AI Agent

An AI agent is a software system built around a language model that can autonomously plan, execute multi-step tasks, and interact with external tools and services to achieve a goal. Unlike a simple chatbot that responds to a single prompt, an agent maintains state across steps, decides which actions to take, observes the results, and adjusts its plan accordingly. The agent paradigm emerged as practitioners realized that many valuable LLM applications require more than one-shot generation. A research agent might need to search the web, read several pages, synthesize findings, and then verify claims against additional sources. A data collection agent might navigate a website, extract structured information from multiple pages, and consolidate results into a report. Each of these workflows involves loops of reasoning and action that a single prompt cannot accomplish. Most agent architectures follow a common pattern: the model receives a goal, generates a plan, selects a tool to execute the next step, observes the tool's output, and repeats until the goal is met or a stopping condition is reached. The tools available to the agent define its capabilities — an agent with access to a web search API can look up current information, one with a browser automation tool can interact with web applications, and one with a code execution environment can run computations. For product builders, the critical questions around agents are reliability, cost, and latency. Agents make multiple LLM calls per task, each of which costs tokens and adds latency. An agent that takes thirty seconds and $0.50 of API calls to answer a question may not be viable for a consumer product but could be perfectly acceptable for a B2B research tool. Error handling is another concern: agents can get stuck in loops, misinterpret tool outputs, or pursue unproductive strategies. Web data access is often the most important capability for useful agents. An agent without internet access is limited to the model's training data. Tools like Browserbase, Stagehand, and AI search APIs give agents the ability to observe and interact with the live web, which is what makes them practical for real-world tasks.