Salesforce launches Headless 360 to give AI agents platform access via APIs, MCP tools, and CLI. Salesforce exposes CRM, workflow, and data capabilities as programmatic endpoints and developer tooling so agents can act directly on platform services. Outcome engineers should treat vendor platforms as first-class agent infrastructure and design orchestration, access controls, and graph integration from day one (Principle 09).

Most enterprises can’t stop stage-three AI agent threats, VentureBeat survey finds. The survey shows many orgs lack isolation and runtime controls necessary to stop machine-speed agent attacks, leaving monitoring-only defenses ineffective. Outcome engineers must build sandboxing, runtime visibility, and automated kill-switches into agent runtimes to enforce Gate and Immune System protections (Principles 14 & 15).

Zero-Copy GPU Inference from WebAssembly on Apple Silicon. Apple Silicon now lets WebAssembly modules share linear memory directly with the GPU for zero-copy inference, enabling compact, high-throughput local runtimes. Outcome engineers can push secure, embeddable inference into client islands and rethink where compute, latency, and data residency live in agent pipelines (Principle 07).

Claude system prompts as a git timeline. Simon Willison surfaces Anthropic’s Claude system prompt history as a git-like timeline, making prompt provenance and revision history auditable and browsable. Outcome engineers should version and publish system prompts as first-class artifacts for reproducibility, documentation, and post-hoc audits (Principle 13).

Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference. The Train-to-Test analysis argues training smaller, overfitted models combined with more inference sampling can minimize real-world deployment cost compared with large-model training. Outcome engineers must revisit model sizing, sampling strategies, and orchestration decisions to optimize operational cost and validation pipelines (Principles 12 & 16).