Second, deeper pass over tools/gateway/hermes_cli plus first pass over
the trees wave 1 missed (acp, acp_adapter, skills, computer_use, docker,
dashboard, conformance, monitoring, secret_sources, hermes_state,
providers). Same rubric as wave 1 (AGENTS.md test policy); security,
alternation/caching invariants, issue-number regressions, and E2E kept.
Real test-quality fixes found and rooted out along the way:
- tests/tools/test_command_guards.py made real auxiliary-LLM HTTPS calls
(DEFAULT_CONFIG smart-approval leaked in) — pinned approval
mode=manual via autouse fixture: 17.4s → 0.4s.
- test_model_switch_custom_providers.py / test_user_providers_model_switch.py
silently probed live provider catalogs (~2s/test) — stubbed
cached_provider_model_ids/provider_model_ids/fetch_api_models.
- test_telegram_noise_filter.py: 15-platform copy-paste matrix over
shared gateway.run logic → 3 representative platforms (55s → 3.9s).
- test_gateway_shutdown.py: stop()'s 5s interrupt-deadline loop spun on
MagicMock agents — interrupt.side_effect now clears _running_agents
(22s → 1.0s).
- test_gateway_inactivity_timeout.py poll-harness timings shrunk 3-5x
(24s → 1.1s); test_mcp_stability.py backoff/SIGTERM-grace sleeps
patched (15.4s → 2.5s); test_async_delegation.py negative-drain wait
5s → 0.5s.
- test_telegram_init_deadline.py: loop-block margin restored to 1.0s
with rationale comment — the watchdog-dump assertion needs the loop
blocked well past deadline+grace under parallel load (flaked once in
the 40-worker verification run at a 0.2s margin).
Verification: full hermetic suite via scripts/run_tests.sh —
2,438 files, 21,718 tests passed, 0 failed, 293.9s wall.
Suite totals vs original baseline: 46,820 → 19,757 test functions
(−57.8%), wall 583.5s → 293.9s (−50%), subprocess CPU 13,564s → 11,623s.
The tool-result persistence budget was a fixed 100K chars/result and 200K
chars/turn regardless of the active model. On a small-context model (e.g. a
65K-token local model switched into mid-session) a single large tool result
(reporter: a 279K-char search result) or a full 200K-char turn (~50K tokens)
could by itself approach or exceed the window, forcing an oversized request
that the provider rejects as "Prompt too long".
- budget_config.budget_for_context_window() scales per-result/per-turn char
caps to a fraction of the model window, clamped to the historical 100K/200K
defaults (large models unchanged) and floored so small models stay usable.
- resolve_threshold() now caps the per-tool registry value at default_result_size
so tools that register a fixed 100K cap (web/terminal/x_search) don't re-inflate
a scaled-down budget. No-op for the default budget (both 100K).
- tool_executor wires the agent's live context_length (recomputed on model
switch) into all four persist/turn-budget call sites.
read_file stays inf-pinned (no persist loop). Verified E2E: a 279K-char result
against a 65K model collapses to a ~1.6K preview; a 200K model is byte-identical
to today.
