How I Build Agentic Systems
From local stacks for rapid prototyping to enterprise-scale Microsoft Azure AI Foundry deployments with ISO 27001-aligned governance — the tooling, controls, and principles are the same. The rigour scales.
Local & SME Stack
- ▸Hermes skills and coding agents for orchestration
- ▸n8n for workflow automation and integrations
- ▸Local and hosted LLMs (Ollama, vLLM, OpenAI API) for routing, summarisation, and execution
- ▸NetBox, Ansible, and scheduled jobs for infrastructure control
- ▸Prometheus, Grafana, and centralised logs for observability
Enterprise Stack — Microsoft Azure AI Foundry
- ▸Azure AI Foundry — unified agent development, model catalog, prompt flow, and deployment orchestration for enterprise-grade AI workloads
- ▸Copilot Studio — low-code agent authoring with Microsoft 365 integration for internal business processes
- ▸Semantic Kernel — AI orchestration with enterprise-grade memory, plugin architecture, and responsible AI filters
- ▸Azure AI Search — RAG at scale with vector + hybrid search, integrated data sources, and semantic ranking
- ▸Entra ID — role-based access control, managed identities, and conditional access for agent endpoints
- ▸Azure Monitor & Application Insights — enterprise observability with log analytics, alerts, and dashboarding for agent health and usage
- ▸Azure DevOps / GitHub Enterprise — CI/CD pipelines with environment gating, approval gates, and audit-ready release management
- ▸Azure Policy & Blueprints — compliance guardrails for data residency, encryption, and network isolation
Guardrails & Governance Framework
Every agent system — whether for a two-person lab or a regulated enterprise — is built on the same control foundation. The difference is the rigour of enforcement, not the principle.
Operational Guardrails
- ▸Dry-run first for every risky action — proposed changes rendered as diffs before any apply step
- ▸Tiered approval for destructive changes: read-only reconnaissance, non-destructive checks, gated changes
- ▸Clear audit trails and timestamped outputs for every agent action
- ▸Verification after change — health checks and rollback orchestration built into every deployment
- ▸Monitoring for both agent health and business impact with alerting SLAs
ISO 27001-Aligned Controls
- ▸A.8 — Asset management: Agent systems inventoried, classified, and owned. Data lineage tracked through every processing step.
- ▸A.9 — Access control: Entra ID RBAC mapped to agent roles. Principle of least privilege enforced at the service principal and API level.
- ▸A.12 — Operations security: Change management, capacity planning, and malware defence baked into the agent lifecycle. Runbooks for every failure mode.
- ▸A.14 — System acquisition: Security requirements defined before any agent is deployed. Vendor assessment for third-party model providers.
- ▸A.16 — Incident management: Agent misbehaviour is a reportable security event. Escalation paths, containment playbooks, and post-incident reviews defined.
- ▸A.18 — Compliance: Regulatory mapping for AI systems. Evidence collection for audits. Automated control testing where feasible.
Agentic Governance Framework
A structured approach to governing agent systems across their lifecycle — from design to retirement — aligned with ISO 27001 and emerging AI regulation.
Risk Assessment
Every agent begins with a structured risk assessment: what data does it touch, what decisions does it influence, what happens when it fails. Controls are mapped before any code is written.
Development Controls
Change management, separation of duties, and audit trails from the first commit. CI/CD pipelines enforce policy-as-code checks before any agent reaches production.
Runtime Oversight
Continuous monitoring, drift detection, and automated escalation. Every agent action is logged immutably. Human-in-the-loop for any action above defined risk thresholds.
Design Principles
The philosophy that applies whether you run a single-agent prototype on a laptop or a multi-agent enterprise deployment in Azure.
Operational software, not demos
Narrow responsibilities, observable outputs, explicit rollback paths, and enough structure that the system is safe to run repeatedly in production. Every agent has a defined blast radius and a documented failure mode.
Compliance by design
Security and governance controls are not bolted on after deployment. They are defined in the architecture phase and enforced through the agent lifecycle — from data classification to access control to audit logging.
Vendor-neutral architecture
I design systems that can run on local hardware, open-source stacks, Azure AI Foundry, or a combination. The control framework stays the same; the underlying inference and orchestration are interchangeable.
Audit-ready by default
Immutable logs, timestamped decisions, and evidence-collection endpoints are not an afterthought. Every agent produces a verifiable trail of what it was asked, what it decided, and what it did — from day zero.
Need agentic systems that scale with your compliance requirements?
Whether you are prototyping with local models or deploying enterprise agents on Azure AI Foundry, I can help design the architecture, controls, and governance framework that fits your risk profile.