durable execution
every tool call is an event. agent crashes? resumes where it left off. no lost work, no replayed side effects.
agent runtime
the agent runtime that never forgets.
not a framework. a runtime. durable execution, persistent workspaces, human-in-the-loop approval, and serverless gpu compute. you deploy the logic, we run it in production.
agents as environments, not functions
most agent frameworks treat agents as functions - stateless, ephemeral, disposable. the inference shell agent runtime treats agents as environments. state lives outside the container. workspaces persist. knowledge compounds. every session builds on the last.
what the runtime handles
infrastructure, not boilerplate.
persistent workspaces
state lives outside the container. workspaces survive restarts, scale independently, and can be shared across agents.
human-in-the-loop
approval gates before dangerous actions. agents pause, wait for human confirmation, then resume. mobile-friendly.
observability without an sdk
traces, logs, cost per request. every signal captured automatically. no instrumentation code in your agent.
serverless gpu compute
per-second billing, scale to zero, sub-second cold starts on warm models. 30+ gpu types across 20+ providers.
skills & knowledge
agents learn from every session. skills evolve through use. knowledge compounds across runs instead of starting from zero.
the runtime stack
four layers. one deploy.
your agent logic
prompts · tool selection · business rules
agent runtime
durable execution · state · retries · hitl · observability
compute layer
serverless gpus · scale to zero · per-second billing
persistence layer
workspaces · knowledge · skills · event history
how we compare
runtime vs. frameworks
| inference shell | LangGraph | CrewAI | AutoGen | Mastra | |
|---|---|---|---|---|---|
| durable execution | yes | yes | - | - | - |
| persistent workspaces | yes | - | - | - | - |
| human-in-the-loop | yes | yes | - | yes | - |
| built-in observability | yes | - | - | - | - |
| hundreds of tools built in | yes | - | - | - | - |
| serverless gpu compute | yes | - | - | - | - |
| skill & knowledge system | yes | - | - | - | - |
| self-hostable | yes | - | yes | yes | yes |
why a runtime, not a framework
frameworks give you building blocks. you import a library, wire up your agent, and handle deployment yourself. that works for prototypes. in production, you need retries, state management, crash recovery, observability, and scaling - and you end up building a runtime anyway.
the inference shell agent runtime starts where frameworks stop. you deploy agent logic - prompts, tool selection, business rules - and the runtime handles execution. durable state, automatic retries, human approval gates, cost tracking, and traces for every request. no infrastructure code in your agent.
the result: agents that survive 24-hour workflows, resume after crashes, wait for human approval, and compound knowledge across sessions. production-grade from day one.
looking for the full agents product - channels, triggers, flows, and hundreds of tools?
explore inference shell agentsan agent runtime is the infrastructure layer that executes ai agents in production. it handles the hard parts - state persistence, crash recovery, retries, scaling, observability - so you focus on agent logic instead of infrastructure.
ready to ship?
start with the hosted platform. deploy your own when you're ready.
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