Enterprise AI:
Strategy & Execution
Building AI systems that work in regulated, high-stakes environments requires more than technical skill. These articles draw from over a decade of consulting across financial services, global enterprises, and technology firms — covering governance, multi-agent architectures, evaluation frameworks, and trusted AI.
AI System Design for Enterprise: From Data to Reliable Decisions
Follow a travel-expense claim from receipt to finance review, connecting data, AI models, policy checks, secure APIs, evaluation and controlled releases.
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AI Governance: The Practical Guide for Every AI Project
What AI governance means, why every project needs it, and how to put risk-based ownership, testing, oversight, and monitoring into practice—with examples from industry standards.
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Evaluating AI Agents in the Enterprise: From Lab Metrics to Production Trust
A complete framework for evaluating AI agents across six pillars — outcomes, trajectories, reliability, safety, user experience, and operational value — with grader strategies, risk tiers, and development-to-production guidance.
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Loop Engineering: The New Discipline for Autonomous Enterprise AI
Loop engineering is the practice of designing the system that runs, checks, and re-runs your AI agent — covering the four loop types, the four-level stack, and the three hardest problems every enterprise team must solve.
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AgenticOps: Operating AI Agents at Enterprise Scale
The operational discipline for managing autonomous AI agents across their full lifecycle — provisioning, orchestration, observability, governance, drift control, and safe decommissioning.
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Meta-Prompting for Enterprise AI: What It Is and How to Evaluate Prompts
A clear guide to creating and evaluating prompts, with industry examples and useful research principles.
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Enterprise AI Risk Management: The GEN-5 Validation Framework
A five-pillar validation and assurance framework that extends established Model Risk Management principles — SR 11-7, SS1/23, MAS AIRG — across GenAI, RAG, PMAS, and fully Agentic AI systems.
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The Architecture of Controlled Autonomy: Governing Enterprise AI Agents
How to govern autonomous AI agents at enterprise scale — covering agent gateways, identity, behavioral drift detection, least-privilege scoping, and emergency kill switches.
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Reasoning RAG Architecture for Enterprise AI
How Reasoning RAG replaces the linear retrieve-then-generate pipeline with iterative multi-hop retrieval, a structured reasoning engine, trust validation, and fully explainable responses — built for regulated enterprise environments.
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Enterprise AI concepts, stripped of jargon. Watch to grasp the idea, then dive into the articles for technical depth.
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Reasoning RAG Architecture for Enterprise AI
On LinkedIn
External Reading
Additional articles published on LinkedIn covering related enterprise AI topics.
Enterprise AI Performance Optimisation: Framework & Systems
A practical framework for engineering performance in enterprise AI systems — covering latency, throughput, inference efficiency, RAG optimisation, and cost-quality tradeoffs at production scale.
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Agentic vs Prescriptive Multi-Agent Systems: Which One Should You Choose?
A deep dive into when to use autonomous agentic frameworks versus structured prescriptive workflows in enterprise AI deployments.
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The GenAI & Agentic AI Evaluation Stack: From Design to Deployment
How to build a rigorous evaluation stack that covers the full lifecycle of generative and agentic AI systems.
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From Autonomy to Accountability: The TRACE Framework for Governing AI
Introducing TRACE — a practical framework for implementing compliance-ready AI governance in regulated industries.
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How to Actually Quantify Trust in GenAI Systems
Moving beyond intuition: concrete metrics and methods to measure and validate trust in generative AI outputs.
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How to Build Compliance-Ready Multi-Agent AI Automation for AML Alert Investigation
A practical blueprint for designing audit-ready, compliance-first multi-agent AI systems that accelerate AML alert investigation in financial services.
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How to Build Enterprise Intelligence as a Strategic Asset
A strategic framework for transforming enterprise AI from a cost centre into a compounding intelligence asset — covering data foundations, capability layering, and boardroom alignment.
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Why Agent Harness Engineering Matters for Enterprise AI
Why harness engineering — the practice of building structured, repeatable test infrastructure for AI agents — is the missing discipline separating reliable enterprise deployments from brittle, unpredictable ones.
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Context Engineering for Enterprise AI Agents
Why context is the new prompt — a deep dive into how enterprise AI agents reason, retrieve, and act based on the context they are given, and how to engineer that context for reliability and scale.
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