Agentic AI & Automation

A chatbot produces text; an agent completes work. Agents built by Neuros call tools, plan across multiple steps, verify intermediate results and back out when needed. The difference is measurable: a chatbot's output is an answer, an agent's output is a changed record in a system — an issued invoice, an updated stock level, a sent quote.

Every irreversible action is gated behind human approval, so an agent never moves money or deletes records on its own. Reversible actions carry a defined compensating step, and every step sits in an immutable log. Neuros runs agents in shadow mode for weeks first: decisions are produced, never applied, and compared against the human decision.

Neuros agents connect to enterprise systems such as SAP, Logo, NetSuite, Salesforce and HubSpot, as well as your own internal services. The connection is never a direct database link but a contracted tool layer: every tool declares what it does, which permission it requires and how it fails back. Model Context Protocol (MCP) is the standard carrier for that layer.

Every agent run carries three ceilings: number of steps, number of tool calls and spend in currency. A run that hits a ceiling stops and hands over to a human. Neuros also tracks cache hit rate, model selection and context length separately — most of the cost usually comes from needlessly long context, not from the model's price.

Text arriving from outside enters the agent as data, never as instruction; system prompt and user content are kept in separate channels. Tool calls are permission-scoped, so even a hijacked prompt cannot execute an action the agent was not granted. The OWASP Top 10 for Large Language Model Applications is added to the regression suite as test cases.