Managed agent infrastructure
LLM infrastructure your agents can run on.
Connect models from multiple providers, approved knowledge, and tools. AgentShelf brings together LLM access, automatic provider failover, budgets, and supported runtime services.
AI model access
- OpenAI
- Anthropic
- Meta
- xAI
- Microsoft Azure OpenAI
- DeepSeek
- AWS Bedrock
Example providers. Availability and capabilities vary by selected AI model, customer configuration, credentials, region, plan, and current catalog.
What an agent workflow needs Use AgentShelf’s managed foundation instead of rebuilding the infrastructure for each workflow.
From knowledge to action
Deployment options for your agents
From meeting briefs to support answers, connect the information and tools each task calls for.
Your business knowledge
Connect approved documents. Your team chooses the data and permissions.
A managed foundation
Define and configure the agent. AgentShelf coordinates AI models, context, tools, and supported runtime services.
AI model providers
Choose configured AI models from the providers available for the task.
Tools to get work done
Look up information and take permitted actions with approved tools. Authorized operators oversee activity.
- Run with AgentShelf
- AgentShelf manages the application and supported runtime services. Configured AI model providers run inference under their own terms; hosting AI foundation models and general-purpose customer compute are not included.
- Use your own or an external runtime
- Your team manages the supported deployment: tools, configuration, execution, networking, availability, and security.
Reliability
Provider failover and availability
AgentShelf’s architecture includes automatic failover across 7+ providers. Evaluate this capability alongside the configured models and supported paths for your workflow.
Platform capabilities
Connect AI models, tools, and controls.
Availability varies by configured AI model, release, credentials, plan, deployment, and runtime path.
Multi-provider LLM access
Access configured AI models through the managed gateway.
- Select configured AI models for task needs on supported paths.
- Access providers with the required configuration and credentials.
Context and tools
Give agents the context and tools their jobs require.
- Connect knowledge and tools approved by your team.
- Define permissions and configuration for the work.
Usage and runtime records
Review operational records from agent runs.
- Usage and cost information for operational review.
- Activity and errors on supported paths.
Budget policies
Organization admins choose soft or hard monetary budgets for organization, team, user, and widget scopes on supported paths.
- Soft limit reached: covered requests continue with a warning.
- Hard limit reached: covered requests are rejected.
Operational review
Investigate agent activity and spending.
Authorized operators use records from supported paths to decide where to intervene.
- Activity and errors
- Investigate what ran and where attention is needed.
- Usage and costs
- Review spending as billed costs, or as provider costs when you use your own key.
Coverage varies by runtime path; records can be incomplete, delayed, or unavailable. Retention depends on record type and configuration. Cost estimates and amounts awaiting reconciliation are not final charges; provider invoices remain the providers’ financial records.
FAQ
LLM infrastructure questions
Keep learning
Explore LLM infrastructure
What would you put your next agent to work on?
Tell us the task. Explore the AI models, tools, and supported deployment options with our team.