Getting AI to work is easy. Getting it to work reliably across an enterprise is where things get difficult. Once AI enters a real business process, the questions change. What if the model hallucinates? How do we control token costs? What happens when an API fails? Can a person step in when AI is uncertain, and can we trace what happened later? Mantle brings the intelligence, workflows, and controls together to answer them.
Mantle treats AI as a first-class workflow component. Six purpose-built nodes (ai.classify, ai.extract, ai.transform, ai.generate, ai.llm, ai.embed) each handle one job, with structured, schema-validated output. Confidence thresholds and human approval mean an uncertain result can branch or pause, so an AI mistake doesn’t automatically become a business action. Managed connectors give AI real context from your CRMs, databases, email, and internal systems. For agents, the Brokered Agent (agent.broker, currently available for Claude models) keeps credentials inside Mantle. The agent states its intent, and Mantle allows it, denies it, or escalates it to a person, within turn limits and cost ceilings, with every decision recorded.
Around that sits the operational layer enterprises need: per-tenant token budgets and cost tracking, durable retries and recovery so failures never duplicate an action, and step-level observability tied to the workflow version that ran. Encrypted secrets, PII protection, and tenant isolation keep sensitive data protected. AI provides the intelligence. Mantle makes it operational, which is how enterprise AI moves from an impressive capability to a system that works at scale.
KEY TAKEAWAY
Enterprise AI succeeds or fails on reliability, not model quality. Mantle wraps AI in purpose-built nodes, guardrails with human approval, a brokered agent that never holds your credentials, cost controls, failure recovery, and full observability, so AI can run safely in production.