Build the Agent Stack: Coordination, Memory, Evals, and Guardrails

GitHub Issues become an agent coordination protocol. They provide a shared, auditable layer for agents working across models, machines, sessions, and projects. That is Principle 09 with Principles 11 and 13: coordination state needs to be legible and durable.

Fixing agent memory reframes memory as a reliability and security surface: agents can preserve failures, stale context, and malicious instructions. Outcome engineers need provenance, inspection, and deletion controls before memory becomes an untrusted dependency—Principles 02 and 14.

Advanced evals: How to Find and Fix Hidden AI Failures in Your Product shows how to turn vague product quality judgments into repeatable tests that expose hidden failures. Build these evaluations into the delivery loop rather than treating them as a launch checklist: Principle 16, Audit the Outcomes.

Claude Code power user tips packages practical patterns for parallel execution, structured context, isolation, automation, and self-verification. The useful shift is from prompting harder to designing workflows where agents produce evidence that their changes work—Principles 03, 06, and 14.

Okta adds an AI agent runtime gateway with runtime enforcement and a broader kill switch for agent access. As agents gain durable permissions and execute across enterprise systems, identity, policy enforcement, and immediate intervention become core infrastructure—Principles 10 and 15.