Agent ops: telemetry, evaluation, control, security, and cost

Agent Seer: Synthesizing Scenarios from Specification Understanding generates realistic, scalable evaluation scenarios from tool specifications so teams can test agent–tool interactions without hand-curated cases or live systems. That gives outcome engineers a way to automate stress-testing and scenario coverage for agents, improving verification and reproducibility (Principles 06, 14).

DARP: Durable Activity Record Protocol defines a small open protocol for agents to emit short, privacy-preserving activity records consumed by apps like Factory Log. Outcome engineers can use this to standardize telemetry, create durable artifacts for audits, and simplify downstream observability and documentation (Principles 08, 13).

A deep dive into T3 Code outlines a local control plane that unifies multiple coding agents across browser, desktop, and mobile. That pattern gives teams a replicable control-plane model for orchestrating, debugging, and containing agents in development and production (Principles 06, 09).

The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents lays out infrastructure, network, and control-plane defenses to prevent lateral movement and data exfiltration. Outcome engineers must adopt this multi-layered approach when designing agent stacks to reduce attack surface and enforce least privilege (Principles 10, 14).

Even an AI cost-management vendor can lose control of its agent spending reports that unchecked agents racked up thousands in unbudgeted charges, exposing gaps in governance and monitoring. Use this as a caution: build spend observability, hard budget gates, and runtime controls into your agent orchestration before scale (Principles 10, 14, 15).