* refactor(agent): ground runtime decisions in structured evidence
Use producer status and structured errors for tool recovery, preserve memory context without lexical confirmation, and remove predetermined receipt and skill conclusions.
Require explicit MCP discovery and authenticated schedule trial actions. Preserve authorization, ownership, isolation, and bounded execution; update documentation and regression coverage.
* test(sandbox): expect read_file initialization error callback
* fix(agent): address evidence review follow-ups
* fix(agent): preserve retry budgets for OpenAI transport errors
* fix(agent): expose tool execution status in model requests
Project validated execution metadata and exact tool text into request-only JSON so status survives provider serialization. Preserve stored results and receipt hashes, cover SDK and graph boundaries, and migrate replay input hashes without changing recorded responses.
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Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(agents): normalize Command-wrapped tool results
Command-wrapped ToolMessages skipped result metadata and progress
tracking, so error receipts could be recorded as success.
* fix(agents): stamp error meta from subagent_status failures
Delegated task Commands leave ToolMessage.status at success and do not
use an Error: content prefix, so normalize_tool_message was labeling
failed/cancelled/timed_out results as success. Honor structured
subagent_status before content heuristics and cover the four statuses.
* style: ruff-format tool_result_meta tests
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Co-authored-by: Yuzhong Zhang <BetterAndBetterII@users.noreply.github.com>
