Idea: Branch Prediction & Speculative Execution for AI Tool Calls

You can probably 10x AI iteration speed by trading latency for tokens with branch prediction & speculative tool call execution:

1. When an agent issues a tool_call, fork the context & workspace into N predictive paths – typically 2 for success or failure.
2. While the tool call is running, ask the model to predict both the successful tool response and the likely failure response with follow-up steps (potentially more tool calls). Send the predictions out-of-band. Run those tool calls in isolated context (local tools are cheap).
3. Once the actual tool response completes, compare the result to predictions to detect a prompt cache hit or a cache miss.
4. On a cache hit, immediately dispatch the pre-planned actions, which may be a follow-up tool call. This saves on follow-up tool call latency.
5. On a cache miss, clear the predictive pipeline and return to normal.

To save on tokens, you may only want to predict down the happy path, or base it on some heuristics of past tool calls. Simple predictions could be made with cheaper models, though.