# Arcta — Stop supervising agents. Start delegating to them.

> Arcta turns company judgment into dependable AI agent workflows that enterprises can delegate, govern, evaluate, and improve.

- Canonical URL: https://www.arcta.ai/
- Last reviewed: 2026-08-06

## The enterprise AI operating problem

AI can create more output without creating equivalent business capacity when senior people still correct the work, reviewers still make one-off risk decisions, and exceptions remain trapped with individuals. Arcta turns those standards, corrections, precedents, and exceptions into governed operating knowledge that agents can use and be evaluated against.

The homepage cites two third-party market-context figures: 88% use AI in at least one business function, from [The state of AI in 2025: Agents, innovation, and transformation](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai); and 5% achieve value at scale, from [Are You Generating Value from AI? The Widening Gap](https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap). These are third-party industry figures, not Arcta customer-performance claims.

## Canon, Refinery, Compiler, and Crucible

Canon is the authoritative record of how a company performs and judges its work. Refinery turns operating evidence into durable rules, precedents, exceptions, and evaluations. Compiler connects that knowledge to workflow state, tools, permissions, approval, and escalation. Crucible evaluates proposed and live behavior, gates changes, and diagnoses failures.

## Enterprise AI implementation

Arcta begins with a bounded workflow, establishes its current baseline, prepares company-specific context, connects approved systems, sets authority boundaries, evaluates representative and failure cases, pilots on controlled real work, and manages the workflow after launch.

## Control and sovereignty

Deployments can operate in customer-controlled or tenant-approved infrastructure. Existing identity and permissions determine who can view, draft, approve, and act. Company operating knowledge remains portable, and engagement-scoped files, prompts, outputs, and evidence are not used to train shared models.

## Canonical resources

- [Enterprise AI solutions](https://www.arcta.ai/ai-solutions)
- [AI implementation partner](https://www.arcta.ai/ai-implementation-partner)
- [Enterprise AI agents](https://www.arcta.ai/enterprise-ai-agents)
- [Enterprise AI knowledge base](https://www.arcta.ai/enterprise-ai-knowledge-base)
- [AI agent governance and evaluation](https://www.arcta.ai/ai-agent-governance-evaluation)
- [Selected work](https://www.arcta.ai/work)
- [Contact Arcta](https://www.arcta.ai/contact)