AI & Intelligent Systems
Where AI Fits Into the Work
This isn't a product list. It's four concrete angles on how AI gets applied inside real engineering work, and where its limits are.
AI Agents
Agent structure, tools, workflow, execution permissions, limits, and oversight.
- Task decomposition and tool-calling
- Multi-agent orchestration
- Human-in-the-loop checkpoints for consequential actions
- Execution reliability and failure containment
AI-driven Engineering
Integrating AI into the development lifecycle: code generation, testing, review, and troubleshooting.
- Agent-assisted development with Claude Code
- AI-assisted code review and debugging
- Knowing where AI output still needs human verification
Local AI
Self-hosting, inference, GPU resources, data privacy, and model selection.
- Self-hosted model deployment
- GPU inference and resource constraints
- Local vs. cloud trade-offs: cost, latency, privacy
AI Systems Design
Integrating AI into application architecture, APIs, automation, and human approval flows.
- API and tool integration patterns
- Structured outputs and context management
- Designing approval gates around AI-executed actions
Illustrative
Conceptual Architecture
Generic diagrams illustrating how these systems are typically structured, not a specific client or internal architecture.
Agent Workflow
Conceptual ArchitectureTask input
Decomposition
Tool calling
Human checkpoint
Execution
LLM Integration Architecture
Conceptual ArchitectureApplication
Context layer
Model (local or hosted)
Tool / API layer
Response handling