How to Avoid AI-Washing

AI-washing is the practice of marketing a rule-based system as an "AI agent" by adding a thin LLM layer. Here's how to build genuine intelligence into your agents.

What is AI-Washing?

AI-washing occurs when:

1. LLM is decorative: Used only for text generation, not decision-making
2. Hardcoded logic: All behavior is predetermined
3. No autonomy: Agent can't adapt to new situations
4. Zero learning: No memory or context retention

Example of AI-Washed Code

def process_request(user_input):
    # LLM only used for pretty responses
    if "weather" in user_input:
        data = get_weather()
        return llm.generate(f"Format this: {data}")
    elif "news" in user_input:
        data = get_news()
        return llm.generate(f"Summarize: {data}")
    # ... more if-else chains

Problem: The LLM isn't making decisions—it's just a formatter.

Building Genuine AI Agents

1. LLM-Driven Decision Making

Let the LLM decide what to do:

def process_request(user_input):
    # LLM decides which tools to use
    decision = llm.invoke([
        SystemMessage("You have access to: weather, news, search"),
        HumanMessage(user_input)
    ], tools=[weather_tool, news_tool, search_tool])

    # Agent executes based on LLM's decision
    return execute_tool_calls(decision.tool_calls)

Better: The LLM is making strategic decisions.

2. Add Memory

Agents without memory can't learn or maintain context:

class Agent:
    def __init__(self):
        self.memory = ConversationBufferMemory()
        self.vector_store = ChromaDB()

    def process(self, query):
        # Retrieve relevant context
        context = self.vector_store.similarity_search(query)

        # Include conversation history
        history = self.memory.load_memory_variables()

        # LLM uses both for decision-making
        response = llm.invoke([
            SystemMessage(f"Context: {context}"),
            *history["messages"],
            HumanMessage(query)
        ])

        self.memory.save_context(query, response)
        return response

3. Implement Tool Orchestration

Real agents can chain tools dynamically:

# AI-Washed: Fixed tool sequence
result = tool1() → tool2(result) → tool3(result)

# Genuine: LLM decides tool sequence
agent = create_react_agent(llm, tools)
result = agent.invoke({"input": query})

4. Add Safety and Error Recovery

Production agents need robustness:

async def safe_llm_call(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            # Input validation
            validated = validate_and_sanitize(prompt)

            # Rate limiting
            await rate_limiter.acquire()

            # LLM call with timeout
            response = await llm.ainvoke(validated, timeout=30)

            # Output moderation
            return moderate_content(response)

        except RateLimitError:
            await exponential_backoff(attempt)
        except Exception as e:
            logger.error(f"LLM call failed: {e}")
            if attempt == max_retries - 1:
                return fallback_response()

The AgentIQ Score Difference

How these patterns affect your intelligence score:

Red Flags in Your Code

Watch out for these anti-patterns:

1. The Keyword Switch

if "weather" in query:
    # ...
elif "news" in query:
    # ...

Fix: Let the LLM parse intent.

2. The Template Machine

response = llm.generate(template.format(data))

Fix: Let the LLM reason about what to say.

3. The Stateless Bot

def handle_message(message):
    return llm.generate(message)  # No context!

Fix: Add memory and context management.

Verification Checklist

Use this checklist to ensure you're building a genuine AI agent:

• LLM makes decisions, not just formats text
• Agent has memory/context retention
• Tool selection is dynamic, not hardcoded
• Error handling around AI calls
• Safety guardrails in place
• Behavior adapts to context
• Planning/reasoning chains present

Test Your Agent

Ready to verify you're not AI-washing?

Analyze your agent on AgentIQ and get objective, evidence-based scores across all seven intelligence dimensions.

Conclusion

Building genuine AI agents requires thoughtful architecture, not just LLM API calls. By following these patterns, you can create agents that are truly intelligent, adaptive, and production-ready.

Remember: If you can replace your LLM with a template and get the same behavior, you're AI-washing.

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