AI Engineer · October 12, 2026

Managed agents: version configuration and verify tenant boundaries

Managed agents: version configuration and verify tenant boundaries video thumbnail
Why it matters

This workshop Q&A covers configuration in CI/CD, long-running tools, and resource separation for customer-facing agents. Current documentation treats agent definitions as versioned resources and metadata as tracking fields. Labelling a session with a customer ID is not an authorization boundary. A deployment needs deliberate credential and resource separation, and self-hosted execution also requires the operator to supply its own sandbox controls.

My takeaway: Record the deployed configuration version and overrides. Using separate test tenants, attempt cross-tenant session, file, memory, and tool access. Treat metadata as routing information, and verify the credential boundary instead of assuming a workspace label enforces it.
Keep exploring

More curated notes connected through Agent Security and AI Engineering.

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

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.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.