Stateless runtime for AI skills

Wrap skills. Unleash agents.

BaoBox is the runtime for production-ready AI skills.

Define a skill as prompt, tools, and model params. BaoBox turns that into a stateless agent API endpoint without Docker, servers, networking, orchestration, eval infra, or observability work.

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Pre-launch now. We are opening early access with a small group of design partners and early teams.

30 min to production

Stateless scale-to-zero runtime

Step evals built into every run

name: support-agent
prompt: |
  You are Acme's support triage agent.
tools:
  - searchTickets
  - createIncident
model:
  provider: openai
  name: gpt-4.1-mini
  temperature: 0.2

POST

/v1/skills/support-agent/run

200 OK trace_id: bxb_0182a7

{
  "output": "Issue routed to infra-oncall",
  "steps": 4,
  "eval_score": 0.94
}

Problem

Shipping agents still feels like building a platform.

Teams start with a prompt, then inherit everything around it: tool wiring, endpoint hosting, logs, eval loops, retry logic, scaling, auth boundaries, and production debugging.

Too much runtime glue

Prompt logic, tool definitions, model settings, network boundaries, and worker orchestration end up spread across too many systems.

Infra cost before product value

You ship hosting, queues, containers, and logging pipelines before the first skill proves it should exist.

Harder to trust in production

Without step-level traces and evals, every agent bug turns into forensic work across prompts, tools, and model behaviour.

Solution

Define a skill. Get an API.

BaoBox treats skills as the deployable unit. You package the system prompt, attach tools, set model parameters, and BaoBox handles the rest of the runtime surface.

Prompt

Tools

Params

→

Production agent API

Stateless runtime with scale-to-zero economics
Vendor-agnostic model and tool execution layer
Built-in eval and observability from day one

BaoBox skill diagram showing tools, system prompt, and model parameters packed into one runtime unit

How it works

One packaging step. Four runtime outcomes.

Package

Define the skill with system prompt, tools, model selection, and runtime parameters.

Deploy

BaoBox turns the skill definition into a production-ready API endpoint with no server layer to manage.

Run

Call the endpoint from apps, workflows, back office tools, or customer-facing products.

Observe

Inspect traces, step-level evals, tool execution, and response quality without bolting on another stack.

Differentiators

Built for teams that want agents in prod, not agent infra.

30 min to production

Ship an endpoint fast enough to validate value before infra grows around it.

Scale-to-zero

Keep idle skills cheap, then wake them up when workloads actually arrive.

Enterprise risk reduction

Closed-source runtime boundaries lower the operational and compliance burden.

Vendor-agnostic runtime

Swap models and providers without rewriting the platform around each skill.

Step-level evals and observability

Trace every run, inspect every step, and tighten behaviour with real evidence.

Use cases

Deploy skills wherever work can be described and tool use matters.

Internal AI agents

Ops, support, revops, legal, and finance helpers behind authenticated APIs.

Data processing

Classification, extraction, summarisation, and routing in production pipelines.

Workflow automation

Trigger tools, evaluate outputs, and hand results back into existing systems.

Customer support agents

Wrap retrieval, triage, ticketing, and escalation logic behind a single endpoint.

Content generation

Run brand-safe generation workflows with explicit tool and model controls.

Tool-using copilots

Expose secure skill APIs to apps that need action-taking, not chat demos.