> ## Documentation Index
> Fetch the complete documentation index at: https://agent-docs.nineteen58.co.za/llms.txt
> Use this file to discover all available pages before exploring further.

# Best practices

# Best Practices for Building Effective Agents

This guide outlines key principles and patterns for building successful AI agents, based on [Anthropic's research and experience](https://www.anthropic.com/research/building-effective-agents) working with teams across industries.

## Core Principles

1. **Start Simple**
   * Begin with the simplest possible solution
   * Only increase complexity when demonstrably needed
   * Optimize single LLM calls with retrieval and in-context examples first

2. **Choose the Right Architecture**
   * **Workflows**: Use for predefined, well-structured tasks
   * **Agents**: Use when flexibility and model-driven decision-making are needed
   * Consider the tradeoffs between latency, cost, and task performance

3. **Framework Considerations**
   * Start by using LLM APIs directly when possible
   * Only add frameworks when they provide clear benefits
   * Ensure you understand the underlying code of any framework used

## Common Patterns

### 1. Augmented LLM (Basic Building Block)

* Enhance LLMs with capabilities like:
  * Retrieval
  * Tools
  * Memory
* Focus on tailoring capabilities to specific use cases
* Ensure clear documentation for LLM interfaces

### 2. Workflow Patterns

#### Prompt Chaining

* Breaks tasks into sequential steps
* Best for tasks with clear, fixed subtasks
* Example: Generate content → Review → Translate

#### Routing

* Classifies inputs and directs to specialized handlers
* Useful for distinct categories requiring different approaches
* Example: Customer service query classification

#### Parallelization

* **Sectioning**: Breaking tasks into parallel subtasks
* **Voting**: Running multiple attempts for higher confidence
* Useful for speed optimization and quality assurance

#### Orchestrator-Workers

* Central LLM coordinates subtasks
* Dynamic task breakdown and delegation
* Best for complex, unpredictable workflows

#### Evaluator-Optimizer

* Iterative refinement through feedback loops
* Use when clear evaluation criteria exist
* Example: Complex content generation with quality checks

### 3. Autonomous Agents

* Use for open-ended problems
* Requires well-designed toolsets
* Include appropriate guardrails and stopping conditions

## Tool Engineering Best Practices

1. **Design Clear Interfaces**
   * Write detailed documentation
   * Include example usage
   * Specify edge cases
   * Define clear boundaries between tools

2. **Format Considerations**
   * Allow sufficient tokens for model reasoning
   * Use formats familiar from internet text
   * Avoid complex formatting requirements
   * Minimize overhead like line counting or string escaping

3. **Testing and Iteration**
   * Test extensively with example inputs
   * Iterate based on model usage patterns
   * Design to prevent common mistakes
   * Use absolute paths over relative when possible

## Further Reading

For more detailed information and examples, refer to the complete [Anthropic research article on building effective agents](https://www.anthropic.com/research/building-effective-agents).