An AI agent is an LLM-powered system that can autonomously use tools, access data, and complete tasks — as opposed to a simple chatbot that only responds to single prompts. AI agents have memory, can plan sequences of actions, and often have access to APIs, browsers, databases, and code execution. In marketing, AI agents are starting to replace entire workflow categories: research, list building, content personalization, and qualification.
For example, an AI agent built on GPT-4 with access to LinkedIn, Clearbit, and your CRM can research a prospect account, identify the right contacts, check if they're already in your system, score their fit, and draft an outreach — all triggered by a single instruction.
We deploy AI agents in our client tech stacks to handle the research and personalization work that used to require junior team members — freeing senior marketers for strategy.
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Book a free strategy call →Agentic AI
Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — going beyond generating text to actually doing work.
Autonomous Workflow
An autonomous workflow is a multi-step automated process that runs without human intervention — trigger, conditions, actions, branches, and loops all executing on schedule or in response to events.
Human-in-the-Loop (HITL)
Human-in-the-loop describes AI automation workflows that include a human review or approval step before consequential actions are taken — particularly sending outreach, making calls, or publishing content.
Large Language Model (LLM)
An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.
Retrieval-Augmented Generation (RAG)
RAG is an AI architecture that combines retrieval (pulling relevant information from a knowledge base) with generation (using an LLM to produce output).