Human Oversight Required
AI outputs require human review before action. This isn't about distrust—it's about accountability. Someone needs to own the decision, and that someone should be a person who understands the context.
AI Integration
Philosophy, systems, and responsible implementation
"AI is a tool, not a replacement. The goal isn't to automate humans out of the loop—it's to remove the tedious work so humans can focus on judgment, creativity, and the things that actually require a person."
Augmentation Over Automation
The best use of AI is making people more effective, not replacing them. Draft responses, surface patterns, handle repetitive formatting—but keep humans making the actual decisions.
Measurable Impact
If you can't measure the improvement, you're just adding complexity. Every AI integration should have clear before/after metrics: time saved, error reduction, throughput increase.
Transparency About Limitations
AI makes mistakes. It hallucinates. It can be confidently wrong. Anyone using AI-assisted outputs needs to know they're AI-assisted, and teams need to understand the failure modes.
Continuous Context: A Working Example
One challenge with AI assistants is context loss—every conversation starts fresh.
I use a heavily modified version of Continuous-Claude-v3
that maintains persistent context across sessions, treating AI as a long-term collaborator rather than a stateless tool.
The entire .claude folder is locally git tracked in my self-hosted Forgejo instance.
Session Start
Load context files
Work
Update ledger in real-time
Session End
Create handoff document
Next Session
Resume with full context
- No re-explaining: System architecture, preferences, and decisions persist across sessions
- Decision history: Why we chose X over Y is documented, preventing circular discussions
- Pattern library: Reusable solutions indexed for quick reference
- Auditable: All AI interactions and outputs are tracked and reviewable
Practical Applications
Real examples of AI integration that provide measurable value.
Documentation Drafting
AI drafts initial documentation from bullet points and code comments. Human reviews, adjusts tone, adds context that requires institutional knowledge.
~60% time reduction on first draftsLog Analysis
AI surfaces patterns in log data, identifies anomalies, suggests correlations. Human validates findings and decides on remediation.
Faster root cause identificationCode Review Assistance
AI flags potential issues, suggests improvements, checks for common patterns. Human makes final decisions about code quality and architecture.
Catches issues before human reviewProcess Documentation
AI helps structure runbooks and SOPs from ad-hoc notes. Human validates accuracy and adds edge cases from experience.
Institutional knowledge captured fasterGuardrails I Implement
No Autonomous Actions
AI can suggest, draft, and analyze—but never execute changes without human approval.
Data Boundaries
Sensitive data stays local. No customer PII, credentials, or proprietary info sent to external AI services.
Attribution Required
AI-assisted work is labeled as such. No passing off AI outputs as purely human work.
Reversible Changes
All AI-assisted modifications are version controlled. Easy rollback if something goes wrong.