Give AI agents execution, not access.
Run AI-generated code inside isolated sandboxes. Credentials stay under your control. Agents never see secrets.
The agent should not hold your secrets.
AI agents need to run code, query databases, call APIs and automate workflows. But giving them raw credentials creates a new security boundary problem.
A runtime boundary between agents and your systems.
Jhansi gives AI agents a controlled place to execute work without directly exposing your machine, environment or credentials.
Connect through MCP or the Python SDK.
Create a sandbox, run generated code, collect output and clean up.
Credentials are injected during execution, not handed to the agent.
Centralized audit, policies and enterprise controls as the platform matures.
Start with a simple self-hosted runtime.
Jhansi focuses on the execution boundary first. Governance can grow later when real teams and real systems are involved.
Self-hosted
Run inside your own infrastructure without depending on a cloud execution provider.
Sandboxed execution
Execute generated code away from the user's local machine and environment.
Secret isolation
Agents get results, not raw passwords, tokens or connection strings.
Audit ready
Build towards logs, run history and reviewable execution records.
For teams adopting AI agents.
Jhansi is for builders who want AI agents to execute useful work without handing them unrestricted access.
AI developers
Run generated code safely while building agents and MCP workflows.
Platform teams
Standardize how agent-generated code runs inside your environment.
Regulated teams
Prepare for auditability and governance before agents reach sensitive systems.
Stop giving credentials to AI agents.
Run generated code safely, keep secrets controlled and make every execution easier to reason about.