What is an AI agent in a company?
An operational definition without jargon, from a partner that deploys them every week.
An enterprise AI agent is neither a chatbot nor rigid RPA. It takes a high-level instruction, decides which steps to execute, acts in your systems, checks its work and stops when the result is obtained. Production requires precise business context, secure access, explicit guardrails and a feedback loop for errors.
What is the difference between a chatbot, an assistant and an agent?
A chatbot answers. An assistant helps formulate a draft that you approve. An agent acts. Asked to follow up with 50 leads that have not replied for seven days, it reads the CRM, selects the right profiles, prepares a personalized message, checks that no similar message has already been sent, sends through Gmail and logs the result. You receive the outcome, not the steps.
What are the technical components of an enterprise AI agent?
Four layers: a language model that reasons and decides; tools connecting databases, business APIs, code and files; memory containing operational vocabulary, constraints, processes and history; and guardrails covering prohibited actions, mandatory human approvals and a complete audit log.
Which use cases work in companies today?
Three families hold up in production: preparing decisions from scattered signals; repetitive execution requiring judgment, such as lead qualification or support triage; and mediation between heterogeneous systems, such as moving data from an ERP to a CRM.
How long does it take to put an agent into production?
With a well-defined case and access to the right systems, a first working agent can take a few days and production deployment two to three weeks. The delay is not the code. It is mapping the real process, defining guardrails and validating the system with operators.
What are the risks and how are they managed?
The main risks are an incorrect action at scale, factual invention and behavioral drift. Human approval protects sensitive actions, internal sources ground factual work, and observability records every decision for regular review.
How is an AI agent different from traditional RPA?
RPA follows a fixed deterministic script and breaks when the interface changes. An agent follows a high-level instruction, adapts to variations and handles cases that were not anticipated during the build. RPA remains useful for stable, high-volume operations. Agents fit work that requires contextual judgment.