Disabling Generation Stops Customer-Facing AI Hallucinations

Not long ago, I met with senior IT stakeholders at a large company to discuss Parlant for crafting conversational agents that remain under tight governance. The discussion shifted when one participant asked: “Can we disable the generation step in Parlant?” At first, I assumed they misspoke. A generative AI agent without its generative core? It seemed contradictory. But I stopped to reflect, and their logic became clear.

Their teams weren’t exploring proofs of concept. They planned to put agents into production, managing millions of user conversations monthly. Even a 0.01% failure rate risks compliance breaches, legal exposure, or reputational harm. At high volume, “mostly reliable” simply doesn’t work. LLMs deliver impressive results, yet free-form outputs still carry risks: hallucinations, off-brand tone, and drifting from facts.

That question wasn’t absurd. It hit a critical point about what enterprise-grade AI really demands.

Later, I considered this further. The company had full-time Conversation Designers—professionals who map out agent behaviors, draft scripts, ensure brand consistency and legal compliance, and create engaging dialogue. They weren’t asking to turn off generation out of fear; they wanted granular control over every word.

This insight shifted my view on “generative AI agents.” They aren’t defined by token-by-token outputs from an LLM. They’re judged on adaptability and context sensitivity. Whether replies flow from a model or a vetted repository is secondary. What matters is that every response fits policy, aligns with the customer’s needs, and maintains the right tone.

Organizations that rely on Conversation Designers can do more than reduce hallucinations. They can eliminate them. These teams deliver intentional, clear, and on-brand messaging that foundation models often misstep on in real-world interactions.

I realized Parlant should embed this designer-driven approach natively. The platform already emphasizes design governance and authority. It made sense to empower designers to own the message rather than patching outputs afterward.

That led to Utterance Templates in Parlant. With these, teams define flexible response patterns that adapt to context and remain fully approved, version-controlled, and auditable. This framework keeps the fluidity of generative AI yet nails precision.

Under the covers, Utterance Templates follow a simple three-step routine:

• The agent crafts a draft message based on context (conversation history, policy rules, external tool data)
• The system selects the best-matching template from the library
• The engine populates the chosen Jinja2 template with live variables and delivers the final reply

Enterprises saw how this fit into Parlant’s hybrid model: developers build reliable agent logic, and Conversation Designers set the tone and policy rules. The team agreed it would work.

This vision shifts conversational AI away from sidelining humans. It equips experts to guide and refine every line of dialogue.

Disabling or tightly governing generation in customer-facing AI isn’t counterintuitive. It may be the most sensible approach.