Introduce AWS Strands Agents hook to common AI provider and Skills integration

[gopidesupavan]

Add Strands Agents hook to common AI provider

Summary

Add AWS Strands Agents as a new agent backend for AgentOperator and @task.agent in the common AI provider, building on the BaseAIHook contract.

• Introduce StrandsHook (shared Strands SDK integration) and StrandsGeminiHook as the first backend (conn_type: strands-gemini, default connection ID: strands_default)

• Wire toolsets through _tool_spec_to_native, converting ToolSpec instances to Strands-native tools

• Add skills support end-to-end: SkillSpec dataclass on BaseAIHook, skills / skills_params on AgentRunRequest and AgentOperator, and Strands AgentSkills plugin integration for filesystem paths and inline skill definitions

• Register the new connection type in provider.yaml / get_provider_info.py and add optional dependency:

  pip install 'apache-airflow-providers-common-ai[strands]'

  (strands-agents[gemini]>=1.0.0)

• Add example DAGs (example_strands.py) covering basic operator usage, skills, inline SkillSpec + SQL toolset, direct hook usage, and @task.agent

• Document connection setup, hook usage, and operator skills in new/updated RST pages

Depends on

BaseAIHook PR #67438

---

Follow-ups

Durable execution for Strands (durable=True)

StrandsHook currently sets supports_durable=False. A follow-up PR should mirror the pydantic-ai durable path so Strands agents can resume from cached steps on task retry.

Out of scope for this PR: usage limits for Strands hooks.

Skills for Pydantic AI (pydantic-ai-skills)

PydanticAIHook currently leaves supports_skills=False, so AgentOperator.skills / skills_params only work with Strands backends in this PR. A follow-up should wire the same operator-level skills API to pydantic-ai via the pydantic-ai-skills library (Agent Skills / agentskills.io spec with progressive disclosure).

Files changed

Area	Files
Hooks	hooks/strands_ai.py, hooks/base_ai.py
Operator	operators/agent.py
Examples	example_dags/example_strands.py
Provider metadata	provider.yaml, get_provider_info.py, pyproject.toml
Docs	docs/connections/strands.rst, docs/hooks/strands_ai.rst, docs/operators/agent.rst, …
Tests	tests/unit/common/ai/hooks/test_strands_ai.py, test_base_ai.py, operators/test_agent.py

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

---

• 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.

[gopidesupavan]

Thanks you @kaxil — resolved comments.

[kaxil]

This constructs AgentRunResult with only output and model_name, so usage and tool_names stay at their None defaults. log_run_summary then skips both the token counts (logging.py:35) and the tool-call sequence (logging.py:49) for Strands even when tools ran, whereas the pydantic-ai path populates both. Strands' result exposes usage metrics and a tool trace; mapping them into AgentUsage / tool_names here would match the pydantic-ai path. Use getattr(..., None) guards if the metrics shape varies across SDK versions. (Low priority: no crash, just missing observability.)

[gopidesupavan]

Yeah this needs refactor , will updated..

[github-actions]

uv.lock on main just moved via #67860 ("[main] Upgrade important CI environment"), commit 707e316 and this PR currently conflicts.

Quickest fix:

git fetch upstream main && git rebase upstream/main
rm uv.lock && uv lock
git add uv.lock && git rebase --continue
git push --force-with-lease

Automated nudge — ignore if you're not ready to rebase. This comment is updated in place on future uv.lock bumps.