A secure execution workspace where AI agents can code, calculate, and act

PhiVM gives AI agents an isolated, on-demand machine where they can generate code for external APIs, run autonomous tasks with an internal LLM, execute complex calculations, inspect files, and deliver verified results — without touching the user’s local environment.

It combines the control of a sandboxed virtual workspace with the speed of an AI-native development environment. Every agent receives its own reproducible runtime, task-specific context, file access, shell execution, package tooling, approved API access, and governed tool access from idea to execution.

Request a Demo

From AI reasoning to AI execution

Many AI assistants can answer questions. Real business workflows require more: agents need to inspect project files, create scripts, connect to external APIs, transform data, run tests, calculate results, generate artifacts, call tools, and verify their own work. PhiVM provides the controlled execution layer for that.

Instead of giving an agent direct access to a developer’s laptop, server, or production environment, PhiVM creates a separate runtime for each task. The agent works inside this controlled space, can operate with an internal or private LLM where required, produces outputs, and leaves a clear execution trail.

Simple explanation: PhiVM is a secure machine for AI agents. The agent can think, create API access code, run autonomous task loops, execute calculations, inspect files, and complete real tasks inside its own workspace — while your local environment stays protected.

Three capabilities that make agents operational

PhiVM is designed for situations where an AI agent must not only reason, but also execute controlled technical work. The machine can create code, run it, inspect results, and continue until the task is complete.

• Automatic code for external APIs: Agents can generate and run task-specific connector code for approved external APIs, including request logic, authentication handling, data mapping, validation, retries, and response processing.
• Autonomous task running with an internal LLM: PhiVM can run agent workflows with an internal or private LLM, allowing agents to plan steps, execute commands, inspect intermediate outputs, adapt, and continue without exposing work to a local environment.
• Complex calculations and data work: Agents can execute numerical scripts, data transformations, simulations, statistical checks, document calculations, optimization routines, and other compute-heavy tasks in a reproducible workspace.

How PhiVM works

Each Agent Virtual Machine is created for a task, supplied with the right context, tools, model configuration, API permissions, and calculation environment, and used as a reproducible execution workspace.

• 1. Create the workspace: An agent receives a dedicated runtime with task context, selected files, environment settings, internal or external LLM configuration, permissions, and tool access.
• 2. Generate and execute code: The agent can create scripts, API connectors, calculation routines, file processors, and validation tools, then run them inside the isolated machine.
• 3. Run autonomous task loops: With an internal or selected LLM, the agent can plan, execute, inspect intermediate results, adapt the next step, and continue until the task reaches the defined goal.
• 4. Verify the result: The agent can run tests, compare API responses, check calculations, inspect logs, validate generated files, and iterate before returning a final answer or artifact.
• 5. Return artifacts: Outputs can be returned to PhiStudio, PhiFlow, a user interface, an API endpoint, or another workflow step together with execution metadata.
• 6. Scale and clean up: Different agents can receive separate machines, making API jobs, calculations, and autonomous task runs easier to parallelize, audit, archive, or discard.

Key capabilities

PhiVM is built for agentic work where a language model needs more than a chat window. It gives agents the runtime needed to connect, calculate, act, test, and produce useful outputs.

• Isolated runtime per agent: Separate execution space for each task, agent, project, or customer workflow.
• Automatic API code generation: Create task-specific code to access approved external APIs, transform responses, and integrate external data.
• Autonomous internal-LLM execution: Run agent tasks with an internal or private LLM for controlled, on-premise, or customer-specific automation.
• Complex calculations: Execute numerical scripts, data transformations, simulations, statistics, comparisons, and validation routines.
• Filesystem access: Agents can inspect, edit, create, and package files in a controlled workspace.
• Shell execution: Run scripts, tests, commands, conversion jobs, and automated checks.
• Package tooling: Install and use task-specific libraries without polluting the host environment.
• Task-specific context: Mount only the files, prompts, Skills, tools, API credentials, and instructions the agent needs.
• Reproducible execution: Make agent work easier to repeat, debug, review, and scale.
• Skills and LLM flexibility: Use PhiSkills and different LLMs to match each task to the right model and capability.
• Governed tool access: Connect to approved PhiBox, PhiStudio, PhiFlow, API, or MCP-style tools under clear rules.
• Execution trace: Keep a clearer trail of generated code, commands, intermediate outputs, calculations, and final artifacts.

The execution layer in the Phi architecture

PhiVM complements the existing Phi product family by giving agents a place to perform controlled work.

• PhiBox: Private knowledge, document search, RAG and citations
• PhiStudio: Agent configuration, prompts, tools and governance
• PhiSkills: Reusable task logic and structured AI capabilities
• PhiVM: Secure execution for code, APIs, calculations and agent actions
• PhiFlow: Workflow orchestration, approvals and business processes

For product managers: Define what an agent should do, connect the right Skills, sources, APIs, and model configuration, and let the agent execute in a controlled workspace.

For developers: Give agents a clean runtime to inspect repositories, generate API connector code, run tests, execute calculations, prepare artifacts, and automate repeatable engineering tasks.

For enterprise teams: Keep API access, calculations, files, and autonomous task runs separated by task, customer, project, or environment while maintaining governance, reviewability, and deployment flexibility.

Typical use cases

External API automation

Let agents generate API access code, call approved services, combine external data with PhiBox knowledge, validate responses, and return structured results.

Autonomous internal-LLM workers

Run controlled task loops with an internal LLM for customer-specific automation, private deployments, recurring jobs, and workflow steps that need local governance.

Complex calculations

Execute Python scripts, data transformations, statistics, simulations, scoring models, document calculations, and validation checks in a reproducible runtime.

Code analysis and test execution

Let agents inspect a repository, identify problems, modify files, run tests, and return a verified patch or recommendation.

AI workflow automation

Combine PhiSkills, Tool Calls, project context, API connectors, and calculation scripts so agents can execute repeatable business tasks without manual handover.

Controlled customer workspaces

Give each customer, project, or workflow a separate agent machine so data, tools, API access, calculations, and results stay clearly separated.

Why PhiVM matters

Agentic AI becomes much more useful when agents can safely act on files, APIs, calculations, tools, and workflows. PhiVM turns an AI agent from an advisor into an executable worker — while keeping that work separated, reviewable, and reproducible.

• Safer execution: Agents work in a controlled environment instead of the user’s local machine.
• Faster delivery: Agents can complete practical tasks without waiting for manual API scripting, file handling, calculation setup, or developer handover.
• Better reproducibility: Task environments can be repeated, reviewed, and debugged more reliably.
• More flexible AI systems: Use different LLMs, Skills, and tool configurations depending on the task.

Let AI agents code, calculate, and complete real work — safely

PhiVM gives every agent its own secure workspace to generate API code, run autonomous internal-LLM tasks, execute complex calculations, validate results, and deliver. It is the practical runtime layer for agentic AI products, enterprise workflows, and developer automation.

Request a Demo