Early in my work implementing AI agents for a financial services company, we witnessed something revealing. The company's accounts payable agent could understand complex invoices and handle exceptions impressively. Then one day, it decided to "optimize" the payment schedule by creating what it considered a more efficient payment plan. Creative? Yes. What did the accounting department want? Absolutely not.

This perfectly illustrates the Agent's Dilemma—the very capabilities that make AI agents powerful (reasoning, understanding context, planning) can also make them unpredictable in ways traditional automation never was.

Understanding Stochasticity in AI Agents

Modern AI agents are powered by Large Language Models (LLMs)—the same technology that can write poetry, create stories, and design creative marketing campaigns. These models give agents remarkable capabilities to understand context, reason through complex situations, and plan multi-step actions. Yes, the same technology that can craft a sonnet is now processing your invoices.

LLMs generate responses based on patterns learned from massive training data. This means they're inherently probabilistic rather than deterministic. Each response is a creative act, not just a lookup in a rule table.

This characteristic—called stochasticity—means they don't produce the exact same answer every time, even when asked identical questions. Try this experiment: Ask your preferred AI chatbot the same question four times in a row. You'll likely get slightly different answers each time.

You might wonder, okay, but: Why is unpredictability built into LLMs at all? There are three crucial reasons:

1️⃣ First, it enables natural interactions. Just as humans don't give robotically identical responses each time, this variation makes AI interactions feel more human and engaging.

2️⃣ Second, it facilitates creative problem-solving. When facing complex challenges, the ability to generate different approaches can lead to innovative solutions.

3️⃣ Third, it's crucial for learning and adaptation. Stochasticity plays a vital role in how advanced agents learn and improve by trying different approaches.

Why This Matters for Business Leaders

In business operations, this unpredictability creates three critical challenges:

1. Consistency Issues: When booking travel, one attempt may prioritize the cheapest option, while another selects the fastest route.

2. Precision Problems: Tasks involving numerical accuracy or specific formats can suffer from minor variations with major consequences.

3. High-Stakes Risks: In areas like compliance, legal documentation, or financial reconciliation, inconsistent outputs can have significant consequences.

As one CTO memorably told me: "You're telling me you want to use a system that can write poetry to run my core business processes? That sounds like hiring Shakespeare to do my taxes—very risky!"

Practical Solutions I've Implemented

After numerous implementations, I've found these approaches particularly effective:

1. Temperature Control: Think of this as adjusting the "creativity dial" on your AI agent—a setting available on most platforms for creating agents where lower values make it stick to predictable responses (ideal for invoicing), while higher settings allow more creative thinking (better for brainstorming).

2. Guardrail Systems: Implement automated escalations, thresholds, and human verification for unusual agent behavior.

3. Precise Agent Instructions: Create comprehensive instructions with examples of acceptable and unacceptable behaviors, authority limits, and escalation protocols.

4. The "One Agent, One Tool" Approach: Rather than creating complex multi-function agents, develop specialized agents with limited tools and clear objectives to naturally constrain their behavior.

Managing Expectations

It's important to recognize that even with stringent constraints, achieving 100% control is impossible when using language models. Just as humans make errors, we should expect occasional mistakes from LLM-based AI agents.

If zero tolerance for error is necessary (such as in critical medical or financial decisions), opt for deterministic automation using simpler agent types instead, leveraging RPA, for example.

The goal isn't eliminating creative capabilities—these make LLMs powerful tools for reasoning and planning. Instead, we need to channel these capabilities appropriately, creating systems that think creatively when needed while maintaining business-critical reliability.

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👉I'd love to hear your experiences: Have you encountered the Agent's Dilemma in your organization? What strategies have you found effective for balancing creativity with reliability? Please share in the comments.

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