Don’t pick a coding agent on a single leaderboard—test long‑horizon repo work yourself and trust your own evals first.
Claude’s new dynamic workflows plus a cheaper fast mode make multi-agent automation practical—kick the tires, but upgrade with the hotfix first.
GitHub is nudging AI dev agents toward safer defaults and measurable costs—use the new controls to lock in trust and rein in tokens.
Agents are production software now—upgrade for tracing and state isolation, then measure everything.
The agent governance layer is arriving: treat agents like services, wire them through MCP, and enforce identity, policy, and audit from the start.
You can now treat AI coding like any other investment: instrument it, tie it to delivery outcomes, and cut waste fast.
Local agents work when you treat them like systems, not prompts: own serving, state, retrieval, and audit.
Use this Hermes Agent vs OpenClaw/GoClaw guide to focus your next agent POC on the right tradeoffs.
Warnings don’t undo falsehoods in training data—filter or reweight negated content or you’ll hard-code errors into your model.