AI Agents are the core intelligent entities in Definable.ai that can understand natural language, reason about problems, and take actions to help users accomplish their goals. They combine the power of Large Language Models (LLMs) with external tools and knowledge bases to create versatile, capable assistants.

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What is an AI Agent?

An AI Agent is an autonomous software entity that:
  • Understands natural language input from users
  • Reasons about problems using integrated knowledge and context
  • Acts by calling tools and functions to accomplish tasks
  • Learns from interactions and improves over time
  • Communicates results back to users in natural language

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Agent Architecture

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Agent Components

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1. LLM Brain

The core intelligence powered by language models like GPT-4, Claude, or others. Responsibilities:
  • Natural language understanding
  • Reasoning and decision making
  • Response generation
  • Context awareness

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2. Context Memory

Maintains conversation history and relevant context. Features:
  • Short-term conversation memory
  • Long-term interaction history
  • Context window management
  • Memory compression and summarization

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3. Task Planner

Breaks down complex requests into actionable steps. Capabilities:
  • Goal decomposition
  • Step prioritization
  • Dependency management
  • Execution planning

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4. Action Controller

Manages tool execution and external interactions. Functions:
  • Tool selection and invocation
  • Parameter validation
  • Error handling and retry logic
  • Result processing

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Agent Types

Definable.ai supports different types of agents for various use cases:

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Chat Agents

Designed for conversational interactions and customer support. Characteristics:
  • Natural conversation flow
  • Context-aware responses
  • Multi-turn dialogue handling
  • Personality and tone configuration
Example Use Cases:
  • Customer service representatives
  • FAQ assistants
  • Product recommendation systems
  • Educational tutors

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Function Agents

Specialized for executing specific functions and API calls. Characteristics:
  • Task-oriented interactions
  • Tool-heavy workflows
  • Structured input/output
  • High accuracy requirements
Example Use Cases:
  • Data processing systems
  • API orchestrators
  • Calculation engines
  • Integration specialists

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Workflow Agents

Handle complex, multi-step processes and business workflows. Characteristics:
  • Process automation
  • Step-by-step execution
  • State management
  • Error recovery
Example Use Cases:
  • Order processing systems
  • Content publishing workflows
  • Data migration processes
  • Approval workflows

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Personal Assistants

Comprehensive helpers for productivity and daily tasks. Characteristics:
  • Multi-domain knowledge
  • Personal context awareness
  • Proactive suggestions
  • Cross-platform integration
Example Use Cases:
  • Executive assistants
  • Research assistants
  • Content creators
  • Project managers

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Agent Lifecycle

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1. Design Phase

Define the agent’s purpose, capabilities, and constraints. Key Activities:
  • Requirements gathering
  • Use case definition
  • Success metrics identification
  • Architecture planning

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2. Creation Phase

Set up the basic agent structure and configuration. Key Activities:
  • Agent initialization
  • Model selection
  • Basic parameter configuration
  • Initial testing

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3. Configuration Phase

Add tools, knowledge bases, and fine-tune behavior. Key Activities:
  • Tool integration
  • Knowledge base attachment
  • Parameter tuning
  • Prompt engineering

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4. Testing Phase

Validate agent behavior across different scenarios. Key Activities:
  • Functional testing
  • Performance testing
  • Edge case validation
  • User acceptance testing

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5. Deployment Phase

Make the agent available for production use. Key Activities:
  • Production deployment
  • Monitoring setup
  • Access control configuration
  • Performance baseline establishment

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6. Monitoring Phase

Track agent performance and user interactions. Key Activities:
  • Metrics collection
  • Log analysis
  • User feedback gathering
  • Performance monitoring

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7. Optimization Phase

Improve agent performance based on real-world usage. Key Activities:
  • Performance analysis
  • Configuration adjustments
  • Training data updates
  • Feature enhancements

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Agent Configuration

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System Prompt

The foundation of agent behavior, defining personality, role, and capabilities. 
Example System Prompt:
You are a customer support agent for TechCorp's e-commerce platform. 
Your primary goals are to:
1. Help customers with order inquiries and tracking
2. Resolve product-related questions
3. Process returns and exchanges
4. Escalate complex issues to human agents

Guidelines:
- Be friendly, professional, and empathetic
- Provide accurate information using available tools
- Ask clarifying questions when needed
- Maintain customer privacy and security

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Parameters

Fine-tune agent behavior and response characteristics.
ParameterDescriptionRangeDefault
temperatureResponse creativity/randomness0.0 - 1.00.7
max_tokensMaximum response length1 - 40961000
top_pNucleus sampling threshold0.0 - 1.00.9
frequency_penaltyReduce repetition-2.0 - 2.00.0
presence_penaltyEncourage topic diversity-2.0 - 2.00.0

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Tool Integration

Connect agents to external capabilities and data sources.

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Agent Capabilities

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Natural Language Processing

  • Understanding: Parse user intent and extract key information
  • Generation: Create human-like responses
  • Translation: Support multiple languages
  • Summarization: Condense information into key points

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Reasoning and Decision Making

  • Logical reasoning: Apply rules and constraints
  • Causal reasoning: Understand cause and effect
  • Probabilistic reasoning: Handle uncertainty
  • Multi-step planning: Break down complex tasks

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Learning and Adaptation

  • Few-shot learning: Learn from examples
  • Context adaptation: Adjust behavior based on situation
  • Feedback incorporation: Improve from user corrections
  • Domain specialization: Focus on specific areas

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Integration Capabilities

  • API connections: Call external services
  • Database queries: Access structured data
  • File processing: Handle documents and media
  • Real-time updates: Stay current with live data

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Best Practices

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Agent Design

  1. Clear Purpose: Define specific roles and responsibilities
  2. Focused Scope: Avoid trying to do everything
  3. Consistent Personality: Maintain voice and tone
  4. Error Handling: Plan for failure scenarios

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Configuration Optimization

  1. Iterative Refinement: Start simple, add complexity gradually
  2. Performance Testing: Validate across different scenarios
  3. User Feedback: Incorporate real-world usage patterns
  4. Regular Updates: Keep knowledge and capabilities current

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Security and Privacy

  1. Access Controls: Implement proper authentication
  2. Data Protection: Secure sensitive information
  3. Audit Trails: Track agent actions and decisions
  4. Compliance: Meet regulatory requirements

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Performance Metrics

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Quality Metrics

  • Accuracy: Correctness of responses
  • Relevance: Appropriateness to user queries
  • Completeness: Thoroughness of answers
  • Consistency: Uniform behavior patterns

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Efficiency Metrics

  • Response Time: Speed of agent replies
  • Token Usage: Cost efficiency
  • Success Rate: Task completion percentage
  • User Satisfaction: Feedback scores

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Usage Metrics

  • Conversation Volume: Number of interactions
  • Session Duration: Length of user engagements
  • Feature Utilization: Tool and capability usage
  • Retention Rate: User return frequency

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Common Challenges and Solutions

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Challenge: Context Loss

Problem: Agent forgets important information from earlier in the conversation. Solution: Implement context summarization and memory management strategies.

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Challenge: Tool Selection

Problem: Agent chooses wrong tools or uses them incorrectly. Solution: Improve tool descriptions and provide clear usage examples.

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Challenge: Hallucination

Problem: Agent provides incorrect or made-up information. Solution: Ground responses in knowledge base data and implement fact-checking.

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Challenge: Performance Degradation

Problem: Agent becomes slower or less accurate over time. Solution: Regular monitoring, optimization, and model updates.

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Next Steps

Now that you understand AI Agents, explore related concepts:
  • Knowledge Base - Learn how agents access and use information
  • Tools - Discover how to extend agent capabilities
  • Vector Database - Understand semantic search and retrieval
  • LLM Models - Choose the right language model for your agents
Ready to create your first agent? Check out the API Reference or start with our Getting Started Guide.