The Cockpit
A separate workspace can hold personal agent configuration and coordinate several repositories. Keep authoritative team knowledge in approved shared storage and respect each repository’s rules.
A separate workspace can hold personal agent configuration and coordinate several repositories. Keep authoritative team knowledge in approved shared storage and respect each repository’s rules.
Use an agent to maintain sourced technical summaries, cross-references and a reviewable wiki. Keep originals, check derived claims and add retrieval when measurements justify it.
Diagnose context failures, organize durable technical knowledge and add QMD retrieval using documented commands, explicit access boundaries and observable maintenance.
AI workflow skills and domain knowledge complement each other. Build a curated, authorized knowledge base and verify whether the output solves the actual problem.
Recognize common failure modes in AI-assisted work: stale facts, exact counting, arithmetic, uncertainty and spatial reasoning. Choose tools and verification suited to the task.
Use team questions to examine task scope, review effort and delivery outcomes. Evaluate AI-assisted work with shared evidence rather than reactions, labels or promises of superiority.
Define acceptance criteria, use deterministic checks for verifiable properties, and review the checks themselves. Passing tests increases confidence within their coverage; it does not prove every behavior correct.
What Anthropic’s Mythos Preview security research demonstrated—and how engineering teams can turn AI findings into validated fixes.
AI can help an individual complete broader implementation tasks. Evaluate that benefit alongside integration, review, ownership and the collaboration a project still requires.
Use AI to prepare concise updates and decision briefs. Choose between asynchronous work and a meeting based on uncertainty, participation and the decision required.
How Claude Code computer use works, how to enable it, and how to turn desktop interaction into evidence for a development task.
Keep the AI workflow as simple as its requirements allow. Add orchestration, retrieval and automation for demonstrated needs while preserving useful checks and review.