Building AGI-like agents

A practical guide to intelligent agents that feel like an AGI, but for a specific domain

Want to build AI that actually thinks, learns, and solves problems on its own? This guide shows you how to create domain-specific AGI systems using practical tools and frameworks available today.

What is AGI?

You've probably heard about AGI (Artificial General Intelligence), the sci-fi dream of human-level AI. It is debatable how much time will it take us to achieve AGI but we are certainly not there yet.

AGI could emerge through several converging paths such as:

1. Neural scaling, where massive models learn broad capabilities from data
2. Symbolic reasoning, which adds structure and logic for true understanding
3. Hybrid neuro-symbolic systems, blending pattern recognition with explicit reasoning
4. Evolutionary approaches, where intelligence develops through interaction and adaptation
5. Cognitive architectures, which replicate human-like memory, planning, and reflection

The most likely route is a hybrid of these different paths, combining large neural models with structured reasoning, continual learning, and real-world grounding.

State of current AI systems

Most current AI systems are narrow experts: they summarize text, classify images, or execute code—but they do not feel like AGI yet.

As of today, you don't need to build a super-intelligent robot either to create something incredibly useful. We may achieve something that behaves like AGI but limited in context of a specific domain.

A domain-specific AI that exhibits following, will feel like AGI:

1. Autonomy: it decomposes vague instructions into concrete plans.
2. Memory: it remembers prior decisions, feedback, and context.
3. Reasoning: it chains thoughts to reach conclusions, not just next tokens.
4. Adaptability: it learns new workflows and tools over time.
5. Self-correction: it evaluates its own performance and revises strategies.

Domain-specific AI is a bounded generalist—general within its professional universe. It is practical today. We can build AI that's genuinely intelligent within a specific field – like scientific research, finance, legal work, or software development.

We can achieve this through cognitive architecture route, by building an AI agent that can:

• Break down complex problems on its own
• Remember what it's learned from past work
• Plan multiple steps ahead
• Use the right tools at the right time
• Learn from mistakes and improve

This guide shows how we can achieve the same, today.

The Agentic architecture as the solution

Instead of just calling an AI model and hoping for the best, we build a complete cognitive system with different parts working together:

User Input 
    ↓
Memory System (remembers context)
    ↓
Planning Engine (breaks down goals)
    ↓
Reasoning Core (thinks through solutions)
    ↓
Tool Executor (takes actions)
    ↓
Evaluator (checks results)
    ↓
Learning Loop (improves over time)

Think of it like building a brain with specialized regions, not just a parrot that repeats things.

The five key components

1. Memory system

What it does: Stores and retrieves relevant information from past interactions. Memory gives the system continuity — “experience” and context persistence.

You will need three layers of memories:

• Short-term memory: Context window or rolling buffer of current conversation or task.
• Long-term memory: Vector database (e.g., Pinecone, Weaviate, FAISS) storing embeddings of past interactions, documents, and facts. Retrieved dynamically using similarity search.
• Episodic + semantic layers: Episodic = past sessions/tasks. Semantic = distilled knowledge or summaries.

How to build it:

• Store conversation history and outcomes
• For larger memory, use vector databases like Pinecone or FAISS
• Enable semantic search (finding related info, not just keywords)

// Simple memory example
const memory = {
  shortTerm: [], // Recent conversation
  longTerm: vectorDB, // Historical knowledge
  
  remember(interaction) {
    this.shortTerm.push(interaction);
    this.longTerm.store(interaction);
  },
  
  recall(query) {
    return this.longTerm.search(query);
  }
};

2. Planning engine

What it does: Breaks big goals into actionable steps.

Popular approaches:

• Chain-of-Thought: Step-by-step reasoning
• Tree-of-Thoughts: Exploring multiple solution paths
• ReAct: Combining reasoning with actions
• Self-reflection and critique loops

// Basic planning pattern
async function planAndExecute(goal) {
  const plan = await planner.decompose(goal);
  
  for (const step of plan.steps) {
    const context = memory.recall(step);
    const action = await agent.decide(step, context);
    const result = await executor.run(action);
    
    memory.remember({ step, action, result });
  }
}

3. Tool orchestration

What it does: Lets your AI use external tools like APIs, databases, and code execution.

Common tools:

• Search engines
• Database queries
• Code interpreters
• Web scrapers
• Custom APIs

The key is teaching your AI when to use which tool.

4. Self-evaluation

What it does: Checks if the AI's output is actually good.

Methods:

• Confidence scoring
• Output validation
• Human feedback loops
• Automated tests

async function selfCorrectingAgent(task) {
  let attempts = 0;
  let result;
  
  while (attempts < 3) {
    result = await agent.execute(task);
    const evaluation = await evaluator.check(result);
    
    if (evaluation.isGood) {
      return result;
    }
    
    // Learn from mistakes
    agent.learn(evaluation.feedback);
    attempts++;
  }
  
  return result;
}

5. Continuous learning

What it does: Improves the system over time using real-world feedback.

Techniques:

• Reinforcement Learning from Human Feedback (RLHF)
• Storing successful strategies
• Fine-tuning on domain data

Building your first AGI-like agent

Step 1: Choose your domain

Pick a specific area where you want AGI-like behavior:

• Research: Literature reviews and data analysis
• Code: Full-stack development and debugging
• Business Analysis: Market research and reporting
• Legal: Document review and case research

Step 2: Gather domain knowledge

Your AI needs to learn the field:

• Documentation and manuals
• Example workflows
• Domain-specific APIs
• Historical data

Step 3: Build the core loop

Here's a practical starting point using popular frameworks:

import { ChatOpenAI } from "langchain/chat_models/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "langchain/tools/calculator";
import { WebBrowser } from "langchain/tools/webbrowser";

// Set up tools
const tools = [
  new Calculator(),
  new WebBrowser(),
  // Add your custom tools
];

// Create the agent
const agent = await initializeAgentExecutorWithOptions(
  tools,
  new ChatOpenAI({ temperature: 0 }),
  {
    agentType: "chat-conversational-react-description",
    verbose: true,
  }
);

// Run a task
const result = await agent.call({
  input: "Analyze the latest trends in renewable energy and create a summary"
});

Step 4: Add memory

import { BufferMemory } from "langchain/memory";
import { VectorStore } from "langchain/vectorstores";

const memory = new BufferMemory({
  returnMessages: true,
  memoryKey: "chat_history",
});

// Store long-term knowledge
const vectorStore = await VectorStore.fromDocuments(
  domainDocuments,
  embeddings
);

Step 5: Implement feedback

async function agentWithFeedback(task) {
  const result = await agent.execute(task);
  
  // Get human feedback
  const feedback = await getUserFeedback(result);
  
  // Store for learning
  await trainingData.store({
    task,
    result,
    feedback,
    timestamp: new Date()
  });
  
  return result;
}

Popular frameworks and tools

Frameworks for building agents

LangChain

• Great for beginners
• Lots of pre-built tools
• Strong community support

AutoGen (Microsoft)

• Multi-agent conversations
• Complex workflows
• Good for team simulations

CrewAI

• Role-based agents
• Built-in collaboration
• Simple to use

Essential Tools

Real-world examples

Pattern 1: Research assistant

const researchAgent = {
  async analyze(topic) {
    // 1. Search for papers
    const papers = await tools.search(topic);
    
    // 2. Read and summarize
    const summaries = await Promise.all(
      papers.map(p => llm.summarize(p))
    );
    
    // 3. Find connections
    const insights = await llm.synthesize(summaries);
    
    // 4. Generate report
    return await llm.writeReport(insights);
  }
};

Pattern 2: Code development agent

const devAgent = {
  async buildFeature(requirements) {
    // 1. Plan architecture
    const plan = await planner.design(requirements);
    
    // 2. Write code
    const code = await coder.implement(plan);
    
    // 3. Test
    const tests = await tester.verify(code);
    
    // 4. Fix issues
    if (tests.failed.length > 0) {
      return this.buildFeature(requirements); // Retry
    }
    
    return code;
  }
};

Measuring success

Key metrics to track

• Task success rate: Percentage of tasks completed correctly on first try
• Learning speed: How quickly performance improves over time
• Reasoning quality: Depth and accuracy of problem-solving steps
• Tool usage efficiency: Using the right tools without wasting calls
• Error recovery: Ability to fix mistakes without human help

Common pitfalls and how to avoid them

Pitfall #1: Too much autonomy too soon

Problem: Agent makes expensive API calls or wrong decisions
Solution: Start with human-in-the-loop approval for critical actions

Pitfall #2: Context window overflow

Problem: Trying to fit too much into the AI's memory
Solution: Use smart summarization and retrieval

Pitfall #3: No failure handling

Problem: System crashes on first error
Solution: Add retry logic and graceful degradation

Pitfall #4: Forgetting to log

Problem: Can't debug or improve the system
Solution: Log everything - inputs, outputs, decisions, errors

Security best practices

Always sandbox code execution

// BAD - Direct execution
eval(aiGeneratedCode); // Never do this!

// GOOD - Sandboxed execution
await docker.run(aiGeneratedCode, { timeout: 5000 });

Limit API access

• Use read-only credentials when possible
• Set rate limits
• Monitor costs closely

Validate All outputs

• Check for malicious code patterns
• Verify data before saving
• Review high-stakes decisions

The AGI Roadmap: Where this is heading

2025-2026: Foundation

• Single-domain expert agents
• Reliable tool usage
• Basic memory systems

2027-2028: Collaboration

• Multiple agents working together
• Cross-domain knowledge transfer
• Advanced planning capabilities

2029-2030: Integration

• Agents teaching other agents
• Unified knowledge graphs
• True lifelong learning

Getting started: Your first weekend build

Starter code template

// Complete starter template
import { ChatOpenAI } from "langchain/chat_models/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { BufferMemory } from "langchain/memory";

class DomainAgent {
  constructor(domain) {
    this.domain = domain;
    this.memory = new BufferMemory();
    this.tools = this.loadDomainTools(domain);
  }
  
  async execute(goal) {
    // 1. Recall relevant context
    const context = await this.memory.recall(goal);
    
    // 2. Plan approach
    const plan = await this.plan(goal, context);
    
    // 3. Execute steps
    const results = [];
    for (const step of plan) {
      const result = await this.executeStep(step);
      results.push(result);
      await this.memory.store(step, result);
    }
    
    // 4. Evaluate and learn
    await this.evaluate(goal, results);
    
    return results;
  }
  
  async plan(goal, context) {
    // Your planning logic
  }
  
  async executeStep(step) {
    // Your execution logic
  }
  
  async evaluate(goal, results) {
    // Your evaluation logic
  }
}

// Usage
const agent = new DomainAgent("research");
const result = await agent.execute("Analyze AI trends in 2025");

Essential Reading

• "ReAct: Synergizing Reasoning and Acting in Language Models" (2022)
• "Reflexion: Language Agents with Verbal Reinforcement Learning" (2023)

Communities

• Invide - Remote Developers Community (invite-only senior devs only or open discord)
• AI Agents Subreddit
• LangChain Discord
• Anthropic Developer Forum

Tools to explore

• LangChain – Start here for most projects
• LangSmith – Debug and monitor agents
• Chroma – Simple vector database
• E2B – Secure code execution
• LangGraph – Complex multi-agent workflows

Conclusion

We explored what AGI is and what is practical to do as of today and how you can do that. In summary:

• Domain-specific AGI is achievable today with existing tools
• Architecture matters more than model size - smart design beats raw power
• Start small and iterate - build one component at a time
• Memory and feedback are crucial - they enable actual learning
• Safety first - always sandbox and validate outputs

To get started, pick your domain, clone the starter template, add domain knowledge, test with real tasks – Start simple, scale up, track metrics and improve.

The future of AI likely isn't one massive superintelligence – it's a network of specialized, intelligent agents working together. And you can start building your piece of that future today.

Join the upcoming AI Agent hackathon, happening on discord every Sun.