Software that automates repetitive tasks by interacting with systems or users at machine speed, from crawling web pages to handling customer queries. Agencies build and deploy bots to automate intake, content distribution, and client-facing support workflows, which makes understanding their limits as important as knowing their capabilities.
Also known as software bot, web bot, automated agent
A bot is software that performs automated tasks by following rules or responding to inputs without requiring human intervention for each action. The category spans a wide range: a simple web crawler that indexes pages is a bot; so is a sophisticated conversational system that handles customer support inquiries.
Chatbots are a subcategory of bots designed for conversational interaction. Earlier chatbot implementations used scripted decision trees that matched user inputs to predefined responses. Modern conversational bots layer language model capabilities on top of that structure, enabling more flexible, less scripted interactions while maintaining defined boundaries around what the bot can and cannot do.
Workflow automation tools make extensive use of bots to connect systems and trigger actions across platforms: a bot that monitors a form submission, creates a CRM record, sends a confirmation email, and notifies a team member is a simple example of multi-step automation. Understanding what a bot can do in each step is necessary for designing reliable workflows.
Agencies are increasingly responsible for bot deployments on behalf of clients, from campaign-specific chatbots to always-on support automations. They are also responsible for media buys that may be contaminated by bot traffic. Understanding the technology from both sides, as a tool to deploy and a threat to defend against, is increasingly part of the agency’s professional obligation.
Client-facing bots carry brand risk. A chatbot deployed under a client’s brand that produces unhelpful, inaccurate, or inappropriate responses reflects on the agency that built or recommended it. Chatbot quality assurance, including coverage testing, edge case identification, and escalation path design, is as much a creative and brand responsibility as a technical one.
Bot traffic distorts campaign measurement. Programmatic ad campaigns regularly serve impressions to bots rather than humans. Invalid traffic detection filters out much of this, but not all. Agencies reporting campaign performance should understand what IVT filtering their measurement partners apply and what percentage of recorded impressions their validation processes verify as human.
Automation scope must be defined clearly. Bots excel at defined, repeatable tasks and fail at edge cases requiring judgment. Agencies designing bot-assisted workflows need to map the boundary between what the bot handles and what escalates to a human, and test that boundary with realistic inputs before going live.
An agency builds a lead qualification chatbot for a SaaS client’s website. After launch, the client reports that several enterprise prospects had a poor experience and mentioned the bot interaction in sales calls as a reason for hesitation. Review of the transcripts shows the bot was handling complex pricing questions by repeating a scripted answer that did not match the prospect’s specific inquiry. The agency rewrites the escalation logic to route any pricing question with a deal size above a threshold directly to a sales calendar tool. The bot’s job becomes narrower and the experience becomes better.
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.