About AgentShelf
AI That Works For The Teams Doing The Work
We build the practical layer that gives teams dependable agents, useful context, and the freedom to choose how they work.
Our point of view
Built for the work between the tools
AgentShelf brings business context, operational control, and flexible AI capabilities into one place so teams can move from experiments to useful work.
Our story
Why we built AgentShelf
We built more than 50 bespoke AI agents for business clients and saw the same problem: useful agent work was trapped in technical environments, disconnected from the people and workflows that needed it.
When agents had the right instructions, tools, and business context, work could move much faster. Making that setup dependable and usable was the harder problem.
AgentShelf is the platform we kept wishing existed: a practical way to build, manage, and review AI agents without forcing every user to become a developer.
Our team
Leadership
Technical depth and GTM. We've built the platforms and sold them inside the teams we're targeting.
Gabe Arce
Founded and led two enterprise consultancies, Talavera Solutions and Arce Cloud Consulting. Ex-VP, Axos Bank. Led more than 100 enterprise deployments across regulated industries with a team of 50.
Edgar Joya
Founder of Joya Technology. Built data engineering and automation systems at Bank of America for more than seven years, then led engineering at Talavera Solutions and shipped production infrastructure for financial services.
Neil Blackman
Built automation systems across financial services at Axos Bank. Applied AI consulting at Talavera Solutions. Core platform engineer on AgentShelf — built the workspace and the institutional context layer.
Track record
Where we've built
Our principles
What we believe
The non-negotiables behind every product decision we make.
Context That Compounds
Give each individual agent a dedicated workspace where approved knowledge, files, tools, and operating context remain available for its work.
Model Choice Without Hardwiring
Use supported models for the job and keep agent instructions and workspace context separate from a single provider wherever the implementation supports it.
Accountable by Design
Keep ownership, activity, and usage visible so people can review how an agent is configured and where it is being used.
AgentShelf is the layer that makes AI work for teams.
Easy. Powerful. Open.