The lines between developer, product manager, and designer are dissolving. What's left is the Builder — someone who thinks about the whole product, from architecture to user experience to growth. One person. Full ownership. But they can't do it alone.
Builders need a new set of tools. Those tools are agents.
The Builder
Full-stack ownership — code, product, design, growth. One person who thinks end-to-end. Powered by agents that handle the mundane.
We believe in this thesis. That's why Glue exists.
This isn't speculation. It's already happening. The SF Standard declared “Engineer is so 2025 — everyone's a builder now.” Sam Altman predicts the first billion-dollar, single-employee company arrives this year. 36% of new startups are solo-founded. Andreessen Horowitz calls 2026 the year of the Agent Employee where AI moves from a tool that assists to a digital workforce that acts alongside you.
But here's what nobody's solving: existing organizations.
Solo founders are building with agents from day one. But the 50-person engineering team? The 500-person enterprise? They're watching solo builders outpace them and they can't figure out why. McKinsey calls it “the agentic organization”, the next enterprise paradigm. Gartner predicts 40% of enterprise apps will have AI agents by end of 2026, up from less than 5% today. 57% of companies already have agents in production, with 66% reporting productivity gains.
The problem? Their context is scattered across 14 tools. Their knowledge lives in people's heads. Their processes were built for the assembly line, not for Builders. KPMG warns: organizations that don't deconstruct legacy workflows will achieve only incremental speed while losing ground to competitors that have rewired around agents.
Glue gives existing organizations everything they need to make this leap. A Product OS that connects every tool, indexes every signal, and gives every person on the team the agents and intelligence to operate like a Builder. Regardless of their title. Regardless of their team size. Not another pilot. The production-grade system that turns a 50-person team into 50 Builders.
The Problem
Your product has a unified existence — one codebase, one customer experience, one set of behaviors. But your tools don't reflect that unity. You have 14 SaaS products. Nothing talks to anything. You are the integration layer.
The 5-Tab Problem
An engineer sees an error spike. Here's what happens:
Check Sentry for the error signature.
Switch to GitHub — did a recent commit match the timeline?
Switch to Datadog — do the metrics correlate?
Switch to Slack — does anyone know what's happening?
Switch back to Sentry to confirm the correlation.
Each tab switch is a context switch. Each context switch is cognitive friction. At scale — 50 engineers, 100 services, 1,000 daily incidents — this friction becomes the bottleneck that kills engineering velocity.
The Product OS
Data builds the foundation. Tools act on it. Skills chain tools into workflows. Agents orchestrate everything and act on their own. Each layer is powerful alone. Together, they make your product intelligent.
The foundation everything else is built on.
Your product has a unified existence — one codebase, one customer experience. The Data layer creates a living, cross-referenced map of your entire product. When you deploy code, it connects the commit to the error rates that changed, the users affected, the tickets filed, and the Slack messages sent. Not because someone linked them — because the system understood they're the same event.
What this means
A code change, a Sentry error, a support ticket, a user behavior shift, and a Slack message about the issue are all linked — because they describe the same event. Your product becomes a connected graph instead of a pile of separate logs.
Bidirectional. Intelligent. Not another Zapier.
Native integrations with GitHub, Sentry, PostHog, Jira, Slack, PagerDuty, and everything else your team uses. These aren't webhooks that sync data — they're bidirectional connections that let the system reason about your product using real-time data. The system doesn't just read your product data — it acts on it.
What this means
The system doesn't just know there's an error. It traces the error to a specific commit, identifies the engineer who owns that code, checks if users are affected, and posts the full diagnosis — all without anyone asking.
Multi-step workflows that eliminate tab-switching.
Skills chain multiple tools into reliable workflows. In one step, the system fetches an error from Sentry, identifies the code in GitHub, checks when it deployed, finds the responsible engineer, checks for user complaints, writes a summary, creates a ticket, and posts context in Slack. Triage, specs, incident diagnosis, documentation — done in one step instead of fifty.
Spec writing, for example
"Write a spec for the new billing dashboard." The system searches user feedback across Slack and PostHog, analyzes the billing code architecture, finds related tickets, identifies dependencies, and drafts a spec with context, tradeoffs, and success metrics. The PM edits for priorities — not building context from scratch.
They don't respond. They act.
Agents are goal-driven and autonomous. They decide which skills and tools to use, maintain context across runs, and act without being asked. They investigate while you sleep. They research while you're in meetings. They draft before you ask. By the time you need to make a decision, they've done the legwork.
3 AM, for example
Error rate spikes. Before the on-call engineer picks up the phone, the system has identified the error, traced it to a commit from 2 hours ago, checked if it's safe to roll back, and posted the full diagnosis in Slack with instructions. The engineer reads it, rolls back, goes back to sleep.
The magic isn't any single layer. Without unified data, tools are isolated. Without tools, data is inert. Without skills, tools stay manual. Without agents, skills are just saved workflows. But when all four layers work together — your product becomes intelligent.
I reviewed 3 tickets overnight and wrote specs for each. Signup conversion dropped 18% since Monday — GLUE-2035 is the root cause. View each ticket to review the spec, then approve.
Specs awaiting review · 3 remaining
The Evolution
1945
The first computers were built to compute — to take the repetitive, soul-crushing work off human hands. Punch cards. Batch processing. Cron jobs. Every generation of computing carried the same promise: we'll handle it so you don't have to.
2005 - 2025
We didn't automate work — we multiplied it and spread it across 14 SaaS products. Each tool solved one problem well. But the tools never talked to each other. The integration layer was always a human.
The Inflection
Not autocomplete. Not suggestions. Real understanding. Models that can parse a 200k-line codebase, trace a function through six services, and explain why that endpoint is failing at 3am. For the first time, we have something that can comprehend context — not just execute commands.
Now
The machine understands the problem, gathers the context, and does the work — so you can focus on the decisions only a human can make. That's not a fantasy anymore. That's a Product OS.
The Builders
One saw it from inside the codebase. The other saw it from inside the deal room. They're building the system that connects product strategy to code reality.
23+ years building enterprise software. IIT Bombay. Led engineering across pharmaceutical compliance, developer tools, and SaaS platforms. Got tired of watching teams make decisions about systems they couldn't see.
Scaling SaaS go-to-market for 15+ years. UCLA Anderson. Former Oracle (multiple MVP awards, closed first cloud deal). Built and scaled GTM at Branch. Got tired of losing deals because nobody could answer "do we have that feature?"
Security
Source code never persists on our servers. Glue clones temporarily during indexing, extracts intelligence, and deletes local copies immediately. We extract answers, not code.
Zero persistence
Clone, extract metadata, delete. No source code ever stored on our servers.
AES-256-GCM
All credentials and tokens encrypted at rest with industry-standard encryption.
Delete means delete
Remove your project and all data goes with it. No lock-in. No residue.
The Mission
Today, a 5-person startup and a 500-person enterprise face the same problem: their tools don't talk to each other, their knowledge lives in people's heads, and every question requires interrupting someone.
We're building toward a world where the size of your team doesn't determine the quality of your product decisions. Where a solo founder gets the same contextual intelligence as a team of fifty. Where the machine handles triage, specs, monitoring, and reporting — and humans focus on the creative work that actually moves the needle.
Not AI that replaces engineers. AI that makes every engineer ten times more effective.
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