AI agents are artificial intelligence that use tools to accomplish goals, remember across tasks and changing states, use one or more AI models to complete tasks, and decide when to access internal or external systems on a user's behalf.

What does an AI agent do in a consumer goods company?

In a consumer goods company, the AI agent gathers data on a weekly basis, autonomously joining marketing data via connected data pipelines. It analyzes performance metrics and compares against expectations, receives business context from an operator when necessary, offers recommendations by writing a standardized report that proposes optimizations, and updates media buying platforms with the recommendations given human approval.

Unleashing AI Agents: Transforming Global Marketing with Claude Code and OpenClaw

Learn how AI agents are revolutionizing business processes, reducing manpower by 5x in a consumer goods company's global marketing campaign optimization.

The long-awaited age of AI agents has suddenly arrived, at least for those technically proficient enough and perhaps foolhardy enough to go all-in on a messy, imperfect, and risky adventure. With the commercial product Claude Code and the open source OpenClaw, coders can now build software as if they'd unleashed a hundred clones or unlocked superpowers.

What Are AI Agents?

AI agents are artificial intelligence that use tools to accomplish goals. They have the ability to remember across tasks and changing states; they can use one or more AI models to complete tasks; and they can decide when to access internal or external systems on a user's behalf. This enables AI agents to make decisions and take actions autonomously with minimal human oversight.

For example, a consumer goods company wanted to optimize its global marketing campaigns using an AI agent to transform processes. A project that once required six analysts per week now required a single employee working with an agent, delivering results in under an hour. The AI agent gathers data on a weekly basis, autonomously joining marketing data via connected data pipelines. It then analyzes performance metrics and compares against expectations, receiving business context from an operator when necessary.

The AI agent offers recommendations by writing a standardized report that proposes optimizations. An operator stress tests and refines the AI agent's recommendations as needed. Finally, when given human approval, the agent updates media buying platforms with the recommendations.

How Do AI Agents Work?

AI agents observe their environment, leverage large language models for planning, and access connected systems to take action and accomplish goals. They constantly collect and process information from their environment, including user interactions, key performance metrics, or sensor data. They can retain memory across conversations, which provides ongoing context across multi-step plans and operations.

Using language models, AI agents autonomously evaluate and prioritize actions based on their understanding of the problem to be addressed, goals to be accomplished, context, and memory. They leverage interfaces with enterprise systems, tools, and data sources to perform tasks. Tasks are governed by the plan delivered by a large language model or small language model.

To execute tasks, the AI agent may access enterprise services such as HR systems, order management systems, or CRMs. This enables AI agents to make decisions and take actions autonomously with minimal human oversight.

Background and Context

The concept of AI agents has been around for a while, but it's only recently that they've gained significant attention. In 2025, Nvidia CEO Jensen Huang said that enterprise AI agents would create a "multi-trillion-dollar opportunity" for many industries, from medicine to software engineering.

A spring 2025 survey conducted by MIT Sloan Management Review and Boston Consulting Group found that 35% of respondents had adopted AI agents by 2023, with another 44% expressing plans to deploy the technology in short order. Leading software vendors, including Microsoft, Salesforce, Google, and IBM, are fueling large-scale implementation by embedding agentic AI capabilities directly in their software platforms.

However, even companies on the cutting edge of deployment don't fully grasp how to use AI agents to maximize productivity and performance. Sinan Aral, a professor of management, IT, and marketing at MIT Sloan, said that the collective understanding of the societal implications of agentic AI on a larger scale is "nascent, if not nonexistent."

Why It Matters to the Industry

The rise of AI agents has significant implications for various industries. They can automate complex tasks, improve decision-making, and enhance customer experiences. However, they also present challenges such as data quality, governance, trust, and security.

Rapid evolution could propel organizations to adopt agentic AI without fully understanding its capabilities or having created a formal strategy and risk management framework. It's essential for every organization to have a strategy to deploy and utilize agents in customer-facing and internal use cases.

What Comes Next

The future of AI agents is exciting, but it also raises concerns about their potential impact on jobs, society, and the economy. As AI agents become more sophisticated, they will likely automate more complex tasks, leading to significant changes in various industries.

Organizations must be prepared to adapt to these changes by developing strategies for deploying and utilizing AI agents effectively. This includes understanding the societal implications of agentic AI and creating formal strategies and risk management frameworks.

Key Facts

Ai agents are artificial intelligence that use tools to accomplish goals.
They have the ability to remember across tasks and changing states, use one or more AI models to complete tasks, and decide when to access internal or external systems on a user's behalf.
AI agents can automate complex tasks, improve decision-making, and enhance customer experiences.
Rapid evolution could propel organizations to adopt agentic AI without fully understanding its capabilities or having created a formal strategy and risk management framework.
It's essential for every organization to have a strategy to deploy and utilize agents in customer-facing and internal use cases.