For enterprise teams
Governed AI for non-technical teams
Give each non-technical user an individual AI agent with a dedicated workspace for approved knowledge, tools, and operating context.
Built by operators from Axos Bank, Google, Bank of America, Comcast Voice AI
The problem
AI Works For Individuals. Organizations Can't Scale It.
LLMs can do everything. Which is why most organizations can't do anything with them.
Nobody Can Build What They Need
Teams can't turn their specific workflow into a tool. The capability is there. The last mile isn't.
What Works Can't Scale
Every org has power users who've figured it out. That knowledge lives in their heads. No way to share it, templatize it, or hand it off.
Shadow AI Fills the Vacuum
When approved paths are unclear, employees choose disconnected tools and personal accounts. Without visible ownership and usage records, adoption becomes difficult to manage.
A common enterprise adoption pattern
The gap isn't intelligence.
It's infrastructure.
Why existing tools fail
Every AI platform forces a tradeoff. AgentShelf doesn't.
Three categories. Three traps. One way out.
The Lock-In Trap
Fast to adopt, but changing models or operating environments can require rebuilding around a different stack.
The Basic-Interface Trap
Easy to start, but limited when an agent needs approved tools, knowledge, files, and durable operating context.
The Do-It-Yourself Trap
Flexible, but the organization must assemble and maintain the workspace, integrations, access rules, and deployment path.
Choice Without Hardwiring
Keep the agent’s purpose and workspace context distinct from any one supported model or deployment surface.
A Workspace for Real Work
Each individual agent gets a dedicated environment for approved tools, knowledge, files, and operating context.
Start With a Focused Agent
Begin with one user, one individual agent, and one dedicated workspace—without making custom infrastructure the first step.
One user. One focused agent. One dedicated place to work.
The dedicated workspace is where one user manages the agent’s context, tools, conversations, results, and available activity.
Use cases
Real decisions your team faces every week.
Each one used to take 30 minutes of searching and copy-pasting. Now it's a conversation.
Tribal knowledge, on demand
"What's our process for handling enterprise procurement?"
Search shared docs→Pull playbook sections→Cite sources→Answer in seconds
Meeting prep in seconds
"I have a call with Acme in 20 minutes."
Pull account context→Draft talking points→Reference last meeting notes→Share with team
Ticket resolution from your docs
"Customer says their integration broke after the update."
Search knowledge base→Match known issue→Draft response→Escalate if needed
Governance approach
Designed for accountable AI adoption.
Keep ownership, configured access, activity, and usage visible while your organization applies its own security and compliance requirements.
Configured model access
Use approved model access and keep agent instructions and workspace context managed separately from a single provider where supported.
Activity and usage records
Review available agent activity, user interaction, and cost records with timestamps and attribution where reporting is enabled.
Clear ownership and review
Keep the user, individual agent, dedicated workspace, and configured policies connected in one reviewable relationship.
Questions we get asked a lot.
Ready to put a focused AI agent to work?
Start with one accountable user, one individual agent, and the dedicated workspace that agent needs. We onboard hands-on.