Agentic AI Design Patterns
Practical architectural patterns for designing reliable AI agents, tool-enabled workflows and multi-agent systems.
AI agents are quickly becoming one of the most important architectural patterns in modern AI systems.
Instead of limiting a language model to generating a single response, agentic systems allow AI applications to reason about tasks, use tools, interact with external systems and execute multi-step workflows.
However, giving an AI system more autonomy also introduces additional architectural challenges.
Start with the Simplest Architecture
Not every AI application needs an agent.
Before introducing agentic behavior, ask whether the problem can be solved with a simpler architecture.
A traditional generative AI application may be sufficient when the primary requirement is:
- Question answering
- Content generation
- Information extraction
- Summarization
- Classification
Agents become more useful when the system needs to decide what actions to take or coordinate multiple steps.
The Tool-Using Agent Pattern
One of the simplest agent architectures combines a foundation model with a collection of tools.
The model can determine when a tool is required and select the appropriate capability.
Tools might include:
- Enterprise search
- APIs
- Databases
- Business applications
- Document processing
- Code execution
- Workflow systems
The important architectural principle is to keep tool permissions explicit.
An agent should only have access to the capabilities required for its purpose.
The Planner and Executor Pattern
More complex tasks can be separated into planning and execution.
The planner determines what needs to happen.
The executor performs the individual actions.
A typical workflow might look like: