Why non-deterministic AI agents are the ultimate doom for enterprises (blog.inxm.ai)

A provocative opinion piece warns that non‑deterministic AI agents — the probabilistic LLM-driven “genies” that produce different outputs for the same prompt — are a systemic risk for enterprises. The author argues that LLMs inherently produce probability distributions over tokens (via self‑attention, embeddings and next‑token training), and sampling, quantization, context limits and lack of grounding introduce instability. When agents chain many steps, even high per‑step accuracy decays multiplicatively (e.g., P(total) = 0.99^n → ~90% for 10 steps, ~37% for 100), so unattended agentic workflows can yield unpredictable, costly failures, data corruption, or embarrassing actions. Technically, the piece calls for hybrid architectures and rigorous orchestration: keep stochastic LLMs for high‑level planning and creativity, but delegate critical, verifiable tasks to deterministic code, rules, or validated components; add feedback loops, grounding, task decomposition, strict logging, immutable workflows and audit trails. The author pitches an orchestration platform (INXM) that claims to bridge LLM fluency and enterprise reliability by enforcing reproducibility, accountability and monitoring. For the AI/ML community, the takeaway is clear: scaling agentic automation requires composability with deterministic safeguards, robust validation and measurable reliability, not blind trust in black boxes.