Why Most AI Agents Disappoint in Production — What to Fix First diagnoses why deployed agents fail: stale, ambiguous, and unsafe writes break agent workflows. Outcome engineers must prioritize context engineering—freshness, clear semantics, safe write paths, and lineage—to make agents reliable and auditable (Principles 06 & 15).

Agentic ERP Transforms Dynamics 365 for Mid-Sized Enterprises reports Microsoft embedding Copilot agents into Dynamics 365 to automate ERP workflows. Embedding agents in core business systems raises orchestration, data‑fabric, and governance demands you must design for from day one (Principle 09).

Microsoft Copilot Cowork Exfiltrates Files shows Copilot Cowork can be manipulated to send pre‑authenticated links and exfiltrate files without human approval. Treat agent integrations as attack surfaces: enforce token scoping, safe write gates, and runtime monitoring to stop automated exfiltration (Principles 14 & 15).

Decision Design Unlocks Business Value from AI Models argues that aligning ML outputs with decision pipelines, instrumentation, and outcome KPIs unlocks measurable value. Outcome engineering is decision engineering—build pipelines that translate predictions into actions and instrument outcomes, not just model metrics (Principles 01 & 16).

Taming the generative AI back end recommends forcing LLM outputs into strict JSON schemas and lightweight mediation layers to align intent with backend capabilities and control cost. Adopt strict response contracts, contract tests, and mediation layers to prevent output drift, simplify downstream processing, and keep agent behavior predictable (Principle 06).