From experimentation to value creation using AI agents

There are blurred lines between how people discuss basic generative AI solutions, LLM workflow patterns, and agentic systems. There is no right or wrong to these variations, but to add clarity to this discussion, we distinguish agents from other AI solutions based on the existence of five core components.

Making agents work for your organization

While conventional AI excels at specific tasks like image recognition or text generation AI agents operate with a degree of autonomy that fundamentally changes how work gets done. These agents can maintain ongoing operations, adapt to changing conditions, and handle complex sequences of decisions that previously required human oversight at every step. When well designed, agents can handle tasks a simple workflow cant.

• Autonomy.
• Use of tools.
• Complex reasoning.
• Adaptability.
• Modularity.

When you start seeing positive outcomes, scale thoughtfully. For example, a coding agent could evolve into a full orchestrator worker model for broader system updates, or a support assistant could expand to handle billing and dynamic routing.

Make sure to manage expectations and allocate resources appropriately. Communicate with teams leading agent initiatives to set expectations regarding both timelines and results. Ensure sufficient scope for system and team integration while helping the rest of the organization understand that piloting new technologies may take longer than established methods. This approach positions your organization to unlock AIs full potential while maintaining focus on measurable success.