What Is Agentic AI?

What does agentic AI offer?

• Goal-oriented approach: Comprehends a high-level goal and the AI agent’s defined role.
• Multistep problem solving: Devises a plan to reach the goal.
• Self-directed execution: Takes tactical action, working with other tools, applications, and workflows.
• Adaptability: Flexibly handles trial-and-error and other changes.

Is agentic AI a fad?

Agentic AI is not new—academic researchers have been working on the topic for years. But interest has surged dramatically over the past year. This interest extends beyond hype, with strong engagement from developers and researchers. However, at this point most AI “agents” are rebranded features, early prototypes with limitations, or rules-based, simplistic agents. In 2025 we expect to see functional, scalable AI agents emerge across domains. Existing AI applications will become more powerful, and new ones will emerge.

Where will we see true AI agents first?

What barriers are hindering the adoption of agentic AI?

• Generalization beyond narrow scopes: AI agents perform well in tightly defined workflows, less so in complex contexts.
• Explanation of decisions: Agents are often “black boxes,” making it hard to validate decisions or fix errors.
• Workflow training: If workflows are undocumented, agents are limited to information at hand to operate independently.
• Cooperation among agents: Agents struggle to operate together without mature orchestration, architectures, and coordination.
• Data and tooling: Agents cannot act without access to clean data and integrated tooling.

How will agentic AI impact the workforce?

• Productivity increases. People become AI supervisors rather than task executors.
• Roles shift. Agents democratize technical skills, increasing the breadth of employee roles.
• Decision making is redefined. Employee autonomy increases and decision cycles speed up.
• Collaboration becomes critical. Agents expedite work, but it remains important to coordinate interactions among people.
• Risk management expands. AI agents increase risk of unintended consequences, requiring new guardrails.

What should businesses do to prepare for agentic AI?

• Embed agents where ROI is greatest. Identify where vendor ecosystems, agentic AI technology, and your business intersect.
• Pilot fast, iterate faster. The agentic phase is moving faster than previous AI waves; test and iterate faster.
• Prepare data and tools. Set up agents for success so they can be rapidly adopted by your employees.
• Be wary of standalone platforms. Pressure test agents’ connectors to other systems.
• Become a learning organization. Encourage experimentation and foster a culture of continuous improvement.