LLM Agent Block
What it does: Create an intelligent AI assistant that can think, reason, and use tools to accomplish complex tasks within your workflow.
In simple terms: Like having a smart assistant that not only understands questions but can also take actions - searching the web, checking databases, calling APIs, and more - to get things done.
When to Use This
Use the LLM Agent when you need to:
- ✅ Build an AI assistant that can use multiple tools
- ✅ Create automated research workflows
- ✅ Handle complex customer inquiries
- ✅ Process and analyze information intelligently
- ✅ Make decisions based on gathered data
- ✅ Orchestrate multi-step AI tasks
Example: Create a sales assistant that can search your product database, check inventory, calculate shipping, and answer customer questions - all in one conversation.
Key Features
- Multi-Model Support: Connect to OpenAI, Anthropic, Groq, and other providers
- Tool Integration: Give your agent abilities like web search, database access, and API calls
- Customizable Prompts: Define exactly how your agent should behave
- Structured Outputs: Get consistent, formatted responses
- Memory Management: Control how much context the agent remembers
- Multi-Step Reasoning: Agents can plan and execute complex tasks
How It Works
- You define the agent's role - Tell it what kind of assistant it should be
- You give it tools - Connect search, databases, APIs, or custom functions
- User asks a question - The agent receives a prompt or query
- Agent thinks and acts - It decides which tools to use and executes them
- Agent responds - It provides a helpful answer based on what it learned
Setup Guide
Step 1: Choose Your AI Model
Select which AI provider and model to use:
| Provider | Best For |
|---|---|
| OpenAI (GPT-4) | Complex reasoning, general tasks |
| OpenAI (GPT-3.5) | Fast responses, cost-effective |
| Anthropic (Claude) | Long conversations, nuanced responses |
| Groq (Llama) | Speed-critical applications |
Step 2: Write Your System Prompt
The system prompt tells the agent who it is and how to behave:
Good System Prompt Example: "You are a helpful customer service representative for TechCo. You can check order status, answer product questions, and help with returns. Be friendly, professional, and always try to solve the customer's problem."
Tips for System Prompts:
- Clearly define the agent's role
- Specify what it can and cannot do
- Set the tone (formal, casual, friendly)
- Include any special instructions or limitations
Step 3: Add Tools (Optional)
Give your agent abilities by connecting tools:
| Tool Type | What It Does | Example Use |
|---|---|---|
| Search Tools | Find information online | Research questions |
| Database Tools | Query your data | Look up customer info |
| API Tools | Call external services | Check weather, stock prices |
| Custom Tools | Your own functions | Calculate shipping, validate data |
Step 4: Configure the User Prompt
This is the actual question or task sent to the agent. Use variables to make it dynamic:
"Help this customer with their question: {{customer.question}}"
"Research this topic and provide a summary: {{topic}}"
Common Use Cases
Customer Support Agent
Build an agent that can answer questions, check order status, process returns, and escalate issues when needed.
Research Assistant
Create an agent that searches multiple sources, summarizes findings, and presents organized research reports.
Data Analyst
Deploy an agent that queries your database, identifies trends, and generates insights from your data.
Content Creator
Use an agent to draft blog posts, social media content, or marketing copy based on your guidelines.
Technical Support Bot
Build a helpdesk agent that troubleshoots issues, searches documentation, and provides step-by-step solutions.
Output Options
Text Response
Get a simple text answer - great for chatbots and simple queries.
Structured Data
Define a specific format for responses, ensuring you always get organized data you can use in other blocks.
Example structured output topics:
- Customer sentiment analysis with score
- Product recommendations with reasons
- Issue classification with priority level
Tips for Success
- Be specific in your system prompt - The clearer your instructions, the better the results
- Start simple - Add tools one at a time and test thoroughly
- Use examples - Show the agent what good responses look like
- Set boundaries - Tell the agent what it should NOT do
- Test edge cases - Try unusual inputs to see how the agent handles them
What You Get Back
After the agent runs:
- Response: The agent's answer or action result
- Tool Calls: Which tools were used and their outputs
- Tokens Used: How many AI tokens were consumed
- Success Status: Whether the agent completed successfully
Troubleshooting
| Problem | Likely Cause | Solution |
|---|---|---|
| Agent gives wrong answers | Unclear system prompt | Make instructions more specific |
| Agent doesn't use tools | Tools not described well | Add better tool descriptions |
| Responses too long | No length guidance | Add response length limits to prompt |
| Agent hallucinates | No grounding in facts | Add tools for fact-checking or limit scope |
Best Practices
- Keep prompts focused - Don't try to make one agent do everything
- Add guardrails - Include instructions on what not to do
- Monitor costs - Complex agents with many tool calls can be expensive
- Log conversations - Track what your agent does for debugging
- Iterate and improve - Regularly refine prompts based on real usage