Building Agentic AI Applications: Lessons from YALG.ai

When I started building YALG.ai, I wanted to solve a real problem: creating engaging LinkedIn content consistently. What began as a simple idea evolved into a deep exploration of Agentic AI - autonomous AI systems that can plan, execute, and adapt their actions to achieve goals.

What Makes AI "Agentic"?

Traditional AI applications follow a simple input-output pattern. You give them data, they process it, and return results. Agentic AI systems are different - they can:

• Plan multi-step approaches to complex problems
• Execute actions autonomously
• Reflect on their outputs and adjust strategies
• Learn from user interactions and feedback

For YALG.ai, this meant creating an AI that doesn't just generate posts, but understands context, analyzes engagement patterns, and adapts its writing style based on what works.

The Technical Architecture

Here's how I structured the Agentic AI system:

class LinkedInPostAgent:
    def __init__(self):
        self.planner = PlanningModule()
        self.executor = ExecutionModule()
        self.reflector = ReflectionModule()
        self.memory = UserContextMemory()
    
    async def generate_post(self, user_input, context):
        # Plan the content strategy
        plan = await self.planner.create_strategy(
            user_input, 
            self.memory.get_user_preferences(context.user_id)
        )
        
        # Execute the content generation
        draft = await self.executor.generate_content(plan)
        
        # Reflect and improve
        final_post = await self.reflector.refine_content(
            draft, 
            plan.success_criteria
        )
        
        # Learn from the interaction
        self.memory.update_preferences(context.user_id, final_post)
        
        return final_post

Key Technical Challenges

1. Context Persistence

One of the biggest challenges was maintaining context across conversations. Users don't want to re-explain their industry, tone preferences, or target audience every time.

Solution: I implemented a vector database using PostgreSQL with pgvector to store user preferences and interaction history, allowing the AI to build a persistent understanding of each user.

2. Multi-Agent Coordination

Different aspects of post creation required different expertise - strategy, writing, optimization. I designed multiple specialized agents:

interface AgentSystem {
  strategyAgent: StrategyPlanner;
  contentAgent: ContentWriter;
  optimizationAgent: EngagementOptimizer;
  coordinator: AgentCoordinator;
}

3. Quality Control

Autonomous systems can produce inconsistent results. I built in multiple checkpoints:

• Semantic validation using embeddings to ensure content relevance
• Tone analysis to maintain brand voice consistency
• Engagement prediction using historical LinkedIn data patterns

Real-World Performance

After 6 months of development and testing:

• 85% user retention after first month
• 3x average engagement on generated posts vs. user's previous content
• 60% time savings reported by regular users

Lessons Learned

1. Start Simple, Scale Smart

My first version was a complex multi-agent system that was hard to debug. I learned to build incrementally - start with a single capable agent, then add specialization as needed.

2. User Feedback is Everything

The most successful features came from user feedback, not my initial assumptions. The AI got dramatically better when I implemented a feedback loop where users could rate and refine outputs.

3. Context is King

The difference between good and great AI content is context. Investing in sophisticated context management paid huge dividends in output quality.

Looking Forward

Agentic AI represents a fundamental shift in how we build AI applications. Instead of building tools, we're building autonomous digital workers that can understand goals and figure out how to achieve them.

At Technova Industries, I'm now applying these same principles to urban safety systems - AI agents that can analyze video feeds, detect anomalies, and coordinate response actions autonomously.

The future isn't just AI that answers questions - it's AI that solves problems end-to-end.

Want to discuss Agentic AI or see YALG.ai in action? Connect with me on LinkedIn or check out the platform at yalg.ai.