Glean

Glean 🤝 @okta We’re a proud partner for Okta Cross App Access (XAA). As AI agents take on more work, enterprises need explicit identity, governed access, least privilege, and full auditability built in. At Glean, secure and governed access has been foundational from day one. XAA helps customers control what each agent can access through existing identity and policy infrastructure instead of broad user-granted connections.

POV: it’s your first week in a new role and you already have a million questions. Zain from LinkedIn built an onboarding agent where employees can choose their role and instantly see the answers, access, and support they need to get started. Watch what @ZainulAbiddin95 built.

@Gartner_inc glean-it.com/4v0AXyw

🎉 We’re excited to share that Glean has been recognized as a Market Shaper in the June 2026 @Gartner_inc® Emerging Market Quadrant for No-Code Agent Builders - Start-Up Vendors. No-code agent building is becoming one of the most important battlegrounds in enterprise AI. Organizations don’t just need agents that are easy to create, they need agents that are useful, trustworthy, and ready to get real work done. Today, organizations rely on Glean for: - Deep enterprise context that helps agents understand the people, knowledge, permissions, and workflows that shape how work gets done - Governance and security from day one, so agents can operate with the trust and controls enterprises require - Real actionability across business systems, workflows, and models – helping teams move from fragmented knowledge to meaningful action Thank you to all of our customers, partners, and community for your support. 💙

Context-poor AI doesn’t just produce worse output, it creates invisible work. It’s linked to more shadow AI, more cleanup, more reruns, and more people delivering work they can’t explain, according to the Work AI Index from @glean's Work AI Institute. The real divide in enterprise AI isn’t just model quality. It’s context. Report linked below.

@DanielSmidstrup AI slop is often really just botshitting. Output that looks done before anyone has applied taste.

The biggest bottleneck in enterprise AI is no longer model intelligence. It’s token yield. As frontier models get stronger, the harder question becomes economic: how much useful work does a system produce per token? Fable 5 is a good example of the shift. It pushed the frontier forward, but at roughly 180% higher output-token cost than Opus 4.8 on comparable jobs. Most enterprise AI cost is no longer in the prompt. It sits in the system around the model: retrieval, tool use, memory, and multi-step reasoning. An eleven-word request can expand into thousands or even tens of thousands of tokens once a system starts gathering context and working through the task. So token efficiency is now an architecture problem. If your system overfetches context, calls unnecessary tools, or routes simple work through expensive models, cost rises faster than value. The winners won’t be the companies that use the most powerful models most aggressively. The real advantage comes from having an AI architecture that can pair the right model and the right level of reasoning to the task, with strong routing, spend controls, and governance built in from the start. That is the approach @Glean is built for. Frontier intelligence is becoming abundant. Efficient execution is not. Model progress still matters but in enterprise AI, architecture is increasingly what turns intelligence into a competitive advantage.

@nisarkhan2 @coreyganim 👋👋👋

@coreyganim Actually sounds good.. I’ve seen a few references to second brain. Are you aware of any examples of commercial execution of Second Brain as a Service??

There's an AI business model that hasn't blown up yet but will be HUGE within a year: "Second Brain as a Service" Businesses need their company context organized so AI can effectively use it. Here's the play: 1. Charge 2-5K to build the knowledge base 2. Load their existing data 3. Create the schema/rules for how it gets organized 4. Charge $500-$1K/mo to maintain it 5 industries that desperately need this: 1. Agencies Build a client delivery brain: -past proposals -call transcripts -client notes -campaign reports -SOPs Skill/plugin idea: "Client Context Retriever" that pulls the right client history before calls, reports, and strategy work. 2. Coaches/consultants Build a content + offer brain: -frameworks -client questions -sales calls -testimonials -old posts Skill/plugin idea: "Offer Angle Finder" that turns repeated customer language into post ideas, emails, and sales assets. 3. Local service businesses Build an operations brain: -quote templates -customer FAQs -technician notes -intake forms -reviews Skill/plugin idea: "Quote Prep Assistant" that drafts the first version of a quote using the customer's request + company rules. 4. Ecommerce brands Build a customer/research brain: -reviews -support tickets -product specs -ad comments -competitor notes Skill/plugin idea: "Customer Voice Miner" that finds recurring objections, product issues, and ad angles. 5. Real estate teams Build a deal/context brain: -property notes -buyer preferences -neighborhood research -past messages -follow-up history Skill/plugin idea: "Deal Brief Generator" that prepares the agent before every showing or follow-up. The entire business model is making a company searchable, usable, and easier to operate with AI.

We were named the 2026 @Databricks ISV AI Visionary Partner of the Year 🎉 We’re at #DatabricksAISummit all week showing how Glean brings governed Databricks insights into the flow of work. Come see us at Booth #323 👋 @Data_AI_Summit

Shared service accounts and long lived tokens were never built for agents that delegate work to other agents. On Insecure Agents, @glean CISO Sunil Agrawal makes the case for a scoped identity per agent and governance that happens before the agent takes action.

@FactSet 🤝

🚀 Big news! @FactSet s joining @Glean's expanded Financial Services MCP Ecosystem—bringing trusted, governed financial intelligence into enterprise AI workflows. In the agentic era, data quality is everything. 💡 #FactSet #AI #FinancialServices #Glean #EnterpriseAI

@CBinsights @crunchbase @daloopa_com @FactSet @SPGlobal glean.com/press/glean-ex…

@CBinsights @crunchbase @daloopa_com @FactSet @SPGlobal 👉 @SPGlobal: query trusted datasets in natural language inside Glean, then combine them with internal deal notes, research, and CRM history for faster pitch books, equity research, valuation summaries, and IC prep.

Markets move fast. Financial services teams need context that keeps pace.  With our new MCP Connectors, teams can turn trusted third-party intelligence into action with their firm’s context — across @CBinsights, @crunchbase,  @daloopa_com, @FactSet, and @SPGlobal.

275+ connectors and counting. Glean gives AI broader coverage across the systems where work happens — with connectors that understand enterprise content + permissions, plus the context architecture to make that access useful. The result: better context for better work. And in our latest benchmark, Glean was preferred ~2.5x as often as other search tools, which used 30% more tokens on average.