Introduction
As industries across the globe increasingly integrate AI, large language model (LLM) agents have emerged as transformative tools for diverse applications. Drawing on experiences from working with numerous teams and building agents ourselves, this guide delves into the best practices for developing effective agentic systems. The focus is on leveraging simple, composable patterns rather than relying on complex frameworks.
Defining Agents and Workflows
At their core, “agents” and “workflows” differ fundamentally:
- Workflows: Systems where LLMs and tools are directed through predefined code paths, ensuring predictability and consistency.
- Agents: Autonomous systems where LLMs independently determine their processes and tool usage, offering flexibility for dynamic tasks.
When to Use Agents
While agents offer unparalleled adaptability and scalability, their complexity brings trade-offs in latency and cost. Developers should consider simpler solutions, such as single LLM calls with retrieval or in-context examples, before opting for agentic systems.
Frameworks for Implementation
Numerous frameworks simplify agentic system development:
- LangGraph by LangChain
- Amazon Bedrock’s AI Agent Framework
- Rivet (drag-and-drop GUI)
- Vellum (workflow builder)
However, starting with direct LLM API usage is recommended for simplicity and deeper understanding.
Building Blocks of Agentic Systems
The foundation of any agentic system is an augmented LLM enhanced with retrieval, tools, and memory. Developers should tailor these augmentations to specific use cases and ensure well-documented interfaces.
Common Workflow Patterns
- Prompt Chaining
Tasks are broken into sequential steps, improving accuracy by simplifying each step. Ideal for tasks like content generation followed by translation. - Routing
Inputs are classified and directed to specialized workflows, optimizing performance by focusing on distinct task categories. Examples include categorizing customer service queries or optimizing resource allocation for AI models. - Parallelization
Tasks are divided into independent subtasks or multiple attempts for diverse outputs. Applications include content moderation and automated code evaluations. - Orchestrator-Workers
A central LLM delegates subtasks to others dynamically. Suitable for unpredictable, complex tasks such as coding and research. - Evaluator-Optimizer
One LLM generates output while another evaluates and iterates for improvement. Effective for nuanced tasks like literary translation or iterative search refinement.
Agents in Practice
Agents are gaining traction as LLMs improve in understanding, reasoning, and tool integration. These systems operate autonomously while periodically seeking human input for clarity or oversight. Successful examples include:
- Customer Support: Enhanced chatbot interfaces that resolve queries using external data and tools.
- Coding: Automated problem-solving agents capable of handling complex GitHub issues and iterating based on test feedback.
Key Considerations for Agent Development
- Tool Design: Clear, intuitive tool definitions and thoughtful prompt engineering are crucial for seamless agent-tool interaction.
- Testing: Extensive sandbox testing and guardrails mitigate risks associated with compounding errors.
- Customization: Combine and adapt workflow patterns to meet unique application needs.
Conclusion
Building effective agents is less about adopting complex frameworks and more about leveraging foundational patterns and iterative testing. By prioritizing simplicity and aligning tools with specific use cases, developers can harness the full potential of LLM agents while ensuring reliability and efficiency.