* 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): keep HarnessGuard from rewriting the result that ends a loop
The alternating-loop check fired when three of the last four tool calls
failed, without checking the current call. After A and B failed in turn,
a successful B therefore received the OSCILLATION DETECTED warning: the
model was told to stop just as the loop ended, and structured output such
as JSON was corrupted by the appended text.
Only a failing call now continues the pattern, so successful results reach
the model unchanged. Failing alternation still warns.
* fix(agents): only warn on an alternating loop when the whole window failed
The oscillation check still counted 3 errors out of the last 4 calls, so a
window that contained a success could warn on the next failure, e.g.
A err, B err, A ok, B err. A success ends the loop, so require all four
calls in the window to have failed, and pin the break shapes with tests.
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Co-authored-by: Totoro <msy626836554@gmail.com>
