AI Agents
Notes on agent orchestration, trust, and the patterns that emerge.
Software now has agency and personality.
AI as a capable colleague that understands context, takes initiative within boundaries, and can be trusted with increasing autonomy. The interesting work is in the architecture around that trust.
The Evolution
Explore
What Are AI Agents?
Start HereStart here: the shift from AI tools to AI colleagues.
- • Key concepts and definitions
- • Why 2026 is the year
- • The real bottleneck: trust
Personal Systems
Level 1How I orchestrate AI agents for my own productivity.
- • Claude Code orchestration
- • MCP-powered workflows
- • Trust patterns in practice
Learning Path
Level 2For curious professionals ready to build their own agent systems.
- • Agent fundamentals
- • Progressive trust building
- • First agent projects
Patterns Library
ReferenceReusable patterns for multi-agent orchestration.
- • Multi-agent orchestration
- • Human-in-the-loop gates
- • Scope lock systems
Trust Engineering
AdvancedThe key differentiator: calibrating how much to trust your agents.
- • Trust levels framework
- • Deliberative refinement
- • When trust fails
The Adoption Curve
The AI Adoption Curve
SeriesEvery layer of the AI stack is being democratized faster than it's being secured. Tracking the pattern from ChatGPT to DeepSeek to OpenClaw.
- • OpenClaw Risk Assessment (published)
- • MCP Trust Assessment (published)
Specializations
Voice Agents
ArchivedNotes from hackathon-era voice work — conversational agents, voice synthesis, and audio generation. 2nd place at the ElevenLabs Worldwide Hackathon.
- • Conversational AI & voice synthesis
- • Sound & music generation
- • Latency and turn-taking patterns
The question isn't IF you're using agents —
It's how much you TRUST them.
First written for daveliew.com while I was learning this; moved to the Brewery in August 2026. More pillars are migrating — the index lives at /learn.