How is Xorviex different from n8n, Make, or Zapier?

Those tools require you to manually define every node, connection, and trigger. Xorviex takes a plain objective and automatically architects the entire execution layer — agents, APIs, and sequencing — from scratch. There is nothing to build.

What is an Operation Unit (OU)?

One OU represents one atomic task executed by an autonomous agent — a scrape, a write, an API call, a validation pass. Complex missions consume many OUs in parallel. Unused OUs roll over monthly on Growth and Sovereign plans.

How does the Supervisor Layer prevent hallucinations?

Every agent output — text, data, API payload — is routed through an independent Supervisor model before it reaches any external system. The Supervisor has no access to execution context, so it audits outputs objectively. If it detects brand DNA mismatches, factual anomalies, or policy violations, it rejects the output and queues a regeneration cycle.

What happens if I never respond to an Idea Engine proposal?

If you set a timeout and don't act, Xorviex auto-applies the idea at the end of that window. Set it to Infinite and nothing is ever executed without your explicit approval. You can change the default timeout per workspace, per mission category, or per individual idea.

Does Xorviex monitor my production environment?

Yes. The Self-Healing Engine continuously watches logs, error rates, and uptime across connected services. When it detects a regression, it isolates the failing component, generates a patch, runs it through the Supervisor, and ships the fix — then reports exactly what changed.

Can I bring my own LLM or API keys?

Yes. Sovereign plan users can route agent computation through their own model providers and API credentials. The Meta-Agent orchestration layer and Supervisor remain Xorviex-managed.

Is my data used to train your models?

No. All mission data, document DNA uploads, and agent outputs are isolated per workspace and are never used for model training. Enterprise customers receive full data processing agreements.

What Are Autonomous AI Agents? The Complete Guide

Autonomous AI agents take a goal and execute it end-to-end — spawning sub-agents, calling APIs, and self-correcting — without human bottlenecks at every step.

Autonomous AI agents are software systems that receive a high-level objective and execute it independently — planning steps, invoking tools, spinning up sub-agents, and correcting errors along the way without a human in the loop at every decision point.

How Autonomous Agents Differ From Traditional Automation

Workflow tools like Zapier or Make require you to pre-define every node, trigger, and connection. If your process changes, you rebuild the workflow. Autonomous agents don't need blueprints. They reason about the goal, choose their own execution path, and adapt when something breaks.

The shift is from workflow orchestration to goal orchestration.

The Core Architecture

Every capable autonomous agent stack has three layers:

1. Meta-Agent — receives the objective and decomposes it into sub-tasks. Spawns and coordinates specialist agents for each task.

2. Execution Agents — domain-specific agents (scraper, writer, analyst, coder) that each own one step of the plan.

3. Supervisor — an independent validation layer that audits every output before it reaches the outside world. Rejects hallucinations, brand inconsistencies, and policy violations.

This architecture is exactly what Xorviex implements. Set the goal. The Meta-Agent spawns the army.

Why Businesses Are Switching to Agent-First Operations

- 24/7 execution — agents don't sleep, don't burn out, and don't block on approval chains

- Parallel scaling — one mission can run dozens of sub-agents simultaneously

- Self-healing — when a sub-agent fails, the orchestration layer retries or re-routes automatically

- Compounding intelligence — agents build on each other's outputs; each step gets better information than the last

What Makes a Good Autonomous Agent Platform?

Look for three non-negotiables:

1. A Supervisor layer — raw LLM output is not production-safe. Every action needs an independent audit pass.

2. Unlimited sub-agent spawning — the number of parallel threads determines your execution speed at scale.

3. Observable missions — you need real-time logs, status transitions, and rollback capability.

Getting Started

The fastest path to autonomous operations is not writing agents from scratch — it's using a platform that already has the orchestration, supervision, and mission tracking built in.

See Xorviex plans → | Start free →