Amplification over automation
The foundational IEVA principle — AI should improve human judgment and execution without removing human authority. What that means in architecture, not slogans.
Read the essayÆkora · Insights
Working notes on executive AI.
How we think about memory, delegation, governance, and the operating layer — written while building it. These pieces are working drafts published ahead of IEVA’s launch; each is labeled with its status.
All articles10
- All
- Agentic systems
- Building in public
- Executive operations
- High-stakes enterprise design
- Human-AI collaboration
- IEVA product thinking
- Product architecture
- Security and governance
An assistant you can audit
Inspectability isn't a compliance feature — it's what makes an AI system correctable, teachable, and safe to rely on. Lessons from running IEVA on plain text and Git.
Building IEVA in public
IEVA started as a personal knowledge system in an Obsidian repository. Why we're documenting the evolution from working prototype to governed product — honestly, stage by stage.
Designing AI for high-stakes decisions
High-stakes environments don't need faster answers — they need defensible ones. Design rules for AI systems whose output feeds consequential decisions.
From knowledge pipeline to operating cadence
IEVA began as a knowledge pipeline — inbox to projects to output to wiki. Generalizing stages-with-purpose into an executive rhythm is what turned notes into an operating system.
Human-in-the-loop as architecture
"Human in the loop" is usually a checkbox. It should be a load-bearing part of the system — approval gates, reviewable memory, and observable actions that cannot be routed around.
The difference between an assistant and an operating layer
An assistant answers when asked. An operating layer holds context, runs a cadence, and coordinates work. The distinction changes what you should expect — and demand.
What a chief of staff should be able to review
If an AI operating layer works alongside a chief of staff, the review surface is the product. A concrete checklist of what must be inspectable.
Why agent delegation needs clean handoffs
The value of delegating to agents is decided at the handoff. What a bounded objective, isolated context, and reviewable result actually buy you.
Why executive AI needs memory, not just chat history
Chat history remembers words. Executive work needs a system that remembers decisions, rationale, commitments, and open loops — and lets you correct it.
Why provenance matters in delegated AI work
When an agent hands you a conclusion, the first question is "says who?" Provenance is what makes delegated work reviewable instead of merely plausible.