Understanding Agentic Systems

The Spectrum of LLM Applications

It's crucial to understand where different types of LLM-based systems fall on the complexity spectrum:

1. Simple LLM Applications

   • Single LLM calls with prompt engineering
   • Basic RAG (Retrieval-Augmented Generation) implementations
   • Structured output parsing
   • These are NOT agents, just applications using LLM capabilities

2. Multi-Step LLM Applications

   • Multiple LLM calls in sequence
   • API integrations and external tool usage
   • Basic state management
   • While more complex, these still aren't necessarily agents

3. True AI Agents

   • Dynamic decision-making about process flow
   • Autonomous tool selection and usage
   • Complex state management and memory
   • Goal-oriented behavior with adaptive strategies

Common Misconceptions

Misconception 1: "My application uses multiple LLM calls, so it's an agent"

# This is NOT an agent - it's a multi-step LLM application
def process_document(doc):
    summary = llm.generate_summary(doc)
    keywords = llm.extract_keywords(summary)
    sentiment = llm.analyze_sentiment(summary)
    return {
        "summary": summary,
        "keywords": keywords,
        "sentiment": sentiment
    }

Misconception 2: "I'm using tools and APIs, so it's an agent"

# This is NOT an agent - it's an automated workflow
def analyze_stock(symbol):
    price_data = stock_api.get_price(symbol)
    news = news_api.get_recent_news(symbol)
    analysis = llm.analyze(f"Price: {price_data}, News: {news}")
    return analysis

Understanding Agentic Systems

1. Workflows vs Agents

Workflows:

# Predefined workflow example
class FinancialAnalysisWorkflow:
    def execute(self, query):
        # Fixed sequence of steps
        market_data = self.get_market_data()
        financial_statements = self.get_financial_statements()
        competitor_analysis = self.get_competitor_data()
        
        return self.generate_analysis(
            market_data,
            financial_statements,
            competitor_analysis
        )

Agents:

# True agent example
class FinancialAnalysisAgent:
    def analyze(self, query):
        # Dynamic planning
        analysis_plan = self.plan_analysis_strategy(query)
        
        # Flexible execution
        for step in analysis_plan:
            if self.needs_revision(step, self.current_state):
                analysis_plan = self.revise_plan()
            
            tool = self.select_appropriate_tool(step)
            result = self.execute_tool(tool, step)
            self.update_state(result)
            
            if self.goal_achieved():
                break
        
        return self.synthesize_findings()

When to Use Each Approach

1. Use Simple LLM Applications When:

   • Tasks are straightforward and well-defined
   • Minimal or no tool interaction is needed
   • Quick response times are critical
   • Cost efficiency is a primary concern

2. Use Workflows When:

   • Process steps are well-understood and consistent
   • Predictability is more important than flexibility
   • You need tight control over execution
   • Performance and reliability are crucial

3. Use Agents When:

   • Tasks require dynamic decision-making
   • Problems have multiple valid solution paths
   • Flexibility and adaptation are crucial
   • Complex tool interactions are needed
   • Tasks benefit from maintaining context

Implementation Best Practices

1. Start Simple

   • Begin with the simplest solution that could work
   • Only add complexity when clearly needed
   • Validate the need for agency before implementing it

2. Consider Trade-offs

   • Agents typically have higher latency
   • Cost increases with complexity
   • Maintenance burden grows with sophistication

3. Design for Observability

   • Implement comprehensive logging
   • Track decision-making processes
   • Monitor tool usage patterns

4. Build in Safeguards

   • Implement rate limiting
   • Add timeout mechanisms
   • Create fallback strategies

Evaluation Criteria for True Agency

Before implementing an agent, evaluate if your system really needs true agency by checking these criteria:

1. Decision Autonomy

   • Can the system choose different paths based on context?
   • Does it make meaningful decisions about tool usage?
   • Can it adapt its strategy during execution?

2. State Management

   • Does it maintain meaningful state?
   • Can it use past interactions to inform decisions?
   • Does it track progress toward goals?

3. Tool Integration

   • Can it choose tools dynamically?
   • Does it understand tool capabilities?
   • Can it combine tools in novel ways?

4. Goal Orientation

   • Does it understand and work toward specific objectives?
   • Can it recognize when goals are achieved?
   • Can it adjust goals based on new information?