An AI agent framework is software for building AI agents: programs in which a language model is given tools, memory and a goal, and decides step by step what to do to reach the goal. The framework connects the model to a company's own systems, such as its email, its ERP or its customer records, and runs the agent inside those systems. ERP, or enterprise resource planning, is the software that records a company's purchases, stock and accounts.
A language model on its own can only write text. An AI agent can also act, and the framework makes this possible with a loop. First, it gives the model the goal and a list of tools, each with a short description, such as 'look up a purchase order by its number'. The model replies with the tool it wants to use and the inputs. The framework runs that tool, for example by querying the ERP, and passes the result back to the model. The model then chooses the next step. The loop continues until the model reports that the goal is reached, or until a limit is hit.
The framework also keeps memory. Short-term memory is the record of the current task. Long-term memory usually comes from documents, through retrieval-augmented generation (RAG). Some frameworks also store how people, suppliers and documents are connected, in a graph database.
Say the procurement team of a cement company in Raipur receives 400 supplier invoices a week by email. An agent built on a framework reads each invoice and looks up the matching purchase order. It then checks the goods receipt, the record of what actually arrived at the plant. In one invoice, the supplier has charged for 120 tonnes of coal, but only 110 tonnes arrived. The agent does not send that invoice for payment. Instead, it writes a note to an employee in the accounts team, with the three documents attached. Invoices that match are sent on for payment approval.
Limits matter as much as tools. The framework can stop an agent after 20 steps, require a person's approval before any payment, and allow each agent only the systems it needs. This is the rule of least privilege, and the page on AI agent guardrails explains why it matters. Each agent also needs its own login, which AI agent identity and security products provide. Before release, teams test agents with LLM evaluation.
Some frameworks are free and open source, and their makers sell support and hosted versions, as in the open source business model. How is a framework different from AI agents for enterprise work? A framework is a toolkit for a company's own engineers. An enterprise agent platform sells ready-made agents that do one role's work.