[kaxil]

Follow-up to #73587, which added Strands support through as_strands_tools(). A tool that kept raising ModelRetry, or kept failing argument validation, came back to the model as an error result every time, so a Strands agent could loop without end. Raising from the tool does not help: Strands catches any exception a function tool raises and hands it to the model as another error. as_strands_tools() has not been released yet, so this replaces it rather than deprecating it.

• AirflowTools is now a Strands plugin. Its AfterToolCallEvent hook re-raises ToolCallError, which Strands raises out of the run as EventLoopException, so a failure the model cannot fix fails the task and Airflow's retry takes over.
• Failures the model can correct are bounded by the tool's max_retries, counted as Pydantic AI counts them: once per tool per model turn, so calls a framework runs concurrently in one turn count once.
• Google ADK: AirflowTools in tools.adk is an ADK toolset. A result reaches the model as {"result": ...}, a failure it can correct as {"error": ...}, and any other failure raises ToolCallError out of the run.
• airflow_tools() moves to the shared toolset base, and collect_tools() turns the toolsets and tools an adapter is given into one list.
• The LangChain bridge runs on the same interface: a correctable failure becomes a ToolMessage with status="error", and a tool that keeps failing raises ToolCallError.
• SandboxToolset can own its sandbox from synchronous code with a with block, which a task running a native agent needs. A tool call outside the block is refused instead of provisioning a sandbox nothing would destroy. Attaching to another task's sandbox works from both forms.
• MCPToolset.airflow_tools() raises: an MCP session belongs to the Pydantic AI run that opens it, so a native agent should use its framework's own MCP client.

Why one neutral interface under both adapters? Strands, ADK and the LangChain bridge now share one conversion, one error model and one retry budget, so an adapter for another framework is a thin mapping rather than a copy of those rules.

Why the Strands and ADK tests skip in CI. Strands pins mcp below 2.2 and ADK pins OpenTelemetry at 1.42.1 or lower, both below the versions in Airflow's development environment, so neither framework can be installed there. Both integrations are marked experimental, and the docs list the versions they are tested with (Strands 1.56.0, ADK 2.9.1).

An end-to-end Dag that runs every toolset through AgentOperator, a Pydantic AI agent, Strands, ADK and LangChain passes with a real Claude model and a Modal sandbox.

The end-to-end run, and the Strands routes from it:

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• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.