Building Effective AI Agents: Patterns and Best Practices
The landscape of AI agents has evolved rapidly over the past year. After working with numerous teams across industries, a clear pattern has emerged: the most successful AI agent implementations aren’t built with complex frameworks or specialized libraries. Instead, they rely on simple, composable patterns that can be combined and customized for specific needs.
Understanding Agentic Systems
Before diving into implementation patterns, it’s important to distinguish between two fundamental types of agentic systems:
Workflows are systems where large language models and tools follow predefined code paths. Think of these as structured, deterministic processes with clear steps.
Agents are systems where LLMs dynamically control their own processes and tool usage, making decisions about how to accomplish tasks on the fly.
Both approaches have their place, and choosing between them depends on your specific requirements.
When to Use Agents (and When Not To)
The golden rule: start simple and add complexity only when necessary. Many applications can be solved with optimized single LLM calls, retrieval systems, and well-crafted examples.
Agentic systems trade latency and cost for improved task performance. Consider this tradeoff carefully:
- Use workflows when you need predictability and consistency for well-defined tasks
- Use agents when flexibility and model-driven decision-making are essential
- Use neither when simpler solutions suffice
The Foundation: Augmented LLMs
Every agentic system starts with an augmented LLM—a language model enhanced with:
- Retrieval capabilities for accessing relevant information
- Tools for taking actions and interfacing with external systems
- Memory for maintaining context and learning from interactions
Modern LLMs can actively leverage these capabilities, generating their own search queries, selecting appropriate tools, and determining what information to retain. The key is tailoring these augmentations to your specific use case with clear, well-documented interfaces.
Five Essential Workflow Patterns
1. Prompt Chaining
Decompose tasks into sequential steps where each LLM call processes the previous output. Add programmatic checks between steps to ensure the process stays on track.
When to use: Tasks that can be cleanly broken into fixed subtasks. Trading latency for accuracy by making each step simpler.
Example use cases:
- Generate marketing copy, then translate it into another language
- Create a document outline, validate it meets criteria, then write the full document
2. Routing
Classify inputs and direct them to specialized follow-up tasks, allowing for separation of concerns and more specialized prompts.
When to use: Complex tasks with distinct categories that benefit from separate handling, where classification can be performed accurately.
Example use cases:
- Route customer service queries to appropriate processes based on type
- Direct simple questions to efficient models and complex queries to more capable ones
3. Parallelization
Run multiple LLM processes simultaneously and aggregate their outputs. This manifests in two ways:
- Sectioning: Breaking tasks into independent subtasks
- Voting: Running the same task multiple times for diverse perspectives
When to use: When subtasks can be parallelized for speed or when multiple perspectives increase confidence in results.
Example use cases:
- Content moderation with separate checks for different violation types
- Code vulnerability reviews from multiple analytical perspectives
4. Orchestrator-Workers
A central LLM dynamically breaks down tasks, delegates to worker LLMs, and synthesizes results.
When to use: Complex tasks where required subtasks can’t be predicted in advance and must be determined based on specific inputs.
Example use cases:
- Coding products making complex changes across multiple files
- Research tasks gathering information from numerous sources
5. Evaluator-Optimizer
One LLM generates responses while another provides evaluation and feedback in an iterative loop.
When to use: When you have clear evaluation criteria and iterative refinement adds measurable value—similar to a human editing process.
Example use cases:
- Literary translation requiring nuanced refinement
- Complex searches needing multiple rounds with quality assessment
Full Agents: When Autonomy Matters
True agents operate independently with these characteristics:
- Begin with user commands or interactive discussion
- Plan and execute autonomously, returning for feedback when needed
- Use tools based on environmental feedback in a loop
- Incorporate stopping conditions to maintain control
When to use agents: For open-ended problems where you can’t predict required steps or hardcode fixed paths, and you have appropriate trust in the LLM’s decision-making.
Important considerations:
- Higher costs and potential for compounding errors
- Require extensive testing in sandboxed environments
- Need appropriate guardrails and human oversight
Real-world examples:
- Autonomous coding agents resolving GitHub issues
- Computer use implementations where Claude controls system interfaces
Three Core Principles
- Maintain simplicity in your agent’s design—resist unnecessary complexity
- Prioritize transparency by showing the agent’s planning and reasoning steps
- Craft excellent tool interfaces through thorough documentation and testing
The Agent-Computer Interface
Just as human-computer interfaces require careful design, so do agent-computer interfaces. When designing tools:
- Think from the model’s perspective: Is it obvious how to use this tool?
- Write clear documentation: Treat it like writing for a junior developer—include examples, edge cases, and format requirements
- Test extensively: Run many scenarios to identify where the model struggles
- Poka-yoke your tools: Design them to make mistakes difficult or impossible
Tool format matters more than you might think. Some formats are significantly easier for LLMs to work with:
- Allow enough tokens for the model to “think” before committing
- Keep formats close to naturally occurring text patterns
- Minimize formatting overhead like accurate line counts or excessive escaping
Practical Applications
Two domains have shown particular promise for AI agents:
Customer Support
Natural conversation flows combined with tool integration for accessing data and taking actions. Success is measurable through resolution rates, making this a proven use case with several companies offering usage-based pricing.
Coding Agents
Code solutions are objectively verifiable through automated tests, allowing agents to iterate based on clear feedback. The structured problem space makes this ideal for agentic approaches, though human review remains essential for system-level considerations.
The Framework Question
Many frameworks exist to simplify agent implementation, but they come with tradeoffs:
Benefits:
- Easy to get started
- Handle low-level tasks automatically
- Simplify common patterns
Drawbacks:
- Add abstraction layers that complicate debugging
- Can obscure underlying prompts and responses
- May tempt unnecessary complexity
Recommendation: Start with LLM APIs directly. Many patterns require only a few lines of code. If you use a framework, ensure you understand what’s happening under the hood.
Measuring Success
The path to effective agents requires:
- Starting with simple prompts
- Comprehensive evaluation
- Adding complexity only when simpler solutions fall short
- Continuous iteration based on measured performance
Success isn’t about building the most sophisticated system—it’s about building the right system for your needs.
Conclusion
AI agents represent a powerful tool for scaling complex tasks, but they’re not a universal solution. By understanding these fundamental patterns and principles, you can make informed decisions about when and how to implement agentic systems. Focus on simplicity, transparency, and thorough testing, and you’ll build agents that are not only powerful but also reliable, maintainable, and trusted by users.
This post is based on insights from Anthropic’s engineering team, drawing on extensive experience building and deploying AI agents in production environments. For code examples and implementations, check out Anthropic’s cookbook.