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### Motivation This PR evolves the harness from a pure execution runtime into an **observable, replayable agent evaluation platform**. The current `harness/graph` checkpoint mechanism is insufficient for true event-sourced introspection—we need append-only event logs capturing every tool call, state transition, memory write, and approval decision, enabling deterministic replay, fork/diff, postmortem analysis, and time-travel debugging. ### Key Design Goals 1. **Event-Sourced Execution Model** Replace coarse checkpoints with granular, append-only event logs. Every operation becomes a durable event: tool invocation, state mutation, memory update, human approval. This unlocks deterministic replay, branching execution histories, and regression datasets derived directly from production failures. 2. **First-Class Replay & Evaluation Loop** Replay is not an afterthought—it is a core primitive. A single live run seeds an offline corpus that supports: repeated playback, model substitution, tool result mocking, and strategy comparison. The harness graduates from "executor" to "continuous evaluation platform" where failed production traces convert directly into offline regression suites. 3. **Operational Observability** Beyond raw traces, expose metrics that prove stability over time: - Tool success / failure rates - Approval latency distributions - Retry frequencies - Checkpoint restore reliability - Memory retrieval quality - Cost per completed task - Fork replay pass rates The underlying thesis: the bottleneck for most agent systems is not execution capability, but the inability to **demonstrate continuous, measurable improvement**. ### Type of change - [x] New Feature (non-breaking change which adds functionality)