ML & Decision Intelligence

• Classifiers for routing, triage, intent and safety pre-screening
• Rerankers and scorers supporting retrieval
• Anomaly detection and forecasting feeding agent decisions
• Optimisation and constraint solving where the answer is a plan, not prose
• Feature store, training pipelines and drift monitoring for these models

Accountable owner

Data Science / Machine Learning

• Head of Data Science — accountable owner
• Data scientist — model development and validation
• MLOps engineer — deployment, monitoring, retraining
• Decision scientist — where optimisation beats generation
• Model risk reviewer in regulated sectors

Ownership and phase are architectural recommendations, not assessed results.

A scale-phase optimisation: introduce when volume makes a measured LLM path too slow, too costly or too imprecise for a repeated decision. Do not create a parallel ML organisation — reuse the same registry, evaluation and observability harnesses.

Signals to watch

• Accuracy or AUC by model against its baseline
• Feature and prediction drift
• Share of decisions served by classical models versus the LLM path
• Cost and latency delta against the LLM alternative

Optimizers can propose candidates and specialist models can change with validation. Neither role grants promotion authority.

Failure modes

1. A language model used where a small classifier is faster, cheaper and more accurate
2. Drift unmonitored because these models sit outside the agent telemetry
3. Two disconnected lifecycles: one for models, one for agents
4. Existing model-risk governance not applied to models now invoked by agents

Existing tool and standards snapshot · approximately mid-2026. Examples, not required dependencies; verify current versions before adoption.

This harness is easy to forget in an agent-centric design and quick to pay for itself. The most reliable cost reduction available is usually not a cheaper model tier — it is not calling a language model at all.