AI Engineer · September 27, 2026

Orchestras, Not Factories: How the Fastest Builders Work — Charlie Holtz, Conductor

Orchestras, Not Factories: How the Fastest Builders Work — Charlie Holtz, Conductor video thumbnail
Why it matters

Charlie Holtz describes Conductor’s approach to coordinating coding agents through shared cloud workspaces and organizational context. His examples include CI-enforced human review for migrations, carefully maintained agent instructions, and a database of working information queried by agents. The presentation combines workflow advice with product demonstrations; it does not measure a general productivity advantage.

My takeaway: Identify changes that require human review, maintain the instructions agents repeatedly read, and give shared workspaces explicit access boundaries. Measure completed work before investing heavily in custom orchestration.
Keep exploring

More curated notes connected through AI Engineering and Agent Security.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

Microsoft Security Blog · guide

AI vulnerability research: measure reproducible findings and completed fixes

Microsoft’s FORGE account describes the work between a model’s vulnerability claim and a useful repair: reusable builds, duplicate removal, reachability checks, project-specific verification, reproducible triggers and regression tests. Structured rejection reasons help improve later searches. The useful operational measure is the flow of findings that survive verification and reach a fix, rather than the number of candidates generated. Reported successful-case costs exclude parts of screening, failed attempts and human work, so they are not the total cost of operating this pipeline.

OpenAI News · framework

Frontier training safety cases: connect evidence to enforced pause and rollback controls

OpenAI proposes training-run safety cases combining alignment evaluations, containment and monitoring with explicit operational ownership. Concrete measures include immutable transcripts, held-out incident tests, checks for evaluation gaming, response deadlines and fail-closed monitoring. Independent internal challenge, leadership vetoes and tracking downstream uses support stopping a run and reversing affected work. The article describes recommendations still being implemented, rather than audited proof that every safeguard already operates or that residual risk has been eliminated.