Transitioning from a standard chatbot to an agentic system requires a fundamental shift in how you manage control flow. A chatbot functions as a request-response pair, whereas an agent operates as a runtime loop that perceives, reasons, acts, and observes.
This architectural change introduces stochastic behavior into your system. For production environments, the engineering challenge is not eliminating this non-determinism, but containing it within defined boundaries to maintain system reliability.
In short
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Treat agentic systems as runtimes rather than functions to account for autonomous tool selection and multi-turn reasoning loops.
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Contain stochasticity by applying specific design patterns that enforce boundaries between the agent's reasoning brain and your deterministic control logic.
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Evaluate whether a deterministic rule engine or state machine can solve your problem before introducing the complexity of an LLM-based agent.
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Start with core architectural patterns and only adopt specialized patterns when your specific use case requires additional reliability guardrails.
The Shift to Agentic Runtimes
An agent becomes truly agentic when it moves beyond single-turn interactions into a continuous loop. It autonomously decides which tools to call, the sequence of those calls, and when to request human intervention or terminate the process.
This autonomy is a design choice that inherently introduces non-determinism. Because the agent can handle ambiguous instructions, its path through your code is not fixed. Architects must treat this as a core system property rather than a bug to be patched.
Architectural Containment
The most effective way to manage agentic systems is to isolate the stochastic brain from the deterministic infrastructure. By using design patterns that enforce these boundaries, you ensure that the agent's reasoning capabilities do not compromise the stability of your underlying services.
Before deploying an agent, verify if a simpler, deterministic approach—such as a state machine or a rule-based engine—can achieve the same outcome. If the problem space is well-defined, deterministic systems are significantly cheaper to maintain and easier to debug than LLM-based agents.
Source
Tame Your Agents: 10 Design Patterns for Reliable Agentic AI
https://community.sap.com/t5/technology-blog-posts-by-sap/tame-your-agents-10-design-patterns-for-reliable-agentic-ai/ba-p/14424874
