Install Jentic One Beta
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Cloud World Model — Agent API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcloudworldmodel.ai%2Fcloudworldmodel" | shStep 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcloudworldmodel.ai%2Fcloudworldmodel" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
What an agent can do with Cloud World Model — Agent API.
Create cloud infrastructure simulations from scenario templates and run them synchronously or step by step
Inject traffic and node failures to stress-test a simulated deployment
Break down per-resource cost and validate it against provider benchmarks
Submit infrastructure optimization jobs and generate autoscaling threshold recommendations
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Explore multi-cloud deployment strategies and stream results as they compute
Create reinforcement-learning environments over a simulation and advance them by action
Generate natural-language explanations, bottleneck analysis, and troubleshooting for a simulation
Patterns agents use Cloud World Model — Agent API for, with concrete tasks.
★ Agent-Driven Infrastructure Modeling Through Jentic
An AI agent planning a cloud change can model it in the Cloud World Model through Jentic before touching real infrastructure. The agent creates a simulation from a scenario, runs it, reads the cost breakdown and bottleneck analysis, and only then recommends a provisioning step. Because simulations are addressed by id, the agent's reach is easy to bound to one run.
Create a simulation from a scenario template, run it statelessly, and read the per-resource cost breakdown to compare against the current setup
Pre-Deployment Cost and Performance Modeling
Before committing a deployment, a team models it and validates the projected cost and performance against provider benchmarks. The simulation returns a per-resource cost breakdown and accuracy validation, and right-sizing hints suggest cheaper resource choices that still meet the workload.
Fetch a right-sizing hint for a simulation, apply it, and validate the updated cost and performance accuracy against benchmarks
Chaos and Failure Testing in Simulation
Resilience testing runs against the model instead of production. A workflow injects targeted node failures or a chaos test, observes how the simulated deployment degrades and recovers, and schedules failure injections to rehearse incident response.
Run a chaos test against a simulation, then run recovery for the failed resource and confirm the deployment returns to a healthy state
Reinforcement-Learning Environments Over Cloud Simulations
The simulation can be turned into a reinforcement-learning environment so a policy learns to operate cloud infrastructure. An agent resets the environment, reads the observation, takes an action step, and submits eval-episode jobs to measure how a policy performs over many episodes.
Create an RL environment over a simulation, reset it, read the observation, and run one action step to advance the episode
54 endpoints — the cloud world model api simulates how a cloud deployment behaves so an agent can model cost and performance before provisioning anything real.
METHOD
PATH
DESCRIPTION
/simulations
Create a cloud simulation
/simulations/stateless
Run a stateless synchronous simulation
/simulations/{simulationId}/cost-breakdown
Fetch the per-resource cost breakdown
/chaos/run
Run a chaos engineering test
/analysis/optimize
Submit an infrastructure optimization job
/multi-cloud/explore
Search multi-cloud deployment strategies
/rl/environments
Create an RL training environment
/scenarios
Search available simulation templates
/simulations
Create a cloud simulation
/simulations/stateless
Run a stateless synchronous simulation
/simulations/{simulationId}/cost-breakdown
Fetch the per-resource cost breakdown
/chaos/run
Run a chaos engineering test
/analysis/optimize
Submit an infrastructure optimization job
/multi-cloud/explore
Search multi-cloud deployment strategies
/rl/environments
Create an RL training environment
/scenarios
Search available simulation templates
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Cloud World Model API by hand means handling bearer tokens or wallet session JWTs for authenticated calls and x402 USDC micro-payments on Base or Solana for metered ones, and tracking which operations carry a price. Through Jentic you install once, import Cloud World Model from the API Directory, store the token once, and your agent calls it.
Permission scoping
Cloud World Model puts the simulation id in the URL path (the /simulations/ prefix), so a rule can pin your agent to one simulation and the operations you choose. You choose the operations it may call, so destructive ones like deleting a failure injection or cancelling an RL environment are not included unless you add them.
Credential isolation
Your Cloud World Model token is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as simulate a cloud deployment or run a chaos test, and Jentic returns the matching Cloud World Model operation with its input schema so the agent calls the right endpoint without reading the reference docs.
Alternatives and complements available in the Jentic catalogue.
Specific to using Cloud World Model — Agent API through Jentic.
What authentication does the Cloud World Model API use?
The Cloud World Model API authenticates with a bearer token, which may be an API key or a wallet session JWT, per its OpenAPI spec. Metered operations can instead be paid per call with an x402 USDC micro-payment in the X-PAYMENT header on Base, or with a Solana payment signature, and some discovery and status operations are open. Through Jentic the token is stored encrypted by your own Jentic One instance and injected at call time.
Can I run chaos tests with the Cloud World Model API?
Yes. You can run a single chaos engineering test or a batch of them in parallel against a simulation, and inject deterministic traffic or targeted node failures to see how the modeled deployment responds. Recovery operations then run recovery for a single failed resource.
Is the Cloud World Model API free to use?
Most operations are metered: the spec marks them with x402 prices you pay per call in USDC, while a handful of discovery and status operations carry no per-call price. Fetch the current prices from the x402 config operation before running a metered call, and do not assume every read is free.
How do I simulate a cloud deployment with the Cloud World Model API through Jentic?
Install Jentic One, then import Cloud World Model from the API Directory and store your token once. Your agent searches by intent such as create a cloud simulation, and Jentic returns the create-simulation operation with its input schema so the agent supplies the scenario and reads the simulation back. To run it on your own infrastructure, install Jentic One from its GitHub repo.
Is there a Cloud World Model API MCP server?
You don't need an MCP server to give your agent the Cloud World Model API. Jentic connects it directly from the API Directory: import it, store your token once, and your agent calls the simulation, chaos, and optimization operations on demand. Operations are discovered when the agent needs them, so none sit in its context until a call is made.
Can I limit what my agent is allowed to do with the Cloud World Model API?
Yes. Because the simulation id sits in the URL path, you can write a rule that pins your agent to one simulation and allows only the operations it needs, such as creating steps and reading cost breakdowns. Deleting failure injections or cancelling RL environments stays out of that set unless you add them, and every call is logged.
For Agents
Simulate cloud deployments to model cost and performance, run chaos tests, explore multi-cloud strategies, and train over reinforcement-learning environments without provisioning real infrastructure. Pay per call with x402 or use a bearer token.
Use for: I want to simulate a cloud deployment before I provision it, Run a chaos test against a simulated cluster, Get the per-resource cost breakdown for a simulation, Explore multi-cloud strategies for a workload
Not supported: Does not provision real cloud resources, manage live production infrastructure, or send bills. Use for simulating, testing, and optimizing cloud deployments only.
The Cloud World Model API simulates how a cloud deployment behaves so an agent can model cost and performance before provisioning anything real. It creates simulations from scenario templates, runs them synchronously or step by step, injects traffic and node failures, and returns per-resource cost breakdowns validated against provider benchmarks. On top of the simulation it offers chaos testing, multi-cloud strategy exploration, autoscaling threshold prediction, reinforcement-learning environments, and natural-language analysis of a run. Metered operations can be paid per call with x402 USDC micro-payments on Base or Solana, or accessed with a bearer token.
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
Base layer of spec validity and structural soundness.
Aggregated quality score from linter diagnostics, weighted by severity.
Percentage of `$ref` references that resolve successfully.
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
Structural correctness score based on schema issues using logarithmic dampening.
Clarity, completeness, and ingestion readiness for developers and tooling.
How richly the API is illustrated with examples.
Percentage of examples that conform to their schemas.
Percentage of operations with complete response definitions (success, client error, server error).
Health of API ingestion, bundling, and resolution within Jentic pipelines.
Semantic breadth, depth, and agent comprehension for AI systems.
Coverage of descriptions across API elements.
Coverage of RFC 9457 Problem Details for error responses.
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
Coverage of summaries across operations/tags/info.
Functional utility, complexity comfort, and AI orchestration readiness.
Agent comfort level based on API operational and structural complexity.
Trust, risk posture, and security compliance.
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
Findability, semantic richness, and reasoning readiness.
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>