Agentic AI Models Guide

Build and deploy autonomous AI agents that can plan, execute, and learn from complex tasks.

What are Agentic Models?

Agentic models are AI systems that can:

• Plan - Break down complex goals into steps
• Execute - Take actions to achieve objectives
• Learn - Improve from experience
• Adapt - Handle unexpected situations
• Collaborate - Work with other agents

Key Differences from Traditional AI

Core Concepts

Agent Architecture

graph TD
    A[Goal] --> B[Planner]
    B --> C[Task Queue]
    C --> D[Executor]
    D --> E[Tools/Actions]
    E --> F[Environment]
    F --> G[Observer]
    G --> H[Memory]
    H --> B

    I[Learning Module] --> B
    I --> D

Agent Components

1. Planner

Breaks down high-level goals into actionable tasks:

class AgentPlanner {
  async createPlan(goal, context) {
    // Analyze goal
    const understanding = await this.understandGoal(goal);

    // Generate task sequence
    const tasks = await this.generateTasks(understanding, context);

    // Optimize execution order
    const plan = await this.optimizePlan(tasks);

    return plan;
  }
}

2. Executor

Carries out planned tasks:

class AgentExecutor {
  async executePlan(plan) {
    const results = [];

    for (const task of plan.tasks) {
      try {
        const result = await this.executeTask(task);
        results.push(result);

        // Update plan based on results
        await this.updatePlan(plan, result);
      } catch (error) {
        await this.handleError(error, task, plan);
      }
    }

    return results;
  }
}

3. Memory System

Stores and retrieves relevant information:

class AgentMemory {
  constructor() {
    this.shortTerm = new ShortTermMemory();
    this.longTerm = new LongTermMemory();
    this.episodic = new EpisodicMemory();
  }

  async remember(event) {
    // Store in appropriate memory
    await this.shortTerm.store(event);

    // Consolidate important info
    if (this.isImportant(event)) {
      await this.longTerm.store(event);
    }

    // Track episodes
    await this.episodic.addToCurrentEpisode(event);
  }
}

4. Tool Interface

Connects agents to external capabilities:

class AgentTools {
  constructor() {
    this.tools = new Map();
  }

  register(name, tool) {
    this.tools.set(name, {
      execute: tool.execute,
      schema: tool.schema,
      description: tool.description,
    });
  }

  async use(toolName, params) {
    const tool = this.tools.get(toolName);
    return await tool.execute(params);
  }
}

Creating Agentic Models

Basic Agent Template

class CustomAgent {
  constructor(config) {
    this.name = config.name;
    this.goals = config.goals;
    this.planner = new AgentPlanner(config.plannerModel);
    this.executor = new AgentExecutor(config.executorModel);
    this.memory = new AgentMemory();
    this.tools = new AgentTools();

    // Register available tools
    this.registerTools(config.tools);
  }

  async run(input) {
    // Understand the request
    const goal = await this.parseGoal(input);

    // Retrieve relevant context
    const context = await this.memory.getContext(goal);

    // Create execution plan
    const plan = await this.planner.createPlan(goal, context);

    // Execute plan
    const results = await this.executor.executePlan(plan);

    // Learn from execution
    await this.learn(plan, results);

    return results;
  }
}

Agent Types

Research Agent

Gathers and synthesizes information:

name: ResearchAgent
capabilities:
  - web_search
  - document_analysis
  - fact_checking
  - summarization
  - citation_tracking

example_goal: 'Research the latest developments in quantum computing'

Code Agent

Writes and debugs code:

name: CodeAgent
capabilities:
  - code_generation
  - debugging
  - testing
  - refactoring
  - documentation

example_goal: 'Create a REST API for user management'

Data Agent

Analyzes and processes data:

name: DataAgent
capabilities:
  - data_cleaning
  - statistical_analysis
  - visualization
  - pattern_recognition
  - prediction

example_goal: 'Analyze sales trends and predict Q4 revenue'

Task Agent

Manages and executes workflows:

name: TaskAgent
capabilities:
  - task_scheduling
  - dependency_management
  - progress_tracking
  - notification
  - reporting

example_goal: 'Coordinate the product launch campaign'

Multi-Agent Systems

Agent Communication

Agents can collaborate through message passing:

class AgentCommunication {
  constructor() {
    this.agents = new Map();
    this.messageQueue = new Queue();
  }

  registerAgent(agent) {
    this.agents.set(agent.id, agent);

    agent.on('message', (msg) => {
      this.routeMessage(msg);
    });
  }

  async routeMessage(message) {
    const recipient = this.agents.get(message.to);
    if (recipient) {
      await recipient.receiveMessage(message);
    }
  }
}

Coordination Patterns

1. Hierarchical

Manager agent coordinates specialist agents:

graph TD
    A[Manager Agent] --> B[Research Agent]
    A --> C[Analysis Agent]
    A --> D[Report Agent]

2. Peer-to-Peer

Agents collaborate as equals:

graph LR
    A[Agent A] <--> B[Agent B]
    B <--> C[Agent C]
    C <--> A

3. Blackboard

Shared workspace for collaboration:

graph TD
    A[Blackboard] --> B[Agent 1]
    A --> C[Agent 2]
    A --> D[Agent 3]
    B --> A
    C --> A
    D --> A

Agent Development Framework

Agent Definition Language (ADL)

Define agents declaratively:

agent:
  name: CustomerSupportAgent
  version: 1.0.0

  goals:
    - Resolve customer issues
    - Maintain satisfaction above 90%
    - Escalate complex issues

  capabilities:
    - intent: understand_issue
      model: gpt-4
      prompt: |
        Analyze this customer message and identify:
        1. Main issue
        2. Urgency level
        3. Sentiment

    - intent: search_knowledge
      tool: knowledge_base
      params:
        index: support_docs

    - intent: generate_response
      model: gpt-4
      prompt: |
        Create a helpful response that:
        - Acknowledges the issue
        - Provides solution
        - Maintains friendly tone

  memory:
    type: conversational
    retention: 30_days

  constraints:
    - max_response_time: 60s
    - escalate_after_attempts: 3
    - forbidden_actions: [delete_data, modify_account]

Training Agents

Agents can learn from:

1. Supervised Learning

async function trainAgent(agent, trainingData) {
  for (const example of trainingData) {
    // Show correct behavior
    await agent.observe(example.input, example.correctAction);

    // Reinforce good decisions
    await agent.reinforce(example.reward);
  }

  // Fine-tune decision making
  await agent.updatePolicy();
}

2. Reinforcement Learning

class RLAgent {
  async learn(environment) {
    let state = environment.reset();

    while (!environment.isDone()) {
      // Choose action
      const action = await this.selectAction(state);

      // Execute and observe
      const { nextState, reward, done } = await environment.step(action);

      // Update knowledge
      await this.updateQValue(state, action, reward, nextState);

      state = nextState;
    }
  }
}

3. Learning from Feedback

async function improveFromFeedback(agent, feedback) {
  // Analyze feedback
  const insights = await analyzeFeedback(feedback);

  // Update behavior
  for (const insight of insights) {
    if (insight.type === 'mistake') {
      await agent.addConstraint(insight.constraint);
    } else if (insight.type === 'success') {
      await agent.reinforcePattern(insight.pattern);
    }
  }

  // Test improvements
  await validateBehaviorChange(agent);
}

Deployment and Management

Agent Deployment

Deploy agents to GreenMonkey:

// agent.config.js
export default {
  name: 'CustomerSupportAgent',
  version: '1.0.0',

  deployment: {
    instances: 3,
    scaling: {
      min: 1,
      max: 10,
      metric: 'queue_length',
    },

    resources: {
      memory: '2GB',
      timeout: 300,
      concurrent_tasks: 5,
    },
  },

  monitoring: {
    metrics: ['response_time', 'success_rate', 'escalation_rate'],
    alerts: [
      {
        condition: 'success_rate < 0.8',
        action: 'notify_admin',
      },
    ],
  },
};

Monitoring Agents

Track agent performance:

class AgentMonitor {
  constructor(agent) {
    this.agent = agent;
    this.metrics = new MetricsCollector();
  }

  async trackExecution(goal, execution) {
    const startTime = Date.now();

    try {
      const result = await execution();

      this.metrics.record({
        agent: this.agent.id,
        goal: goal,
        duration: Date.now() - startTime,
        success: true,
        steps: result.steps.length,
        toolsUsed: result.toolsUsed,
      });

      return result;
    } catch (error) {
      this.metrics.record({
        agent: this.agent.id,
        goal: goal,
        duration: Date.now() - startTime,
        success: false,
        error: error.message,
      });

      throw error;
    }
  }
}

Agent Marketplace

Share and monetize agents:

marketplace_listing:
  name: 'SEO Content Strategist Agent'
  category: 'Marketing'

  description: |
    Autonomous agent that creates comprehensive
    SEO strategies and content plans.

  capabilities:
    - Keyword research
    - Content planning
    - Competitor analysis
    - Performance tracking

  pricing:
    model: 'usage_based'
    rate: '$0.10 per task'

  requirements:
    - api_keys: ['openai', 'semrush']
    - min_memory: '1GB'

Advanced Agent Patterns

Chain of Thought (CoT)

Enable reasoning transparency:

class ChainOfThoughtAgent {
  async reason(problem) {
    const thoughts = [];

    // Step 1: Understand the problem
    const understanding = await this.think(`Let me understand this problem: ${problem}`);
    thoughts.push(understanding);

    // Step 2: Break down into steps
    const steps = await this.think(`To solve this, I need to: ${understanding}`);
    thoughts.push(steps);

    // Step 3: Execute each step
    for (const step of steps) {
      const result = await this.executeStep(step);
      thoughts.push(`Step result: ${result}`);
    }

    return {
      solution: await this.synthesize(thoughts),
      reasoning: thoughts,
    };
  }
}

Self-Reflection

Agents that evaluate their own performance:

class ReflectiveAgent {
  async executeWithReflection(task) {
    // Initial attempt
    let result = await this.execute(task);

    // Self-evaluation
    const evaluation = await this.evaluate(task, result);

    // Iterate if needed
    while (evaluation.score < this.threshold) {
      const improvements = await this.reflect(evaluation);
      result = await this.executeWithImprovements(task, improvements);
      evaluation = await this.evaluate(task, result);
    }

    return result;
  }
}

Tool Creation

Agents that build their own tools:

class ToolBuildingAgent {
  async createTool(need) {
    // Identify tool requirements
    const spec = await this.specifyTool(need);

    // Generate tool code
    const code = await this.generateCode(spec);

    // Test the tool
    const tests = await this.generateTests(spec);
    const results = await this.runTests(code, tests);

    // Deploy if successful
    if (results.passed) {
      await this.deployTool(code);
      this.tools.register(spec.name, code);
    }

    return code;
  }
}

Security and Safety

Agent Constraints

Define boundaries for agent behavior:

constraints:
  actions:
    forbidden:
      - delete_user_data
      - modify_permissions
      - access_private_keys

  resources:
    max_api_calls: 1000/hour
    max_memory: 2GB
    max_execution_time: 5min

  data_access:
    allowed_sources:
      - public_database
      - user_provided
    denied_sources:
      - internal_systems
      - other_users_data

Monitoring and Control

class AgentSupervisor {
  async supervise(agent, task) {
    const monitor = new SafetyMonitor(agent);

    // Set up safety checks
    monitor.on('action', async (action) => {
      if (this.isDangerous(action)) {
        throw new Error(`Blocked dangerous action: ${action}`);
      }
    });

    // Execute with supervision
    try {
      return await agent.execute(task);
    } catch (error) {
      await this.handleSafetyViolation(agent, error);
      throw error;
    }
  }
}

Real-World Examples

Customer Service Agent

Complete implementation:

class CustomerServiceAgent extends Agent {
  constructor() {
    super({
      name: 'CustomerServicePro',
      model: 'gpt-4',
      tools: ['search_kb', 'create_ticket', 'send_email'],
    });
  }

  async handleCustomerQuery(query) {
    // Understand intent
    const intent = await this.analyzeIntent(query);

    // Search knowledge base
    const relevantInfo = await this.searchKnowledge(intent);

    // Generate response
    const response = await this.generateResponse(query, intent, relevantInfo);

    // Check if escalation needed
    if (this.needsEscalation(intent, response)) {
      await this.escalateToHuman(query, intent);
    }

    return response;
  }
}

Research Assistant Agent

class ResearchAssistant extends Agent {
  async conductResearch(topic) {
    // Plan research strategy
    const plan = await this.planResearch(topic);

    // Gather information
    const sources = await Promise.all([
      this.searchAcademic(topic),
      this.searchWeb(topic),
      this.searchNews(topic),
    ]);

    // Analyze and synthesize
    const analysis = await this.analyzeSources(sources);
    const synthesis = await this.synthesize(analysis);

    // Generate report
    return await this.generateReport({
      topic,
      sources,
      analysis,
      synthesis,
      citations: this.extractCitations(sources),
    });
  }
}

Best Practices

Design Principles

1. Clear Goals - Define specific objectives
2. Bounded Autonomy - Set appropriate limits
3. Transparency - Make reasoning visible
4. Fail Gracefully - Handle errors properly
5. Learn Continuously - Improve from experience

Performance Optimization

// Cache frequent operations
const cache = new LRUCache({ max: 1000 });

// Batch similar tasks
const batcher = new TaskBatcher({
  maxBatchSize: 10,
  maxWaitTime: 1000,
});

// Parallel execution where possible
const results = await Promise.all(independentTasks.map((task) => agent.execute(task)));

Testing Agents

describe('CustomerServiceAgent', () => {
  it('should handle simple queries', async () => {
    const agent = new CustomerServiceAgent();
    const response = await agent.handleQuery('How do I reset my password?');

    expect(response).toContain('reset');
    expect(response).toContain('password');
    expect(response.sentiment).toBe('helpful');
  });

  it('should escalate complex issues', async () => {
    const agent = new CustomerServiceAgent();
    const spy = jest.spyOn(agent, 'escalateToHuman');

    await agent.handleQuery('I want to sue your company');

    expect(spy).toHaveBeenCalled();
  });
});

Next Steps

1. Build your first agent
2. Explore multi-agent systems
3. Deploy agents to production
4. Join agent builders community