Agentic AI combines a model with tools and a control loop to work toward a goal across several steps. The system can inspect results and choose another action; its reliability depends on the implementation, permissions and checks.
Autonomous AI agents extend their capabilities by calling external tools during execution. Common tools include web search, code interpreters, file read and write operations, calendar access, and API calls to external services. The model decides when to use a tool and what to pass to it, then incorporates the output into its next reasoning step.
After each tool call or action, an agentic system observes what happened and adjusts. If a web search returns insufficient results, it tries different search terms. If code it generated throws an error, it reads the error message and revises the code. This cycle of action, observation, and revision is what allows complex tasks to be completed without human guidance at each step.
A user provides a high-level objective: "summarize all customer complaints from last month," "book the cheapest flight to Berlin before Thursday," or "identify which of these contracts contains non-standard liability terms." The system receives the goal as input but is not given instructions on how to accomplish it.
The language model at the core of the system generates a plan. It reasons about what steps are necessary, what tools are available, and in what order to proceed. A common framework for this is the ReAct pattern, introduced by Yao et al. in 2022, which interleaves reasoning traces with concrete actions. The model thinks through what it should do, does it, observes the result, reasons about what to do next, and continues.
The plan is not fixed. If early steps produce unexpected results, the model revises its approach rather than following a predetermined script.
Execution requires reaching beyond the model's internal knowledge. The system calls tools: a web search to retrieve current information, a code interpreter to run calculations, a database query to pull structured records, or an API call to take an action in an external system. Each tool call is chosen by the model based on what the current step requires.
The output from each tool becomes input to the next reasoning step. The model reads what the tool returned and decides whether to proceed, repeat, or change direction.
If a file write fails, the system reads the error. If a search returns irrelevant results, it reformulates the query. If a code block produces incorrect output, it debugs. The loop continues until the goal is reached or the system determines it is stuck.