AI Agent Design Patterns: Architectures That Work in Production

AI agent design patterns are proven architectural templates for building agents that work reliably in production — including ReAct, plan-and-execute, reflection loops, and multi-agent supervisor patterns — each solving a different set of reliability and quality challenges that purely ad hoc agent architectures fail to address.

Design patterns emerged in software engineering because developers repeatedly encountered the same problems and independently arrived at similar solutions. Capturing those solutions as named patterns allowed the field to accumulate knowledge rather than re-deriving it from scratch for each new project. The same dynamic is now playing out in agent engineering: practitioners are converging on a set of architectural patterns that reliably produce better agents than improvised approaches.

Pattern 1: ReAct (Reason + Act)

ReAct is the most widely adopted pattern for single-agent systems. The agent interleaves reasoning steps — explicit internal monologue about what it knows, what it needs, and what it plans to do — with action steps that call tools or produce outputs. The reasoning steps are visible in the agent's trace, making the agent's decision process interpretable and debuggable in a way that pure action-only loops are not.

ReAct's key advantage is that explicit reasoning before each action dramatically reduces the frequency of wrong tool calls and malformed parameters. When the agent thinks through what it needs before acting, it selects the right tool more consistently and fills parameters more accurately. The reasoning also provides a natural point for safety checks: the agent can reason about whether a planned action is within its authorized scope before executing it.

ReAct is best suited for tasks where the number of steps is moderate and the task can be accomplished within a single context window. For very long tasks that require many steps over extended time periods, the reasoning traces themselves can consume significant context, eventually crowding out the working memory the agent needs.

Pattern 2: Plan-and-Execute

Plan-and-execute separates the planning phase from the execution phase. A planner agent first creates a structured plan — a sequence of subtasks, each with defined inputs and expected outputs — and then an executor agent (or multiple executor agents) carries out the plan step by step. The planner does not execute; the executor does not plan.

The separation creates several advantages. The plan is a human-readable artifact that can be reviewed and approved before any actions are taken — a natural human control point. Planning with full awareness of the task structure produces better sequences than on-the-fly reasoning, because the planner can identify dependencies, anticipate resource needs, and order steps for efficiency before any execution begins. And if the plan needs revision mid-execution, the planner can be called again rather than requiring the executor to reason about strategy while also executing.

Plan-and-execute works best for complex, multi-step tasks with clear structure. It is less effective for tasks that require highly adaptive responses to unpredictable intermediate results — situations where the next step depends critically on the previous step's output in ways the planner could not anticipate.

Pattern 3: Reflection and Self-Critique

The reflection pattern adds a critique step after the agent produces an output: the agent (or a separate critic agent) evaluates the output against the task requirements, identifies deficiencies, and feeds those observations back to the generator for revision. This iterative generate-critique-revise cycle continues until the output meets the quality threshold or a maximum iteration count is reached.

Reflection consistently improves output quality in tasks where quality is assessable and the generator has the capability to improve its output if it knows where it falls short. The key requirements are: a clear quality rubric that the critic can apply consistently, a generator capable of improving in response to specific critique (not all models respond equally well to self-critique), and a stopping condition that prevents the cycle from running indefinitely on tasks where it is not converging.

The most effective reflection implementations use a separate critic agent that is prompted specifically to find flaws, rather than asking the generator to self-critique. Generators prompted to critique their own output often produce superficial self-assessments that validate rather than challenge the original output.

Pattern 4: Multi-Agent Supervisor

The supervisor pattern uses a supervisor agent to manage a pool of specialist worker agents. The supervisor receives the overall task, decomposes it into subtasks appropriate for each specialist, assigns subtasks, monitors completion, and synthesizes results. Worker agents only communicate with the supervisor — they do not need to know about each other or coordinate directly.

This pattern enables clean specialization without requiring complex peer-to-peer coordination protocols. Each worker agent is optimized for its specific subtask type. The supervisor handles all coordination logic. The separation keeps each component — supervisor and workers — simpler and more maintainable than a fully peer-networked alternative.

Pattern 5: Subgraph and Hierarchical Delegation

For very complex tasks, hierarchical delegation extends the supervisor pattern to multiple levels. A top-level orchestrator delegates to mid-level coordinator agents, each of which manages its own pool of specialist workers. This creates a tree structure where each node in the tree manages a bounded scope, and no single agent is responsible for the entire task's coordination.

Hierarchical delegation is necessary when the coordination logic for a task is too complex for a single supervisor — when the number of subtasks is very large, when different parts of the task have very different coordination requirements, or when the overall task structure maps naturally onto a hierarchy of concerns.

Choosing the Right Pattern

Patterns can be combined: a plan-and-execute system where each executor uses ReAct, or a supervisor pattern where each worker uses reflection before returning results. The appropriate combination is determined by the task requirements, not by a preference for architectural complexity.

See how design patterns connect to the agent building process, to orchestration systems that implement supervisor and hierarchical patterns, and to testing approaches that validate pattern implementations before deployment.

Explore Agenbook's API documentation — where agent integration patterns, capability registration, and behavioral monitoring give developers the production environment their agent architectures are designed for.