Building effective AI agent systems is not just about having a capable language model; it is about designing the right workflow architecture. Agentic workflow design patterns provide reusable solutions to common challenges in AI automation, much like software design patterns provide reusable solutions to common programming challenges. Understanding these patterns enables you to compose them into sophisticated systems that handle complex, real-world tasks reliably.

These patterns emerge from practical experience building production agent systems. Each addresses a specific architectural need, and most production systems combine multiple patterns to create their complete workflow.

Pattern 1: Prompt Chaining

The simplest agentic pattern breaks a task into a fixed sequence of LLM calls, where each call processes the output of the previous one. For example, generating a blog post might chain: outline generation, section drafting, editing, and formatting. Each step has a focused prompt optimized for its specific subtask.

When to use: Tasks with well-defined, predictable sequences of steps where the workflow does not need to adapt based on intermediate results. This is the most controlled and predictable pattern.

Adding Gates Between Steps

Quality gates between chain steps validate intermediate outputs before proceeding. If the outline does not meet quality criteria, the chain can retry that step rather than propagating a poor outline through the remaining steps. This prevents error accumulation.

Prompt chaining provides maximum control and predictability. When your task follows a consistent sequence, this pattern delivers reliable results with minimal complexity.

Pattern 2: Routing

The routing pattern classifies incoming requests and directs them to specialized processing paths. A customer support system might route billing questions to a billing-specialized workflow, technical issues to a troubleshooting workflow, and general inquiries to an FAQ workflow. Each path can use different tools, prompts, and processing logic.

When to use: When you handle diverse request types that benefit from specialized processing rather than a one-size-fits-all approach.

Key Takeaway

Routing is one of the most impactful patterns because it lets you optimize each processing path independently. A specialized workflow for each request type almost always outperforms a generic workflow trying to handle everything.

Pattern 3: Parallelization

Parallelization executes multiple LLM calls or tool operations simultaneously, then combines the results. Two common variants exist:

Sectioning divides a task into independent parts that are processed simultaneously. Analyzing a document might process each section in parallel, then combine the section-level analyses into an overall summary.

Voting runs the same task multiple times independently and aggregates the results. For classification tasks, three independent LLM calls voting on the category provides higher accuracy than a single call. For code generation, multiple independent implementations can be compared to identify the most robust approach.

Pattern 4: Orchestrator-Worker

The orchestrator-worker pattern has a central orchestrator that dynamically decomposes tasks, delegates subtasks to worker agents, and synthesizes results. Unlike prompt chaining, the orchestrator can adapt the workflow based on intermediate results, deciding at runtime how many workers to deploy and what each should do.

A coding orchestrator might analyze a feature request, determine that it requires changes to three files, spawn a worker for each file, collect the implementations, and then verify they work together. If a worker fails, the orchestrator can retry with different instructions or reassign the task.

When to use: Complex tasks where the number and nature of subtasks cannot be predetermined. This pattern provides flexibility at the cost of more LLM calls for orchestration.

Pattern 5: Evaluator-Optimizer

The evaluator-optimizer pattern creates an iterative improvement loop. A generator produces output, an evaluator assesses its quality, and if the output does not meet the quality threshold, the generator receives feedback and tries again. This continues until the evaluator is satisfied or a maximum iteration count is reached.

This pattern is particularly effective for tasks with clear quality criteria that can be automatically assessed: code that must pass tests, text that must meet readability scores, or analyses that must cover specific required topics.

The evaluator-optimizer pattern implements a form of self-improvement. Each iteration builds on the previous attempt's feedback, producing progressively better results until quality criteria are met.

Pattern 6: Tool-Augmented Agent

The tool-augmented agent pattern gives the LLM access to external tools and lets it autonomously decide when and how to use them. This is the most common pure agent pattern, exemplified by the ReAct architecture. The agent perceives its environment, reasons about what to do, takes action through a tool, and observes the result in a loop.

When to use: When the task requires interaction with external systems and the specific tools needed depend on the situation. This is the most flexible pattern but also the least predictable.

Combining Patterns

Production systems rarely use a single pattern. Complex workflows combine multiple patterns:

  • A routing layer classifies incoming requests
  • Each route leads to a prompt chain or orchestrator-worker workflow
  • Individual steps within the workflow use tool-augmented agents for flexible execution
  • An evaluator-optimizer loop ensures output quality before delivery
  • Parallelization accelerates independent steps within the workflow

Key Takeaway

Start with the simplest pattern that meets your requirements and add complexity only when needed. A prompt chain should be your first choice. Add routing when you handle diverse request types. Add an orchestrator when tasks require dynamic decomposition. Layer in evaluation when quality assurance is critical.

Implementing Reliable Workflows

Regardless of which patterns you choose, reliable agentic workflows require several operational capabilities:

  1. State persistence: Save workflow state at each step so that failures do not require restarting from the beginning
  2. Retry logic: Implement intelligent retries with backoff for transient failures in LLM calls and tool invocations
  3. Timeout management: Set appropriate timeouts at each step and for the overall workflow to prevent runaway execution
  4. Observability: Log every decision, tool call, and state transition for debugging and optimization
  5. Graceful degradation: When a step fails permanently, fall back to simpler approaches or escalate to humans rather than failing silently

These workflow patterns are the building blocks of production AI systems. Mastering them and understanding when to apply each will be the defining skill of AI engineers building the next generation of intelligent applications.