As AI model providers increasingly move downstream, launching products and agents for specific enterprise applications and sectors like finance, one big question still remains: how will said AI agents be equipped with the proper context surrounding a task — who assigned it, which other stakeholders are involved, what data or discussions have taken place about it and how it should be done?
This practice of "context engineering" remains one of the great unsolved problems of the AI era. But SageOx, a Seattle-based startup founded by the veterans who built the original AWS EC2 and EBS infrastructure, believes it has the answer: a new systems layer it calls "agentic context infrastructure."
Using a combination of small hardware recording devices and the existing applications enterprises already rely on — Slack, email, documents, files — and applying new, open-source frameworks and instructions atop it all, SageOX has developed a system by which enterprises can keep agents as "in-the-loop" and updated on the enterprise's tasks as their human employees are, and prevent them from "drifting" off their assigned tasks and the firm's larger goals.
“We are capturing all of this context where it happens," said Ajit Banerjee, founder and CEO of SageOX and a former Hugging Face, Meta, Amazon and Apple engineer said in a recent video call interview with VentureBeat. "Product development is a team sport, and the context doesn’t just come from people typing on a keyboard. It happens in conversations.”
By capturing the "why" behind the "what"—the intent that lives in Slack threads, whiteboarding sessions, and water-cooler conversations—SageOx aims to provide a "hivemind" that ensures agents don't drift and humans stay in flow.
"The way people have to work is not old-school coordination, where I write down an issue and then it goes through a sequence. It has to be almost like playing jazz," Banerjee added.
Today, the company emerged from stealth to announce its $15 million seed round led by Canaan and participation
from A.Capital, Pioneer Square Labs, and Founders’ Co-op.
The architecture of team memory
Today’s AI agents operate in isolated sessions, lacking a shared memory of prior decisions or architectural intent.
Every task effectively starts from scratch, forcing developers to manually recap context—a process that undermines the very speed agents are meant to provide. SageOx addresses this through a multi-surface product suite designed to capture context wherever it naturally occurs.
At the center of this ecosystem is the Ox Dot. A customized hardware device designed for the shared office, the Dot captures meetings, standups, and design reviews with a single touch.
Its most distinctive feature is "Auto Rewind"—a fail-safe for the spontaneous brilliance of a team. If a breakthrough happens during an unrecorded conversation, Auto Rewind allows the team to "go back" and capture the discussion after the fact. This audio is transcribed, speaker-identified, and distilled into team memory, where it becomes accessible to both humans and agents.
For the developer, the open-source, MIT-licensed Ox CLI provides the bridge. Commands like ox agent prime allow coding assistants—including Claude Code and Codex—to consult the team's shared history before writing code. This ensures that if a team decided in a meeting to use a specific authentication pattern, the agent knows it without being explicitly told in a prompt.
As Dr. Rupak Majumdar, Scientific Director, Max Planck Institute for Software Systems, noted after seeing the team’s development speed, they are effectively "treating code like assembler."
Agentic engineering: moving Beyond "clean" code
The shift to an agent-first workflow has forced the SageOx team to reconsider nearly every principle of modern software management.
SageOX CTO Ryan Snodgrass, formerly of Amazon, notes in a blog post transcript that traditional branch management and "clean" commit histories are often "bad for the agents." In the old world, humans preferred large PRs that were easy to read during a single code review.
In the agentic era, 10,000-line PRs spread across the codebase make it impossible for an agent to reason about intent.
Instead, SageOx advocates for smaller, high-volume, and highly focused commits. This "agent-readable" history allows the machine to look back and understand exactly why a specific change was made. The team is even re-evaluating repo structures; while they currently utilize a monorepo for their 750,000 lines of code, they are exploring a future where agents manage a constellation of micro-repos, as agents can "get lost" when a codebase grows too large for their context window.
This philosophy of "speed-over-stasis" allowed the team to build their own firmware for the Ox Dot in less than two weeks, despite having no recent hardware experience.
By feeding technical PDFs and documentation into AI models, they bypassed months of traditional research. CEO Ajit Banerjee calls this the "unlearning" of old habits—realizing that the "undifferentiated heavy lifting" of knowledge work can now be offloaded to a system that remembers everything the team knows.
Radical transparency: beyond open source to an "open work" model
Perhaps as significant as the technology is SageOx’s commitment to "Open Work." Moving beyond traditional open-source software, the company is practicing a form of radical transparency in an effort to foster the acceleration of development across the entire open source community and any enterprises who wish to learn from the way they work.
SageOx's team openly shares their internal prompts, their planning sessions, and even their unfiltered internal debates with the public. Users can sign in to the SageOx console and watch the team build SageOx in real-time.
This "open kimono" approach was an intentional decision to lead by example. Banerjee argues that since they are asking teams to change how they work, they must be willing to show the "WTF" moments and the course corrections as they happen.
"The revolution is not going to be televised," Banerjee says. "It's going to be SageOxed."
This transparency is intended to prove that a small, lean team—"yoking up lean"—can outpace massive organizations by leveraging a shared context layer.
As for how SageOx plans to monetize and become profitable, Banerjee said the revenue path is modeled on the AWS EC2 playbook: start with early adopters, especially small AI-native startups, then expand toward enterprises as the need becomes obvious.
The pedigree of infrastructure
The technical foundation of SageOx is rooted in the early days of cloud infrastructure.
Banerjee was an original member of the AWS EC2 team, and Snodgrass was one of Amazon's first engineers, leading the transition from monolithic architectures to microservices.
This background is reflected in the company’s name: the "Ox" represents the "Yeoman work" they aim to do—a dependable animal that handles the heavy lifting of data and context so the team can move forward.
The SageOx vision is one where humans are no longer the manual assemblers of context.
Instead, they act as the directors of a "parallel processing" engine.
In a recent demonstration, a feature request moved from a verbal discussion to a completed implementation in under seven minutes. By priming coding agents with the recorded context of the original discussion, the team bypassed the need for formal specs or Jira tickets.
The new way of work
SageOx is currently focusing its efforts on "AI-native" startups—teams that operate primarily through prompts and rely heavily on agentic coworkers.
Their suite of tools, from the open-source Ox CLI to the hardware-enabled Ox Dot, is designed to solve the immediate problem of alignment drift.
As AI moves from being a tool to a teammate, the most valuable asset a company possesses is no longer its raw source code, but its shared context.
SageOx suggests that the way forward is not to hoard information behind "private fences," but to create a communal ground where intent is visible to every teammate—human or machine. In this new epoch, the teams that win will be the ones that can remember as fast as they can execute.
[/gpt3]
