Build
Create specialized agents for specific jobs, teams, and workflows.
AI Builder Platform
Build agentic workflows without rebuilding the operating layer.
AgentShelf gives founders, operators, agencies, and internal builders one platform for creating agents, connecting context and tools, deploying runtime experiences, tracking usage, reusing proven agents, and governing AI work.
- No-code agent creation
- Context and tools
- Runtime surfaces
- Usage visibility
- Libraries and marketplace
- Governance controls
The builder problem
The workflow is valuable. The operating layer slows you down.
Agentic workflows usually start with a clear business need. The hard part is everything around the agent: context, tools, permissions, runtime, lead capture, usage records, billing, observability, and reuse.
Every workflow becomes an infrastructure project
Teams lose time wiring sessions, context, tools, auth, provider access, and deployment surfaces before they can prove the workflow is useful.
Useful agents get rebuilt instead of reused
Without libraries and catalog workflows, every team recreates the same research, support, lead qualification, and reporting agents in slightly different ways.
Usage and cost become hard to explain
Leaders need visibility into who is using AI, which workflows drive spend, and how usage maps back to teams, agents, workspaces, and runtime surfaces.
Public and private boundaries get blurry
Website agents, internal workspaces, and product embeds should not all use the same access pattern.
The AgentShelf operating layer
Build, connect, deploy, reuse, and govern from one platform.
AgentShelf brings the operating layer around agents into one managed control plane so builders can focus on the workflow instead of the infrastructure.
Connect
Attach approved context, files, skills, connectors, tools, and automation paths.
Deploy
Run agents in websites, internal workspaces, products, and workflow surfaces.
Reuse
Standardize proven agents through libraries and marketplace-style discovery.
Govern
Track usage, model activity, budgets, cost reporting, and accountability.
Platform capabilities
The operating layer around repeatable agentic work.
Use one platform for agent creation, approved context, deployment surfaces, reuse, and governance.
Agent builder
Configure role, goal, context, tools, model behavior, runtime surface, and handoff rules by workflow.
Context and tools
Give agents approved files, knowledge, skills, connectors, tools, and automation paths.
Runtime surfaces
Deploy agents to websites, workspaces, products, external applications, and recurring workflow surfaces.
Libraries and marketplace
Help teams standardize, install, update, and reuse effective agents through private libraries and public discovery.
Governance controls
Keep usage, model/provider activity, runtime surfaces, budgets, and operational accountability visible.
Agent builder
Turn repeatable expertise into specialized agents.
Business builders can configure agents around a job to be done. Technical teams can extend deeper runtime paths when needed, but the first step does not need to be a custom app.
Builder principle
AgentShelf is not just a prompt box. It is the operating layer for turning repeatable work into managed agent experiences.
Deployment surfaces
One platform for internal, public, embedded, and automated agent experiences.
Deploy agents where the workflow happens, while keeping the operating layer connected to AgentShelf.
Website AI agents
Answer visitor questions, qualify intent, capture leads, and preserve useful handoff context.
Internal workspaces
Give teams authenticated spaces for working with agents, files, tools, generated outputs, and shared context.
Product embeds
Use Developer Runtime paths when teams need custom frontend or product-integrated agent experiences.
Automation paths
Support recurring workflows, context refreshes, reports, follow-up, and research tasks where configured.
Operate what you build
Launch agents with visibility and accountability built in.
As workflows move into production, teams need to understand usage, cost, model/provider activity, runtime surfaces, and adoption.
Usage visibility
Track agent, model, token, cost, workspace, team, user, and runtime-surface activity where reporting dimensions are enabled.
Budget accountability
Use budgets, credits, rate limits, and cost reporting to support AI spend management and workflow-level accountability.
Model flexibility
Route work through configured model/provider access so teams are not forced to rebuild workflows around one model vendor.
Reuse
Use org-scoped internal libraries and public marketplace discovery to help teams reuse effective agents.
Admin oversight
Manage agent access, workspace behavior, public runtime boundaries, and governance controls from the platform.
Workflow examples
Start with workflows your team already repeats.
The best agentic workflows are usually recurring tasks people already perform manually across sales, support, operations, research, finance, and internal knowledge work.
Website visitor qualification
Answer visitor questions, qualify buying intent, capture lead details, and preserve handoff context.
Support triage
Use approved knowledge and workflow rules to classify issues, draft responses, and prepare escalation context.
CRM follow-up
Summarize conversations, prepare next steps, and support CRM-connected follow-up workflows where configured.
Internal knowledge assistants
Give teams managed agents that can work with approved company context, files, and operating procedures.
Sales research
Create repeatable research workflows that help sellers prepare for calls and draft follow-up.
Recurring reporting
Support context refreshes, research summaries, recurring analysis, and report preparation where enabled.
Create the workflow. AgentShelf handles the operating layer.
Move from agent experiments to repeatable agentic workflows with creation, context, tools, runtime surfaces, usage visibility, reuse, and governance in one platform.