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Platform

The building blocks under every AI employee

An AI employee is a composition, not a product feature. These are the parts you assemble — and the parts you can inspect when something goes wrong.

Agents

Named AI employees with their own instructions, model, skills, tools and approval policy.

Workflows

Multi-step sequences that chain agents, tools and conditions into a repeatable process.

Skills

Packaged instructions and procedures an agent can load for a specific job.

Plugins

Connector packages that give agents authenticated access to a product's API surface.

MCP

Configure Model Context Protocol servers so agents can use external tools and resources.

Knowledge

Your documents and data, retrievable by agents so answers stay grounded in your material.

Approvals

External sends and write actions require human approval unless you explicitly enable autonomy.

Scheduling

Recurring runs on a cron-style schedule, with full history of what each run did.

Analytics

Run history, credit usage and outcome reporting across every agent in the workspace.

Execution model

One path, always inspectable

Runs are recorded step by step: which skill loaded, which model answered, which tool was called and what the human approved.

  1. 1.Goal — You describe the outcome in plain language.
  2. 2.AI Employee — A named agent owns the goal, with its own scope and policy.
  3. 3.Skill + Model — The agent loads the right skill and runs on your chosen model.
  4. 4.Plugin / MCP — Authenticated tools give it access to your real systems.
  5. 5.Approval policy — Write actions and external sends wait for a human unless you allow autonomy.
  6. 6.Real action — Email sent, record written, report published, campaign scheduled.
  7. 7.Run history — Every step, tool call and output is logged and reviewable.
Browse integrationsMCP support
Enverif workflow builder canvas with manual trigger, webhook trigger, AI agent and output nodes plus a node inspector
The workflow builder: triggers, AI agents, plugin actions, human approval, conditions and delays on one canvas.
Enverif workspace overview showing agents, leads, campaigns, approvals, recent runs and next schedules
Workspace overview — active agents, pending approvals, recent runs and the next scheduled job in one place.

Execution trace

Every action keeps its context

Inspectable
  1. 1

    Load the assigned agent instructions and skills

    The next step runs only with the tools and permissions assigned to this agent.

  2. 2

    Select the configured model and knowledge context

    The next step runs only with the tools and permissions assigned to this agent.

  3. 3

    Call only the connected plugin or MCP tools

    The next step runs only with the tools and permissions assigned to this agent.

  4. 4

    Require approval for governed write actions

    The next step runs only with the tools and permissions assigned to this agent.

  5. 5

    Retain the run history for review

    The result and tool activity remain available in the run history.

Tools are scoped
Writes can require approval
Run history retained

Put your first AI employee to work this week.

Start on Enverif Cloud, or clone the repository and self-host. Approvals are on by default either way.

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