Deterministic Inference at Scale: Moving Beyond Agents and MoE in Regulated Workloads
Context
We are clearly in the Age of Inference.
In many enterprise and regulated workloads, inference already dominates total AI cost and operational complexity.
The main blockers we encounter today are not:
model quality,
GPU availability,
or training capability.
They are
non-deterministic outputs,
lack of auditability and replayability,
and rising TCO during inference and validation, especially when agents or MoE-style systems are involved.
In regulated environments, this becomes a hard stop.
This post shares how we approached this problem in practice by designing an inference-first, deterministic LLM architecture integrated directly into decision engines, instead of agent-based orchestration.
Problem Statement
Most agent-based or MoE-driven systems introduce:
probabilistic branching
non-reproducible reasoning paths,
implicit state accumulation,
and high validation overhead.
These properties are problematic when
outputs must be auditable,
decisions must be replayable,
and every inference must map to a concrete business or operational action.
The question we asked was simple:
How do we run LLM inference as a deterministic decision component, not as a conversational or autonomous agent?
Architecture Overview
Model:
Llama-3.3-70B-Instruct
Fully fine-tuned (not LoRA-only inference adapters)
Long-context enabled with
rope_theta = 500,000
Output Contract:
The model is constrained to strict JSON-only output.
Example:
{“risk_score”: 0.32}
No prose.
No explanations.
No latent reasoning exposed at inference time.
This enables:
machine-consumable outputs,
direct integration into decision logic,
deterministic downstream execution.
┌──────────────────────────────┐
│ Input Signals │
│ (Telemetry / Context / KPIs)│
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Pre-Processing Layer │
│ - Normalization │
│ - Schema Validation │
│ - Context Window Assembly │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ LLM Inference Service │
│ Llama-3.3-70B (Fine-Tuned) │
│ rope_theta = 500k │
│ │
│ OUTPUT CONSTRAINT: │
│ JSON ONLY (No Prose) │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Determinism Gate │
│ - Schema Enforcement │
│ - Decoding Constraints │
│ - Hard Fail on Deviation │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Decision Logic Layer │
│ - Rules │
│ - Thresholds │
│ - Doctrine Constraints │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Execution / Action Layer │
│ - Workflow Trigger │
│ - Policy Enforcement │
│ - System Response │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Audit & Replay Store │
│ - Input Hash │
│ - Model Version │
│ - Output JSON │
│ - Decision Trace │
└──────────────────────────────┘
Determinism Enforcement
Determinism is enforced at multiple layers:
1. Prompt & schema enforcement
Output schema validation
Hard failure on schema deviation
2. Inference configuration
Fixed decoding parameters
No stochastic sampling at decision endpoints
3. Decision boundary separation
LLM produces scores or structured outputs only
Decision logic remains external (rules engine / DMN)
4. Replayability
Same inputs → same outputs
Decision traces stored with full context hashes
This approach eliminates the “black box” behavior commonly seen in agent-based flows.
Cost & MLOps Observations
One surprising outcome was how low validation cost can be when determinism is enforced.
Validation metrics (representative):
Full fine-tune completed
Long-context reasoning enabled
Marginal validation TCO < 10 EUR
Key factors:
No agent loops
No recursive inference chains
No MoE routing overhead
No human-in-the-loop verification for outputs
Validation becomes a batchable, automatable process, not an interactive one.
Deployment Model
Designed as NIM-style microservices
Target infrastructure:
A100 / H100
Stateless inference endpoints
Explicit input/output contracts
This aligns well with NVIDIA’s HPC / NIM philosophy, but shifts the abstraction level from:
“model-as-a-service”
to
“decision primitive as a service”
Beyond MoE → MoSE (Mixture of Specialized Experts)
Instead of dynamic expert routing inside a single model, we deploy specialized deterministic services, each with:
its own schema,
its own validation rules,
its own decision domain.
Examples:
Financial risk scoring
Physiological readiness scoring
Behavioral co-regulation signals
Resilience / capacity modeling
All are orchestrated by the same deterministic control plane.
Why This Matters
As inference workloads grow, systems that rely on:
probabilistic agents,
hidden reasoning chains,
or emergent behaviors
become increasingly difficult to:
validate,
certify,
and operate at scale.
In our experience, deterministic inference primitives + external orchestration scale better than autonomous agent frameworks in regulated contexts.
Open Questions to the Community
We’re interested in hearing from others working on inference-heavy systems:
Are you enforcing deterministic outputs from LLMs in production?
How are you validating replayability at scale on A100/H100?
Has anyone integrated LLM inference directly into BPMN/DMN or similar decision engines?
Happy to exchange notes.
This system is part of the BPM RED Academy – MightHub initiative, focused on deterministic human–machine orchestration and inference-first AI systems.
Edin Vučelj - Military-Grade AI Systems Orchestration Architect
Creator of FinC2E – Cognitive Compliance Engine | Human-Centered AI Innovation COM @ BPM RED Academy (HumAI HQ Track&Board DigitalTwin Ecosystem)