The Signal

Use Govern first. NIST's AI RMF names four functions — Govern, Map, Measure, Manage — but only Govern is cross-cutting, infused through the other three. Skip it and your evals and monitoring have no owner, no risk tolerance, and no authority to act. Govern is what makes the rest stick.

Verdict: use it. Model Context Protocol is real leverage, not hype — a shared client-server interface that turns the M-models-by-N-tools integration mess into M+N, exposing tools, resources, and prompts over a common JSON-RPC message format. But the spec is still versioned and moving, and authentication and security remain the immature edge, so keep your adapters swappable.

Use C2PA now, but don't trust it alone. The Coalition for Content Provenance and Authenticity signs media with tamper-evident Content Credentials recording origin and AI use. It's the right technical primitive for AI-media authenticity, yet opt-in adoption and metadata stripping mean a missing credential proves nothing today.

Verdict: extra inference compute — the "reasoning" or chain-of-thought approach — earns its cost on hard, multi-step, checkable problems like math, code, and logic, where intermediate steps help. It mostly wastes money on lookup, formatting, and short factual tasks. The 2022 chain-of-thought paper showed these gains emerge only at model scale.

Use a held-back task set; treat saturating agent leaderboards as noise. AgentBench (arXiv 2308.03688) shows the value is structural — eight live, multi-turn environments that expose where agents fail at long-horizon reasoning and instruction-following, not a single leaderboard number. A private set mirroring your workflow cannot be trained on, so it predicts production behavior long after public scores converge.