AI Agent Design Patterns

Best Practice: AI Agent Design Patterns

AI agent design patterns are reusable structures for building LLM systems that take multiple steps, use tools, and pursue a goal with some autonomy. Common patterns include tool use (calling functions and APIs), reflection (the model critiques and revises its own output), planning (breaking a goal into steps), and multi-agent collaboration (specialized agents coordinated by an orchestrator). A key principle is to prefer the simplest pattern that works: many tasks need a fixed workflow, not a fully autonomous agent. These patterns matter because they make agent behavior more predictable, debuggable, and cost-effective.

Anthropic's widely cited guidance draws a useful line between workflows, where LLM calls are orchestrated through predefined code paths, and agents, where the model dynamically directs its own steps and tool use. Workflows are more predictable and cheaper; agents are more flexible but harder to control. The recommended approach is to start with the simplest building block, a single augmented LLM call with retrieval and tools, and add complexity only when it measurably improves outcomes. Patterns such as prompt chaining, routing, reflection, and orchestrator-worker each solve a specific class of problem.

Step-by-Step Implementation Guidance

1. Decide whether the task needs an agent or a deterministic workflow; favor the simpler option.
2. Start with augmented LLM calls (retrieval plus tools) before adding autonomy.
3. Use prompt chaining for tasks with clear sequential steps.
4. Add routing to send inputs to specialized handlers.
5. Introduce reflection so the model checks its work against criteria.
6. Give the agent well-scoped tools with clear schemas and limits.
7. Add stopping conditions, step limits, and human approval for risky actions.
8. Trace every step and evaluate end-to-end task success.

Reliability comes from constraints as much as from capability. Well-built agents have explicit stopping conditions and step limits so a confused agent cannot loop indefinitely or run up cost. High-impact or irreversible actions are placed behind human approval. Every step is traced so failures can be debugged, and the whole system is evaluated on end-to-end task completion rather than on individual model calls. The recurring lesson from production deployments is that the simplest pattern that meets the requirement is almost always the right choice.

Common Mistakes Teams Make When Ignoring This Practice

• Reaching for a fully autonomous agent when a workflow would do.
• Giving agents broad, unconstrained tools and no step limits.
• No reflection, so errors compound across steps.
• Missing human-in-the-loop for high-impact actions.
• No tracing, making failures impossible to debug.

Tools and Techniques That Support This Practice

• Frameworks: LangGraph, AutoGen, CrewAI, OpenAI Agents SDK.
• Patterns: ReAct, reflection, planner-executor, orchestrator-worker.
• Infrastructure: tool sandboxes, step tracing, evals on task completion.

How This Practice Applies to Different Migration Types

• Cloud Migration: An orchestrator agent plans steps while workers execute scoped, approved infra changes.
• Database Migration: A planning agent sequences schema changes with validation between steps.
• SaaS Migration: A routing agent dispatches per-vendor integration sub-tasks.
• Codebase Migration: A reflection loop translates code, runs tests, and self-corrects.

Checklist

• Agent-vs-workflow decision made deliberately
• Simplest viable pattern chosen
• Tools well-scoped with schemas and limits
• Reflection added where errors compound
• Stopping conditions and step limits set
• Human approval for high-impact actions
• Steps traced and task success evaluated