feat(gooddata-eval): capture reasoning_steps through the agentic-CLI path

[Tomkess]

Summary

Stacked on #1708. That PR wires ChatResult.reasoning_steps through runner.py's generic single-turn path (ChatBackend.ask()), but never through the agentic-CLI path (cli/agentic_runner.py → evaluate_agentic_*). As a result, agentic_alert_skill, agentic_metric_skill, and agentic_conversation items could never carry a reasoning trace — run_agentic_items builds its own bare ItemReport and reasoning_steps was simply never assigned, regardless of what the platform emitted.

• alert_skill.py / metric_skill.py / conversation.py: accumulate chat_result.reasoning_steps across every send_message call in each evaluator's run loop; attach to the run/turn result.
• evaluate_agentic_alert_skill / evaluate_agentic_metric_skill / evaluate_agentic_conversation: return the reasoning steps on pass; attach them to the raised exception on fail (mirrors the existing conversation_id-on-exception idiom in ChatClient.ask()).
• cli/agentic_runner.py: _dispatch_agentic now returns whatever evaluate_agentic_* returns; run_agentic_items writes it onto ItemReport.reasoning_steps from either the return value or the caught exception.

general_question/guardrail/search_tool/visualization evaluators are untouched — still return None, no behavior change.

Confirmed live (not just unit tests): ran a real agentic_alert_skill question end-to-end against a live workspace and got a populated reasoning trace where before there was none.

Test plan

• packages/gooddata-eval test suite: 268/268 pass (13 new tests added)
• ruff check clean
• Live end-to-end run against a real workspace produced a non-empty reasoning trace for agentic_alert_skill

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