The most predictable runtime for AI agents
AOS is a machine-native SDK for building deterministic, replayable agents with skills, budgets, safety guardrails, and observability built-in. Stop babysitting — start shipping.
What is AOS?
Machine-native agent runtime
AOS sits between your LLMs, tools, and infrastructure — providing a deterministic execution layer with full observability.
Deterministic & Replayable
Every run is reproducible. Debug, replay, and audit agent behavior with full execution traces.
Budget & Safety Guardrails
Rate limits, cost caps, approval workflows, and prompt injection protection built into the runtime.
Full Observability
Prometheus metrics, structured logging, audit trails. Know exactly what your agents are doing.
How it works
Three steps to production agents
Define Skills & Contracts
Register skills with explicit capabilities, rate limits, and resource contracts. AOS knows what each skill can do before execution.
Simulate Before Running
Use runtime.simulate() to check feasibility, estimate costs, and validate plans before committing resources.
Execute with Full Control
Run workflows with automatic checkpointing, structured outcomes, and complete audit trails. Failures are data, not exceptions.
Why AOS?
Built for production, not demos
The Problem
- Agent frameworks designed for humans to babysit
- Opaque failures that require log archaeology
- No cost control until the bill arrives
- Non-deterministic behavior across runs
The AOS Way
- Machine-native APIs agents can query directly
- Structured outcomes — failures are navigable data
- Budget enforcement per-run, per-day, per-model
- Replay any run with identical behavior
Quickstart
Up and running in 5 minutes
Install the SDK, connect to the API, and run your first simulation.
# Install Python SDK
pip install aos-sdk
# Or JavaScript/TypeScript
npm install @agenticverz/aos-sdkfrom aos_sdk import AOSClient
# Initialize client
client = AOSClient(
base_url="https://api.agenticverz.com",
api_key="your-api-key"
)
# Check capabilities
caps = client.capabilities()
print(caps.skills, caps.rate_limits)
# Simulate before running
result = client.simulate(plan={
"steps": [{"skill": "http_call", ...}]
})
if result.feasible:
run = client.execute(plan)SDKs & Resources
Available now
Ready to build predictable agents?
Get started with AOS today. No credit card required.