Kylon vs. Notion AI vs. Glean: Documents and search are not enough

Notion and Glean are two of the most respected productivity platforms in the market. Notion turned the humble wiki into a flexible workspace. Glean turned enterprise search into something people actually use. Both added serious AI capabilities in 2025–2026.

But here's the thing: Notion's DNA is documents. Glean's DNA is search. And in a world where AI agents can actually do work, finding information and writing documents are necessary but not sufficient.

What Notion AI does well

Notion has always been good at making information feel organized. The AI layer makes it better.

Notion's autonomous agents can run multi-step tasks for up to 20 minutes — building project plans, compiling research across multiple pages, drafting reports, and updating database entries at scale. You can pick from GPT-5.2, Claude Opus 4.6, or Gemini 3, or let the system auto-select. Custom agents can run on schedules or triggers without human input.

The enterprise search is genuinely useful: one search bar that spans Notion pages, Slack messages, Google Drive files, GitHub repos, and Jira tickets. AI Meeting Notes transcribes Zoom and Teams calls without a bot joining the meeting. Research Mode generates structured reports from both workspace context and web sources.

For teams that already live in Notion, the AI feels native and well-integrated. It's not a bolt-on — it understands your pages, databases, and project structures.

What Glean does well

Glean solved a problem that plagued enterprises for decades: "I know we have this information somewhere, but I can't find it."

Connect Glean to 100+ enterprise applications, and it builds a knowledge graph that maps documents, messages, tools, and people. The AI doesn't just keyword-match — it understands relationships. "Who's working on the Henderson deal?" returns not just the deal doc but the relevant Slack threads, email chains, and meeting notes.

The Agent sandbox is powerful for analysis: it can process large result sets, run code, and produce charts, summaries, and CSV exports. PowerPoint generation using company-approved templates is a nice enterprise touch. Voice commands, SOC 2/HIPAA/GDPR compliance, and row-level security make it ready for regulated industries.

For large organizations drowning in scattered information, Glean is a genuine time-saver. Finding information in 10 seconds instead of 10 minutes adds up.

The gap: finding and formatting aren't doing

Both Notion AI and Glean make existing information more accessible. Notion helps you organize and write. Glean helps you search and analyze. But neither platform is designed for agents that execute work.

Consider a typical team workflow: research competitors, build a comparison dashboard, draft a blog post, deploy it to the website, track its SEO performance, and adjust based on results. Here's what each tool handles:

• Notion AI can draft the blog post and organize the research. It can't deploy the website, track SEO metrics, or coordinate multiple agents.
• Glean can find the competitive data across your systems. It can't write the blog post, build the dashboard, or take any action beyond reporting.
• Neither can coordinate multiple agents working on different parts of the same project.

This is the execution gap. In a world where AI agents can actually build apps, run workflows, send emails, update databases, and deploy pages — a tool that only helps you find or write information is leaving 80% of the value on the table.

How Kylon approaches this differently

Agents that execute, not just assist

In Notion, AI helps you write better documents. In Glean, AI helps you find the right document. In Kylon, agents do the work that produces the document in the first place.

A Kylon agent doesn't just draft a blog post — it researches competitors, builds the comparison, writes the article, deploys it to your website, sets up SEO tracking, and reports on performance the next morning. Another agent monitors your CRM pipeline and flags deals that need attention. Another processes incoming emails and routes them based on rules your team defined.

These aren't hypothetical capabilities. This is how Kylon teams work every day.

Multi-agent teams, not a single AI layer

Notion has "Notion AI" — one AI capability shared across the platform. Glean has "Glean AI" — one search and analysis layer. Both are powerful. Both are singular.

Kylon runs multiple specialized agents in the same workspace. A data agent, a content agent, a marketing agent, and an operations agent — each with their own identity, memory, skills, and permissions. They work in the same channels alongside humans, read each other's output, and hand off tasks naturally.

When your marketing lead kicks off a campaign in a channel, the research agent pulls competitive data, the content agent drafts copy based on the findings, and the analytics agent sets up tracking — all in the same thread, all building on each other's context. No one manually copies output from one tool to another.

Workspace-native, not document-native or search-native

This is the fundamental architectural difference.

Notion's primitives are pages and databases. Everything lives inside documents. AI enhances those documents. But the document is still the center of gravity.

Glean's primitive is search. Everything flows through queries and retrieval. AI enhances those queries. But search is still the core interaction.

Kylon's primitives are channels, agents, databases, workflows, and skills. The workspace itself is the operating system. Agents live inside it as first-class members. Data lives in built-in database apps that agents read and write directly. Workflows run on schedules or triggers. Skills package reusable capabilities that any agent can use.

This means your team's work doesn't have to fit into the shape of a document or a search query. It can be a conversation, a database record, a deployed app, a scheduled report, or a multi-step workflow — all inside the same workspace, all accessible to agents and humans alike.

Persistent memory across the organization

Notion AI's context is bounded by the pages you're working with. Glean's context is bounded by your search query. Both are session-scoped — once the interaction ends, the AI doesn't carry that context forward.

Kylon agents build persistent memory across every interaction. An agent that processes a customer complaint in January remembers the pattern when a similar issue surfaces in June. An agent that learns your team's deployment procedure doesn't need to be re-taught every time someone asks about it.

This institutional memory compounds over time. After six months of operation, your Kylon workspace knows things about your business that no document or search index captures — the informal decisions, the edge cases, the patterns that only emerge over hundreds of interactions.

When each tool makes sense

Choose Notion AI when your team's work is primarily document-centric. If you live in wikis, project docs, and databases — and you need AI to help you write, organize, and automate within that world — Notion AI is excellent. It's the best AI-augmented knowledge base available.

Choose Glean when your organization has information scattered across dozens of tools and the primary bottleneck is finding what you need. If your team spends hours searching for the right document, the right person, or the right answer — Glean's knowledge graph and enterprise search are genuinely transformative.

Choose Kylon when your team needs AI that doesn't just help you find or write information — it needs AI that does the work. If your challenge is coordinating multiple agents across multiple workflows, building institutional knowledge, and having AI execute real tasks alongside your team — Kylon is the workspace built for that.

Documents and search got us here. Execution takes us further.

The progression is clear: first we needed better documents (Notion). Then we needed better search (Glean). Now we need better execution — AI agents that don't just help you organize and find information but actually do the work your team needs done.

That's not a criticism of Notion or Glean. They're excellent at what they do. But what they do is one layer of the stack. The execution layer — where agents build, deploy, coordinate, and learn — is where the next wave of team productivity lives.