Large language models are becoming increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents.
This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable best practices. It includes frameworks for identifying promising use cases, clear patterns for designing agent logic and orchestration, and best practices to ensure your agents run safely, predictably, and effectively.
What is an Agent?
An agent is an autonomous system that independently executes workflows on behalf of users. Unlike traditional software, agents manage complex tasks end-to-end, leveraging LLMs for decision-making and dynamically using external tools.
Key characteristics of agents:
- Manage multi-step workflows with minimal human intervention
- Dynamically select and use external tools
- Recognize task completion and handle errors gracefully
When Should You Build an Agent?
Agents are ideal for workflows where traditional automation struggles, particularly in areas requiring:
- Complex decision-making (e.g., nuanced customer service decisions)
- Difficult-to-maintain rules (e.g., evolving compliance workflows)
- Heavy use of unstructured data (e.g., document analysis, natural language understanding)
If a use case involves ambiguity, dynamic reasoning, or complex tool interactions, agents can add significant value.
Foundations of Agent Design
Building an agent involves three core components:
- Model: The LLM powering reasoning and workflow decisions.
- Tools: APIs or functions that allow the agent to gather information or take action.
- Instructions: Structured prompts and guidelines that direct agent behavior.
Orchestration Patterns
Two main patterns for managing agent complexity:
Single-Agent Systems: A single agent with access to multiple tools handles the entire workflow. Best for simple to moderately complex tasks.
Multi-Agent Systems: Useful when workflows become too complex for one agent.
Building Guardrails
Guardrails are essential for ensuring safe, predictable agent behavior. They include:
- Relevance classifiers: Prevent off-topic actions.
- Safety classifiers: Detect unsafe prompts and inputs.
- PII filters and moderation layers: Protect sensitive information and ensure appropriate content.
- Tool safeguards: Rate tool risks and add checkpoints for sensitive actions.
Guardrails should be layered and evolve as real-world vulnerabilities are discovered.
Human-in-the-Loop
Even with strong guardrails, human intervention remains critical.
Use human escalation when:
- Agents exceed failure thresholds (e.g., misunderstanding repeated inputs)
- High-risk actions are involved (e.g., financial transactions, sensitive decisions)
Conclusion
Agents offer a transformative leap in automating complex workflows. To succeed:
- Start simple with single-agent systems.
- Add complexity (multi-agents) only when necessary.
- Implement strong guardrails and human oversight.
- Iterate and improve based on real-world feedback.
With thoughtful design and cautious deployment, agents can unlock powerful new capabilities for businesses and users alike.
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