Agent Orchestration
Multi-agent workflows that route work to the right specialist, keep roles clean, and make handoffs explicit instead of magical.
Routing
This is the server room of the lab — the place where agents, tools, memory, dashboards, and automation loops are wired together into systems that stay understandable under load.
I’m interested in AI systems that can actually be operated — not just demoed. That means predictable routing, visible tool use, careful memory boundaries, and interfaces that make it obvious what the system is doing.
Multi-agent workflows that route work to the right specialist, keep roles clean, and make handoffs explicit instead of magical.
RoutingRetrieval, memory, and vault workflows that turn scattered notes, files, and logs into context the system can actually use.
RAG • MemoryDashboards and logging layers that expose model behaviour, tool activity, session state, and failure points before they become mysteries.
MonitoringScheduled tasks, deployment routines, and operational glue that keep systems moving without turning them into black boxes.
WorkflowThe goal isn’t raw complexity. It’s building systems that can reason, act, recover, and explain themselves well enough for a human to stay in charge.
Choosing the right model for the job, switching tiers when needed, and keeping specialised work bound to the right runtime identity.
Giving agents access to the right tools, sequencing those tools safely, and verifying output instead of trusting first-pass results.
Scoped memory, vault-backed context, and research pipelines that keep prior work accessible without drowning the model in tokens.
Status cards, dashboards, logs, and live operational surfaces that help humans inspect, steer, and trust the system.
Most of the systems here follow the same pattern: understand the request, route it to the right capability, execute with clear tool boundaries, then verify the result before calling it done.
That architecture shows up in agent workflows, research pipelines, classroom tooling, and site operations. The details change, but the principle doesn’t: clarity first, then automation.
System layers
Design priorities
Human still in charge
Design Principle“A good AI system shouldn’t feel mystical. It should feel inspectable. You should be able to see where the context came from, why the tool was chosen, what happened when it failed, and how to improve it without guessing.”
These are the kinds of systems this section will continue to expand — practical builds that connect reasoning, tooling, and real-world workflows.
Orchestrated systems where research, writing, coding, teaching, and archive work move through dedicated specialist lanes.
Interfaces for live task state, usage, logging, and system health — designed to reduce guesswork while the system is running.
Vault, repo, and document pipelines that make the right context available at the moment it matters.
Cron jobs, deploy routines, and operational glue that keep recurring work happening without losing visibility.
Browse the broader project notebook, step into the simulations, or trace how the rest of the lab fits together.