feat: standardize message whitespace and JSON formatting

Normalize api_messages before each API call for consistent prefix
matching across turns:

1. Strip leading/trailing whitespace from system prompt parts
2. Strip leading/trailing whitespace from message content strings
3. Normalize tool-call arguments to compact sorted JSON

This enables KV cache reuse on local inference servers (llama.cpp,
vLLM, Ollama) and improves cache hit rates for cloud providers.

All normalization operates on the api_messages copy — the original
conversation history in messages is never mutated.  Tool-call JSON
normalization creates new dicts via spread to avoid the shallow-copy
mutation bug in the original PR.

Salvaged from PR #7875 by @waxinz with mutation fix.

run_agent.py

@@ -3212,7 +3212,7 @@ class AIAgent:
         if platform_key in PLATFORM_HINTS:
             prompt_parts.append(PLATFORM_HINTS[platform_key])
 
-        return "\n\n".join(prompt_parts)
+        return "\n\n".join(p.strip() for p in prompt_parts if p.strip())
 
     # =========================================================================
     # Pre/post-call guardrails (inspired by PR #1321 — @alireza78a)
@@ -8047,6 +8047,36 @@ class AIAgent:
             # manual message manipulation are always caught.
             api_messages = self._sanitize_api_messages(api_messages)
 
+            # Normalize message whitespace and tool-call JSON for consistent
+            # prefix matching.  Ensures bit-perfect prefixes across turns,
+            # which enables KV cache reuse on local inference servers
+            # (llama.cpp, vLLM, Ollama) and improves cache hit rates for
+            # cloud providers.  Operates on api_messages (the API copy) so
+            # the original conversation history in `messages` is untouched.
+            for am in api_messages:
+                if isinstance(am.get("content"), str):
+                    am["content"] = am["content"].strip()
+            for am in api_messages:
+                tcs = am.get("tool_calls")
+                if not tcs:
+                    continue
+                new_tcs = []
+                for tc in tcs:
+                    if isinstance(tc, dict) and "function" in tc:
+                        try:
+                            args_obj = json.loads(tc["function"]["arguments"])
+                            tc = {**tc, "function": {
+                                **tc["function"],
+                                "arguments": json.dumps(
+                                    args_obj, separators=(",", ":"),
+                                    sort_keys=True,
+                                ),
+                            }}
+                        except Exception:
+                            pass
+                    new_tcs.append(tc)
+                am["tool_calls"] = new_tcs
+
             # Calculate approximate request size for logging
             total_chars = sum(len(str(msg)) for msg in api_messages)
             approx_tokens = estimate_messages_tokens_rough(api_messages)