fix(agent): make context-budget hard_max configurable via agent_input_token_hard_max setting (#1273)

Completes the reviewer requirement from PR #1190 review that was carried
over but not implemented in #1230:

> "The hard max is a function-local constant. For this setting, the ceiling
>  should be configurable or at least represented as a named setting/default
>  with tests."
                                                                — review on #1190

#1230 shipped the adaptive auto-derivation but left `DEFAULT_HARD_MAX = 200_000`
as a hardcoded module constant in src/context_budget.py. Admins on premium
APIs with large context windows (kimi-k2 / minimax-m3 at 1M, etc.) can use
their full window today only by setting `agent_input_token_budget`
explicitly — which then takes them off the adaptive auto-path entirely.

## What this PR changes

- src/settings.py: register `agent_input_token_hard_max` in
  DEFAULT_SETTINGS, default 200_000 (matches `DEFAULT_HARD_MAX`). Inline
  comment documents the no-op semantics in the explicit branch.

- src/agent_loop.py: read the setting at the call site and pass it as the
  `hard_max` kwarg of `compute_input_token_budget`. Defensive parsing —
  missing / non-int / zero values fall back to `DEFAULT_HARD_MAX`, so a
  misconfig cannot silently zero the budget.

- src/tool_implementations.py: three friendly aliases for `manage_settings`:
  - "hard max" -> agent_input_token_hard_max
  - "token budget cap" -> agent_input_token_hard_max
  - "input budget cap" -> agent_input_token_hard_max
  Plus the existing "token budget" -> agent_input_token_budget keeps a
  matching shorter alias "input budget".

- tests/test_context_budget.py: 6 new tests on top of the existing 6:
  - hard_max raises the auto ceiling (1M ctx + raised cap -> 85% of ctx)
  - hard_max lowers the auto ceiling (128K ctx + 50K cap -> 50K)
  - hard_max has no effect on the explicit branch
  - DEFAULT_SETTINGS contains the new key
  - manage_settings aliases are registered
  - the live get_setting path returns the override value, and malformed
    values fall back per the agent_loop defensive parsing

12 passed in 0.04s. No changes to the pure helper signature or semantics;
#1230's behavior is the default when the new setting is unset.

## How it lets users drop the explicit override

Before this PR, on a 1M-context model:
  agent_input_token_budget = 900_000  (explicit)  -> 900K  [user override]
  agent_input_token_budget = <unset>  (auto)      -> 200K  [HARD_MAX]

After this PR, same model:
  agent_input_token_budget = <unset>
  agent_input_token_hard_max = 900_000
                                      -> min(1M * 0.85, 900K) = 850K  [auto, no override needed]

The explicit-override path keeps working unchanged for users who prefer it.

src/agent_loop.py

@@ -1487,13 +1487,23 @@ async def stream_agent_loop(
     _t3 = time.time()
     try:
         from src.context_compactor import trim_for_context
-        from src.context_budget import compute_input_token_budget
+        from src.context_budget import compute_input_token_budget, DEFAULT_HARD_MAX
         from src.settings import is_setting_overridden
 
         soft_budget = int(get_setting("agent_input_token_budget", 6000) or 0)
         if soft_budget > 0:
             before_trim_tokens = estimate_tokens(messages)
             reserve_tokens = min(max(max_tokens or 1024, 512), 2048)
+            # Honour the configurable ceiling for the auto-derived budget path.
+            # No-op when the user has an explicit `agent_input_token_budget`
+            # (that branch ignores hard_max). Falls back to DEFAULT_HARD_MAX
+            # on missing/malformed values so misconfig can't zero the budget.
+            try:
+                hard_max = int(get_setting("agent_input_token_hard_max", DEFAULT_HARD_MAX) or DEFAULT_HARD_MAX)
+            except (TypeError, ValueError):
+                hard_max = DEFAULT_HARD_MAX
+            if hard_max <= 0:
+                hard_max = DEFAULT_HARD_MAX
             # Scale the default budget to the model's context window so long-context
             # models aren't silently capped at 6000; an explicit user setting is
             # still honoured (clamped to the window). (#1170)
@@ -1501,6 +1511,7 @@ async def stream_agent_loop(
                 soft_budget,
                 context_length,
                 is_setting_overridden("agent_input_token_budget"),
+                hard_max=hard_max,
             )
             trimmed_messages = trim_for_context(
                 messages,