AI & Data
AI Agent
An AI agent is a software system that uses a large language model to reason about a goal, then autonomously takes multi-step actions such as calling tools, querying data, and updating systems to accomplish it.
What AI agents are used for
Unlike a chatbot that only answers, an agent acts. Given a goal, it plans steps, calls tools such as APIs, databases, and search, observes the results, and adjusts until the task is done. Practical examples include a support agent that looks up an order, checks refund policy, and issues the refund; a research agent that gathers and synthesizes information from multiple sources; and an operations agent that reconciles records across systems. Frameworks such as LangGraph and standards such as MCP provide the orchestration and tool connectivity that make agents buildable in production.
Why it matters for business software
Agents extend automation to work that rules-based systems could never handle, because each case requires judgment: reading context, choosing among actions, and handling exceptions. The business value comes with governance requirements. A production agent needs scoped permissions so it can only touch approved systems, human approval gates before irreversible actions, logging for audit, and evaluation to measure how often it succeeds. Companies that treat agents as governed software systems, rather than clever demos, are the ones that get durable value from them.
How Wizcoder AI Labs uses it
Building production agents is a core service: see AI agent development. We design the tool integrations, approval workflows, and evaluation harnesses around the model, and we connect agents to business systems through MCP servers as part of broader workflow automation engagements.
Related terms
Where we use AI Agent
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