Agentic Thoughts
Practical explorations of architecture models, policy decisions, requirements and implementation guidance for teams building with AI coding tools and agents.
About this series: Agentic Thoughts are AI-assisted explorations of evolving product ideas and real-world applications. They are reviewed for direction and clarity, but do not necessarily describe committed features or formal advice.
Where to begin
AI coding tool policy management connects architecture models and security controls to requirements and implementation guidance for coding assistants.
Read the thought ↗ Start hereHow architecture models can become policy-backed AI coding requirements through components, controls, specifications and evaluation criteria.
Read the thought ↗ Start hereLearn how architecture models, policy controls and specification blocks can produce focused steering files for AI coding tools such as Kiro, Cursor and Copilot.
Read the thought ↗Explore how architecture models and policy controls can provide project-specific context for AWS Kiro steering files without replacing requirements or organisational governance.
Learn how to turn architecture and security controls into GitLab Duo agent-config.yml settings for flow execution and network access.
Understand the difference between AI coding rules, steering files, Cursor rules, Copilot instructions and repository guidance.
A practical approach to governing GitHub Copilot, Cursor and other AI coding assistants with architecture context and reviewable controls.
AI coding tool policy management connects architecture models and security controls to requirements and implementation guidance for coding assistants.
Learn how centralised policy management keeps AI coding guidance consistent, architecture-aware and traceable across repositories and teams.
Use this AI coding tool policy checklist to cover architecture, data access, permissions, security controls, review evidence and evaluation.
How teams can govern GitHub Copilot, Cursor and other AI coding assistants with architecture context, security constraints and review expectations.
Understand the difference between AI coding rules, steering files, Cursor rules, Copilot instructions and repository guidance.
How centralised policy management keeps AI coding guidance connected to architecture instead of scattered across prompts, repositories and review checklists.
Explore how architecture models and policy controls can provide project-specific context for AWS Kiro steering files without replacing requirements or organisational governance.
Learn how architecture models, policy controls and specification blocks can produce focused steering files for AI coding tools such as Kiro, Cursor and Copilot.
How architecture models can become policy-backed AI coding requirements through components, controls, specifications and evaluation criteria.
Compare the OWASP Secure-by-Design Framework with system-specific architecture requirements for AI-assisted software development.
Secure-by-design architecture explained: how security analysis, controls and requirements can influence architecture before implementation begins.
A practical structure for secure-by-design policies that connect architecture, security objectives, controls, AI coding guidance and evaluation.
How to create architecture-aware rules for GitHub Copilot and Cursor from system components, security objectives, policies and implementation constraints.
How architecture and security constraints can guide AI coding tools toward approved boundaries, controls and implementation patterns.
How to turn an architecture diagram into requirements and implementation guidance that AI coding tools can use and teams can review.
See what a secure-by-design policy for AI-assisted development can include, from architecture objectives and controls to testable implementation requirements.
How system-specific security requirements help teams guide and evaluate AI-generated code beyond generic secure coding prompts.
Why AI coding agents need both architecture-derived requirements and persistent steering instructions to build software within the intended security boundaries.
What reported AI-agent containment incidents suggest about sandbox design, layered controls and evaluating agent access before production use.
How to define and review network egress, approved services, package sources and external actions for AI agents.
How to test AI-agent containment with explicit boundaries, failure cases, monitoring evidence and repeatable evaluation criteria.
A practical approach to threat modelling AI agents, sandbox boundaries, network paths, credentials and connected tools before deployment.
How to apply least privilege to AI agents by modelling tools, credentials, data stores, actions and approval boundaries.
A practical least-privilege framework for deciding which files, tools, credentials, data and actions an AI agent should access.
How teams can use architecture models and policy controls to define what an AI agent sandbox must isolate, monitor and prevent.
Why secure AI-agent design needs layered controls for identity, tools, network access, monitoring and recovery in addition to isolation.
Learn how to turn architecture and security controls into GitLab Duo agent-config.yml settings for flow execution and network access.