AI Agents
Published
October 22, 2026
Last updated
October 22, 2026
Definition
AI agents are software systems that carry out goal-directed, multi-step tasks for a team. An agent perceives data, reasons about the next step, and acts across connected tools within the permissions the team sets. Where agentic AI is the broader approach, agents are the individual systems that put it into practice.
In FP&A and business planning, AI agents take on recurring analytical work. An agent can run a monthly variance analysis with draft commentary or update a rolling forecast with the latest actuals. It can also flag spend anomalies or run scenarios on agreed drivers, often using forecasts from machine learning in FP&A.
Agents take AI-assisted planning past single answers: conversational analytics responds when asked, while an agent works through several steps and acts on what it finds. Teams decide which tasks agents handle and what data they can access. People review consequential changes, such as a forecast revision, before the agent applies them, and keep judgment on strategy.
Frequently Asked Questions
How are AI agents different from agentic AI?
Agentic AI is the broader approach of building AI that can pursue a goal, plan steps, and act. AI agents are the individual software systems that apply that approach to a specific task, such as a variance analysis or a forecast update.
How are AI agents different from chatbots or conversational analytics?
Chatbots and conversational analytics answer a question when you ask it, usually in a single exchange. An AI agent works toward a goal across multiple steps and can act in connected tools, such as preparing a forecast update for human review.
What tasks can AI agents handle in FP&A?
AI agents handle recurring work such as variance analysis with draft commentary, rolling forecast updates from the latest actuals, anomaly flagging, and scenario runs. FP&A teams review the output and keep judgment on strategy and final decisions.
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