Yes! Agentic AI can be Hallucination-Proof. Here’s how.
In our earlier piece, Who Can You Trust?, we argued that trust, not fluff, wins in real-life production AI applications. This article answers the many follow-ups we received challenging our claim of “100% hallucination-free”.
The uncomfortable truth (and the simple fix)
At today’s state of the art, nothing an AI model says or parses is guaranteed to be hallucination-free. That’s fine. Production systems don’t need “perfect AI” - they need perfect controls.
The pattern that works is hybrid agentic design:
- AI where errors are cheap: STT/TTS, natural language, small-talk, intent detection, tone.
- Deterministic where stakes are high: authentication, schema validation, read-backs, business-rule enforcement, tool invocations, payments, audit trails.
With this split, you may still see the occasional intent miss (the assistant answers with helpful info instead of executing), but no wrong action can reach your core systems.
What went wrong (twice): drive-thru voice AI
- McDonald’s ends its IBM drive-thru pilot (2024). After about two years across 100+ stores, the pilot was discontinued, with “mixed results” and widely reported accuracy issues. McDonald’s said it remains open to voice AI in the future, but the tech was removed from test sites. AP News
- Taco Bell rethinks where voice AI fits (2025). Following a large rollout, leadership publicly acknowledged uneven performance and is reassessing how and where to deploy voice AI, particularly under peak-hour pressure and customer trolling. The Wall Street Journal
Related market signal: in Jan 2025, the SEC issued a cease-and-desist order against Presto Automation for misleading statements about its voice AI product. Hype isn’t a substitute for controls. SEC
Why these efforts stumble: open-ended language + noisy channels + LLM hallucinations create non-deterministic outcomes. LLM confidence does not equal correctness, and a single hallucination or mis-parse can cascade into a terrible user experience and reputational damage
What works: The Curacao hybrid approach with JSFE
6-week rollout, live today.
- Customer payments (financial, irreversible).
- Authenticated account inquiries (balances, payment history, payoff amounts).
Observed outcome:
- 100% reliable commits on high-stakes steps.
- The only “failures” are benign intent misses. Never wrong charges or corrupted state.
Why: The moment intent is detected, the conversation is no longer between the user and the AI. It’s between the user and a deterministic JSFE flow:
- The flow SAYS exactly what to ask, GETS typed/validated inputs, BRANCHES predictably, and CALLS predefined tools with bounded, schema-checked arguments.
- At any time, the user can cancel or request a live agent.
- There are no “edge cases” in transactional logic: a flow either gathers all required, valid inputs and commits with 100% accuracy, or it deterministically cancels.
- Tool calls can fail (invalid inputs, endpoint issues), but always deterministically; flows own the recovery/route-to-human path. No failure can ever be caused by a hallucination.
- The AI has zero authority to transact. It only narrates outcomes after the flow completes.
Want to see the engine behind this design? JSFE (JavaScript Flow Engine) is open source: https://github.com/ronpinkas/jsfe.
Why many big-brand assistants still avoid payments/balances
Look at Best Buy’s public Gen-AI assistant scope: troubleshooting, delivery/scheduling changes, membership management, agent assist - valuable but notably not leading with payments/balances. That’s consistent with the risk calculus we describe. The hybrid pattern is how you expand into high-stakes territory safely. Best Buy Corporate News and Information
The “Hallucination-Proof” blueprint
Design the split
- AI zone: STT/TTS, small-talk, intent detection, paraphrase.
- Deterministic zone (JSFE-style): identity checks, schemas/enums, read-back from canonical state, explicit confirmation, tool contracts, logging, auditing.
Non-negotiables
- Schema locks. If it touches money, inventory, or personally identifiable information (PII), values must pass types, enums, ranges, and cross-field rules.
- Read-back from state, not the model. Confirm the exact values the system will use.
- Explicit “Yes” on a signed state. No commit without user confirmation bound to a state hash.
- Tool contracts only. Only flows can execute, LLMs are excluded.
- Cancel/Agent-at-any-time. Flows own graceful exits; there’s no undefined path.
- Full observability. Per-step logs and audit trails.
What to measure
- Commit Accuracy (target ≥ 99.9%)
- Handoff Rate (to human before side-effects)
- Intent Miss Rate (benign “answered with info”)
Executive takeaway
Pure “end-to-end AI” dazzles in demos but buckles in real-world applications (see McDonald’s & Taco Bell).
At Curacao, the production rollout shows the alternative: AI for Capture; deterministic flows for Confirm/Commit, producing 100% reliable transactions even as language remains non-deterministic.
If it touches money, inventory, or personally identifiable information (PII), make it deterministic. Let AI assist, but never transact.
Sources
- Who Can You Trust? (InstantAIguru blog)
- AP News — McDonald’s is ending its test run of AI-powered drive-thrus with IBM
- The Wall Street Journal — Taco Bell Rethinks Future of Voice AI at the Drive-Through
- SEC — Press page for the Presto Automation order
- Best Buy Corporate News — How Best Buy is using generative AI to create better customer support
As always, your feedback is highly appreciated.
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