis hub that feeds context to your coding agents, project management tools, and communication channels.

A Google Developer Expert demonstrated this pattern already by building agent "skills" with Google's Antigravity SDK, showing how Gemini-powered tools can extend beyond their base capabilities when combined with agentic frameworks.

In my experience building production AI systems, the tools that win long-term aren't the ones with the most features at launch. They're the ones with the strongest platform underneath. NotebookLM's Gemini integration is its strongest strategic asset, and I don't think enough developers are paying attention to that.

The Hybrid Workflow: NotebookLM + Coding Agent

The most productive setup I've found isn't NotebookLM OR a coding agent. It's both.

Here's the workflow I've been running:

1. Research phase — dump all relevant docs, papers, and prior art into NotebookLM. Use chat to explore the problem space. Generate a mind map to visualize relationships.
2. Spec phase — use NotebookLM's slide deck generation to create a rough spec. Refine it manually.
3. Build phase — hand the spec to Claude Code or your preferred coding agent. The research context from step 1 directly informs your prompts.
4. Documentation phase — feed the completed code and README back into NotebookLM. Generate Audio Overviews for the team. Create flashcards for key architectural decisions.

This isn't theoretical. I've used this pattern on three projects in the last quarter. The research phase alone saves hours because NotebookLM's source-grounded answers are way more reliable than asking a large language model to synthesize information from its training data. You're working with your documents, not the model's memory of the internet.

The key insight: NotebookLM and coding agents aren't competitors. They're complementary tools that operate at different points in the development lifecycle. Treating them as interchangeable is like comparing Figma to VS Code. Both are development tools. They solve entirely different problems.

Should You Pay for NotebookLM Plus?

NotebookLM's free tier is surprisingly capable. You get access to all the output types: Audio Overviews, Video Overviews, Slide Decks, the works. The Google Labs Team launched NotebookLM Plus in December 2024 as the premium tier with deeper Gemini integration, higher usage limits, and priority access to new features.

For individual developers exploring NotebookLM as a research tool, the free tier is more than enough to evaluate whether it fits your workflow. For teams that want to use public notebooks as a knowledge-sharing platform, Plus starts making sense.

My advice: start free. Upload a real project's documentation and spend 30 minutes exploring the different output types. If you find yourself hitting usage limits, upgrade. Don't pay for potential. Pay for demonstrated value.

This is one of those things where the boring answer is actually the right one. Try it before you buy it.

What Comes Next: Predictions for NotebookLM's Developer Story

I'll stick my neck out here.

NotebookLM will get MCP support within the next 12 months. Google knows the developer tooling landscape is consolidating around MCP as the interoperability standard, and leaving NotebookLM disconnected from agent orchestration workflows is a strategic mistake they won't make. The Gemini backbone already supports function calling. MCP integration is an engineering problem, not a research problem.

NotebookLM will get some form of code execution. Not terminal-level access like Claude Code, but sandboxed execution similar to Google Colab. The Gemini model already handles code generation in other Google products. Adding execution within NotebookLM notebooks is a natural next step.

NotebookLM will become the default onboarding and documentation tool for teams on Google Workspace. The combination of source-grounded chat, multi-modal outputs, and public notebooks creates a knowledge management workflow that nothing else in the market matches right now.

But here's my most important prediction: NotebookLM won't replace your coding agent in 2026. It will make your coding agent better. The developers who figure out the hybrid workflow — research in NotebookLM, build in Claude Code or Cursor — will ship faster than those who try to force everything through a single tool.

The AI agents landscape is maturing past the "one tool to rule them all" phase. The winners will be developers who assemble the right stack, not those who bet everything on a single platform. NotebookLM just earned a spot in that stack. But as the research layer, not the coding layer.

Stop asking whether NotebookLM can replace your coding agent. Start asking how it can feed better context into one.

Frequently Asked Questions

Can NotebookLM write and execute code?

NotebookLM can generate code snippets through its Gemini-powered chat, but it cannot execute code directly. It lacks terminal access, file system integration, and sandbox execution environments. For actual code generation and execution workflows, dedicated coding agents like Claude Code or GitHub Copilot Workspace are significantly more capable.

Is NotebookLM free for developers?

Yes. NotebookLM offers a free tier that includes all major output types: Chat, Audio Overviews, Video Overviews, Slide Decks, Mind Maps, Infographics, and Flashcards. NotebookLM Plus is the paid premium tier with higher usage limits and deeper Gemini integration, but the free version is fully functional for evaluating the tool.

How does NotebookLM compare to Claude Code for coding tasks?

They solve different problems. Claude Code is a terminal-native coding agent that reads your file system, writes code, runs tests, and iterates autonomously. NotebookLM is a research and synthesis tool that generates multi-modal outputs from uploaded documents. NotebookLM excels at architecture research, documentation, and learning — not at writing and shipping code.

Does NotebookLM support MCP (Model Context Protocol)?

No. As of mid-2026, NotebookLM does not support MCP integration, plugins, or CLI extensions. This is the primary reason it cannot participate in the agentic coding tool ecosystem the way Claude Code, Cursor, or Windsurf do. MCP has become the standard for connecting AI agents to external tools and data sources.

What is the best way to use NotebookLM as a developer?

The highest-value developer workflow is using NotebookLM for the research and documentation phases of a project. Upload architecture docs, RFCs, papers, and prior art, then use chat and mind maps to explore the problem space. Generate slide decks for specs and Audio Overviews for team onboarding. Hand the synthesized context to your coding agent for the build phase.

Will NotebookLM get coding agent features in the future?

It's likely. NotebookLM runs on Google's Gemini infrastructure, which already supports function calling and code generation in other products. Google's integration of NotebookLM into the Gemini Apps ecosystem suggests that capabilities like persistent memory, connected apps, and eventually sandboxed code execution could arrive as Gemini's features cascade into NotebookLM.

Originally published on kunalganglani.com