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From the team. What we're building and why.
AI Agent Boundary Violations: Why Agents Guess When Instructions Leave Gaps
Research shows most AI agent runs violate at least one scope boundary when instructions are underspecified. The fix is not stronger guardrails — it is a clearer specification.
Comprehension Debt: The AI Code Review Problem Teams Aren't Measuring
Comprehension debt — reviewing AI-generated code faster than you understand it — is accumulating silently in engineering teams. Here is what the research shows and how to slow it down.
Context Rot: Why Your AI Agent Gets Worse the Longer It Runs
Context rot is what happens when a long AI coding session fills with failed exploration and the agent stops taking new direction. Here is why it happens and how to clear it.
How to Reduce AI Agent Token Costs When Running Multiple Sessions
Running multiple AI agents in parallel multiplies token costs — unless you share context between sessions. Here is how Token Intelligence cuts usage by up to 64%.
Why Parallel Agents Change Everything
Running one AI agent is useful. Running five in parallel, each on its own branch, is a different category of tool entirely. Here is why isolation is the missing piece.
The Case Against Context Switching Between AI Agents
Every time you manually switch context between AI coding sessions, you pay a hidden tax. Here is why session continuity and parallel isolation are not convenience features — they are the whole point.
Token Intelligence: Eliminating Redundant File Reads Across Agent Sessions
When multiple agents read the same files independently, you pay for every read. Token Intelligence builds a local code-knowledge graph so agents share what they already know, cutting context consumption by up to 64%.