Understanding Agents

Deep dive into Atlas agents - the AI workers that power your automations

Understanding Agents

Agents are the heart of Atlas. They’re autonomous AI entities that perform tasks, make decisions, and work together to accomplish complex goals. Let’s explore what makes them tick.

Agent Philosophy

Atlas agents are designed to be:

• Autonomous - They work independently once given a task
• Specialized - Each agent has a specific purpose and expertise
• Collaborative - Agents can work together in workflows
• Intelligent - They can reason, use tools, and learn from context
• Secure - Each runs in an isolated environment

Types of Agents

1. LLM Agents 🤖

Language model agents powered by AI providers like Anthropic, OpenAI, etc.

agents:
  analyst:
    type: "llm"
    model: "claude-3-5-sonnet-20241022"
    purpose: "Analyze data and provide insights"
    prompts:
      system: "You are a data analyst..."

Characteristics:

• Natural language understanding
• Complex reasoning capabilities
• Tool usage
• Creative problem solving

Best for:

• Analysis and research
• Content generation
• Decision making
• Interactive conversations

2. System Agents ⚙️

Built-in agents provided by Atlas for platform operations.

agents:
  chat:
    type: "system"
    agent: "conversation"  # Uses ConversationAgent
    config:
      model: "claude-3-5-sonnet-20241022"
      use_reasoning: true

Available System Agents:

• conversation - Interactive chat sessions
• memory - Memory management operations
• synthesizer - Content synthesis and summarization

Best for:

• Platform operations
• Standardized workflows
• Performance-critical tasks

3. Remote Agents 🌐

External agents accessed via network protocols.

agents:
  code-reviewer:
    type: "remote"
    config:
      protocol: "acp"  # Agent Communication Protocol
      endpoint: "https://api.codereviewer.ai/agent"
      auth:
        type: "bearer"
        token_env: "CODEREVIEWER_TOKEN"

Characteristics:

• Integrate third-party AI services
• Distributed processing
• Specialized external capabilities

Best for:

• Leveraging external AI services
• Distributed architectures
• Specialized domain expertise

Agent Anatomy

Core Components

agents:
  researcher:
    # Identity
    type: "llm"
    description: "Research specialist for gathering information"
    
    # Model Configuration
    model: "claude-3-5-sonnet-20241022"
    
    # Purpose & Behavior
    purpose: "Research and analyze information"
    prompts:
      system: |
        You are a research specialist with expertise in:
        - Information gathering
        - Source validation
        - Pattern analysis
        - Clear summarization
      
      user: "Research the following topic: {input}"
    
    # Capabilities
    tools: ["web-search", "calculator", "file-reader"]
    
    # Operational Config
    config:
      temperature: 0.3
      max_tokens: 2000
      timeout: "120s"
      retry_on_failure: true
      max_retries: 3

Agent Lifecycle

1. Initialization - Agent loaded and configured
2. Validation - Safety and capability checks
3. Execution - Task processing begins
4. Tool Usage - External capabilities invoked
5. Output - Results generated
6. Cleanup - Resources released

Agent Capabilities

1. Reasoning

Agents can think through problems step-by-step:

config:
  use_reasoning: true  # Enable reasoning mode
  max_reasoning_steps: 10

This enables agents to:

• Break down complex problems
• Consider multiple approaches
• Self-correct mistakes
• Explain their thinking

2. Tool Usage

Agents can use tools to extend their capabilities:

tools: ["filesystem", "web-search", "database"]

# Or with MCP servers
tools:
  mcp:
    servers:
      github:
        transport:
          type: "stdio"
          command: "github-mcp-server"

3. Memory Access

Agents can access workspace memory:

config:
  memory_enabled: true
  memory_scope: "session"  # or "workspace"
  context_window: 10  # Recent memories to include

4. Collaboration

Agents work together through:

• Sequential execution - Output passed between agents
• Parallel execution - Multiple agents work simultaneously
• Hierarchical supervision - Supervisors coordinate agents
• Shared context - Common memory and state

Agent Patterns

1. The Specialist Pattern

Create focused agents for specific tasks:

agents:
  # Data specialist
  data-analyst:
    type: "llm"
    purpose: "Analyze datasets and find patterns"
    tools: ["pandas", "matplotlib"]
  
  # Writing specialist
  report-writer:
    type: "llm"
    purpose: "Create clear, professional reports"
    prompts:
      system: "You write executive-level reports..."
  
  # Integration specialist
  api-integrator:
    type: "llm"
    purpose: "Connect and integrate with APIs"
    tools: ["http-client", "json-parser"]

2. The Pipeline Pattern

Chain agents for complex workflows:

jobs:
  research-pipeline:
    execution:
      strategy: "sequential"
      agents:
        - id: "researcher"
          task: "Gather information on {topic}"
        - id: "analyst"
          task: "Analyze findings and identify patterns"
        - id: "writer"
          task: "Create comprehensive report"

3. The Ensemble Pattern

Multiple agents provide different perspectives:

jobs:
  decision-making:
    execution:
      strategy: "parallel"
      agents:
        - id: "optimist"
          task: "Identify opportunities"
        - id: "pessimist"
          task: "Identify risks"
        - id: "strategist"
          task: "Recommend approach"

4. The Supervisor Pattern

Hierarchical coordination:

agents:
  team-lead:
    type: "llm"
    purpose: "Coordinate team of specialists"
    prompts:
      system: |
        You manage a team of AI agents.
        Break down tasks and delegate appropriately.
        Synthesize results into cohesive output.

Configuration Best Practices

1. Model Selection

Choose the right model for the task:

# Fast, simple tasks
simple-task:
  model: "claude-3-5-haiku-20241022"
  
# Complex reasoning
complex-task:
  model: "claude-3-5-sonnet-20241022"
  
# Critical decisions
critical-task:
  model: "claude-3-5-opus-20241022"

2. Temperature Settings

Control creativity vs consistency:

# Analytical tasks (low temperature)
analyst:
  config:
    temperature: 0.2  # More focused, deterministic
    
# Creative tasks (higher temperature)
creative-writer:
  config:
    temperature: 0.8  # More varied, creative

3. Token Management

Optimize for efficiency:

config:
  max_tokens: 1000  # Limit response length
  max_input_tokens: 4000  # Control context size

4. Error Handling

Build resilient agents:

config:
  timeout: "60s"
  retry_on_failure: true
  max_retries: 3
  fallback_behavior: "return_partial"

Security Considerations

Isolation

Each agent runs in an isolated Deno Web Worker:

• No access to file system by default
• Network access controlled by permissions
• Memory isolation between agents

Permissions

Control what agents can do:

agents:
  restricted-agent:
    permissions:
      allow_net: ["api.example.com"]
      allow_read: ["./data"]
      deny_write: ["./"]

Input Validation

Always validate agent inputs:

agents:
  safe-processor:
    input_schema:
      type: "object"
      properties:
        data:
          type: "string"
          maxLength: 1000
      required: ["data"]

Performance Optimization

1. Caching

Enable response caching:

config:
  cache_enabled: true
  cache_ttl: "3600s"  # 1 hour

2. Batching

Process multiple items efficiently:

config:
  batch_size: 10
  parallel_processing: true

3. Resource Limits

Prevent runaway agents:

config:
  max_memory: "512MB"
  max_cpu_time: "30s"

Next Steps

• Try the ConversationAgent
• Learn about Using LLM Agents
• Explore Agent Prompts
• Discover Giving Agents Tools