Common workflow patterns for AI agents—and when to use them

2026-03-05 · Source: Claude Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This analysis details three common workflow patterns for AI agents: sequential, parallel, and evaluator-optimizer, which provide structure to autonomous agent decisions. These patterns are crucial for coordinating steps, ensuring predictable outcomes, and orchestrating timing in complex problems. Sequential workflows execute tasks in a fixed order, suitable for multi-stage processes with dependencies, trading latency for accuracy. Parallel workflows run independent tasks simultaneously, ideal for speed improvements in evaluations or document analysis, but require an aggregation strategy. Evaluator-optimizer workflows involve iterative refinement, where one agent generates content and another evaluates it against criteria, improving output quality for tasks like code generation or professional communications. The choice of pattern depends on task structure, quality requirements, and resource constraints, with a recommendation to start simple and evolve as needed.

Key takeaway

For AI Engineers designing agent systems, understanding these workflow patterns is critical for optimizing performance and cost. You should first attempt tasks with a single agent; if quality or latency is an issue, implement sequential workflows for dependent tasks, parallel for independent speed, or evaluator-optimizer for iterative quality improvement. Always define clear aggregation strategies for parallel workflows and stopping criteria for evaluator-optimizer loops to manage token usage and latency effectively.

Key insights

Structuring AI agent autonomy with sequential, parallel, or evaluator-optimizer workflows optimizes performance and reliability.

Principles

• Workflows shape agent autonomy, not replace it.
• Match workflow complexity to actual requirements.
• Separate generation and evaluation for specialized agents.

Method

Start with a single agent; if insufficient, apply sequential for dependencies, parallel for independent speed, or evaluator-optimizer for iterative quality refinement.

In practice

• Use sequential for data pipelines or draft-review cycles.
• Employ parallel for multi-dimensional evaluations or code review.
• Apply evaluator-optimizer for high-quality code or communication generation.

Topics

• AI Agents
• Workflow Patterns
• Sequential Workflows
• Parallel Workflows
• Evaluator-Optimizer Workflows

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Claude Blog.