nwave-ai/nw-design-patterns

Agentic Design Patterns

Pattern Decision Tree

Is the agent doing a single focused task?
  YES -> Does it need self-evaluation?
    YES -> Reflection
    NO  -> ReAct (default for most agents)
  NO -> Is it coordinating multiple agents?
    YES -> Are tasks independent?
      YES -> Parallel Orchestration
      NO  -> Are tasks sequential with dependencies?
        YES -> Sequential Orchestration
        NO  -> Hierarchical (supervisor + workers)
    NO -> Is it routing to one of several specialists?
      YES -> Router
      NO  -> Does it need structured task decomposition?
        YES -> Planning
        NO  -> ReAct (default)

1. ReAct (Reason + Act)

General-purpose agents needing tool calling and iterative problem-solving.

Loop: Reason -> Select/execute action -> Observe result -> Repeat until done. When: Default pattern. Most specialist agents. Examples: software-crafter, researcher, troubleshooter.

2. Reflection

Agent must evaluate and iteratively improve its own output.

Loop: Generate -> Review against criteria -> Identify gaps -> Refine -> Validate threshold met. When: Quality-critical outputs where first-draft insufficient (code review, architecture review, agent validation). Examples: agent-builder-reviewer, solution-architect-reviewer, software-crafter-reviewer.

3. Router

Request classified and delegated to exactly one specialist.

Loop: Analyze request -> Classify -> Select specialist -> Delegate. When: Task dispatching, single path execution. Low overhead, fast routing. Examples: workflow-dispatcher, task-router.

4. Planning

Complex tasks requiring structured decomposition before execution.

Loop: Decompose into sub-tasks -> Sequence -> Allocate resources -> Execute with checkpoints. When: Multi-step implementations, migrations, large refactoring. Examples: project-planner, migration-coordinator.

5. Sequential Orchestration

Linear workflows with clear dependencies between stages.

Structure: Agent1 -> Output1 -> Agent2 -> Output2 -> Agent3 -> Result When: Pipeline workflows where each stage transforms previous output. Example: nWave waves: DISCUSS -> DESIGN -> DEVOPS -> DISTILL -> DELIVER.

6. Parallel Orchestration

Multiple independent analyses needed simultaneously.

Structure: Supervisor -> [Worker1, Worker2, Worker3] (concurrent) -> Aggregate results. When: Independent analyses, multi-aspect reviews, parallel risk assessment. Example: Multi-reviewer code review, parallel security + performance + correctness analysis.

7. Hierarchical

Supervisor coordinates multiple worker agents dynamically.

Structure: Supervisor manages workers, routing tasks and aggregating results. When: Complex coordination where routing depends on intermediate results. Example: feature-coordinator supervising frontend/backend/database/testing specialists.

Pattern Combinations

• ReAct + Reflection: Reason/act then self-review (most reviewer agents)
• Planning + Sequential: Decompose then execute pipeline (devop)
• Router + Hierarchical: Route to supervisor who coordinates workers

Choosing for nWave Agents

| Agent Role | Pattern | Rationale | |-----------|---------|-----------| | Specialist (single domain) | ReAct | Tool-using, iterative task completion | | Reviewer (-reviewer suffix) | Reflection | Must self-evaluate and iterate on critique | | Wave orchestrator | Sequential | Clear dependency chain between phases | | Multi-agent coordinator | Hierarchical | Dynamic task routing to specialists | | Task dispatcher | Router | Classification and single-path delegation |