Conversational AI systems that respond to text or voice input using natural language understanding, handling questions, guiding users through processes, or completing tasks on their behalf. For agencies, chatbots went from scripted FAQ trees to genuinely capable client-facing tools the moment large language models became practical to deploy.
Also known as AI chatbots, conversational AI bots
An AI-powered chatbot uses language models to interpret what a user is asking and generate a useful response. Unlike earlier rule-based chatbots that matched keywords to scripted replies, modern chatbots understand context, handle follow-up questions, and maintain coherent conversation threads. The underlying capability is a large language model, usually constrained to a specific domain or task by the system design around it.
Chatbots can be configured to stay on-topic by restricting what subjects the model will address, providing a knowledge base the model retrieves answers from, and setting guardrails on the types of responses it generates. Well-designed ones also know when to escalate to a human rather than attempting an answer they are not equipped to give reliably.
For agencies, the relevant categories are client-service chatbots built for brand or e-commerce clients, internal productivity bots that assist with research or briefing, and the chat interfaces embedded in AI creative and workflow tools used daily.
Chatbots are among the most publicly visible AI deployments a brand can make. When an agency recommends, builds, or manages a chatbot for a client, every conversation that chatbot has is a brand interaction. The stakes are higher than the technical complexity might suggest.
Brand voice at scale. A chatbot handling thousands of conversations per day is expressing brand voice at a volume no human team could match. Getting the tone, language, and persona calibrated before launch is essential. Recalibrating after public complaints is much harder.
Hallucination risk is public. AI chatbots can produce confident, wrong answers. When that happens in a customer service context, it is a brand reputation event. Agencies deploying or advising on chatbots need to understand hallucination risk and what mitigation looks like in practice, not just in theory.
Internal productivity bots carry a different risk profile. Not all chatbots face customers. Many agencies use internally deployed bots for research, briefing support, and document summarization. These carry lower reputational risk but raise data governance questions that still need clear answers before deployment.
An agency launches a customer service chatbot for a retail client ahead of peak shopping season. Before go-live, the team runs a structured evaluation: testers try to get the bot to contradict the return policy, recommend discontinued products, or respond off-brand. Every failure gets logged and addressed. The bot launches with documented limitations and a clear escalation path to human agents for anything it handles poorly. Three weeks in, the client’s human support volume for routine inquiries is down significantly. The remaining tickets are the hard ones, which is where human judgment should have been focused all along.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.