What is an AI Agent?
An AI agent is a system where a language model can take actions — calling tools, making decisions, and running multiple steps — to complete a goal autonomously.
AI model vs AI agent
A standard AI model responds to a prompt and stops. An agent can: search the web, read a file, call an API, write code, check the result, and retry if it fails — in a loop until the task is done. The model drives the decision-making; the 'agent' is the architecture around it.
Real business examples
A sales research agent: given a company name, it searches Apollo, reads the company website, pulls LinkedIn data, checks for recent news, and produces a structured brief — all autonomously. An ops agent: monitors a dashboard, identifies issues, creates tickets, and notifies the right team. These tasks that once took an hour happen in minutes.
How we build agents
We typically build agents using Claude's tool use API, combined with Make or n8n for orchestration. The model decides which tools to call; the automation platform executes them. This approach is more reliable and observable than pure agent frameworks, and easier to debug in production.
Current limitations
AI agents work best on structured, defined tasks with clear success criteria. They struggle with tasks that require real human judgment, highly ambiguous goals, or workflows where errors have serious consequences without human review. Most production agents work best with a human in the loop for final decisions.
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