Agentic design patterns

Core agentic design patterns

Here are four foundational patterns that are shaping the next generation of AI applications:

1 – Reflection Pattern

This pattern equips agents with the ability to assess their own actions, decisions, or outputs and use that insight to improve over time.

With the reflection pattern, agents:

• Monitor their past behaviours and results
• Identify mistakes or inefficiencies
• Adjust their reasoning strategies or tool usage based on feedback

This pattern enables continuous learning without requiring explicit retraining or developer intervention. It supports the creation of adaptive systems that improve with every task or interaction.

2 – Tool use pattern

Rather than embedding all capabilities within the agent, this pattern enables agents to extend their abilities by calling on external tools and systems.

Agents using the tool use pattern can:

• Query databases, search APIs, or documentation engines
• Call functions (custom built), language translators, or document generators and much more.
• Fetch, manipulate, and return results in real-time

This pattern reduces the cognitive load on agents and supports the principle of modularity, where each tool or service does one thing well and agents know when and how to use them.

3 – Planning pattern

Agents often need to accomplish multi-step goals that can’t be achieved in a single turn. The planning pattern allows them to:

• Break down a high-level goal into actionable subtasks
• Sequence and prioritize steps dynamically
• Adapt plans in response to intermediate results or external changes

This pattern is especially useful in domains like customer service resolution, sales pipeline execution, legal case handling, and content creation — where long-term thinking and step-by-step execution are key.

The outcome is an AI that not only thinks but strategically acts, delivering value across longer workflows.

4 – Multi-agent collaboration pattern

Instead of building one monolithic AI agent, this pattern encourages the use of multiple specialized agents that collaborate to solve complex problems.

With multi-agent collaboration:

• Each agent is assigned a clear role or expertise (e.g., researcher, validator, communicator)
• Agents exchange information, negotiate, or escalate decisions as needed
• The system can operate as a virtual team with clearly defined coordination mechanisms

This pattern enables scalability through specialization — similar to how human teams are structured — and is especially powerful in high-complexity domains like supply chains, financial modeling, and software engineering.These core patterns — reflection, tool use, planning, and multi-agent collaboration — are shaping the future of intelligent, adaptive AI systems. They provide the foundation for agents that can reason, learn, and act with purpose in complex environments.

Next, in Part 3, we’ll explore how to bring these patterns together to build modular, intelligent agent systems — the final step in creating AI that’s ready for real impact.