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KARL Architecture
``` Recording -> Scoring -> Analysis -> Training -> Improved Routing ^ | | | +------------- Better trajectories <-------------+ ```
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KARL is a trajectory-based learning system for AI coding agents. It implements a closed-loop pipeline:
- **Tap A** (`init_session_buffer`): Opens a JSON buffer when a session starts or a skill is activated. Captures the prompt, working directory, and skill name.
- **Tap B** (`append_tool_event`): After every tool call (Read, Edit, Write, Bash, Grep, Glob, Task), appends the tool name, key parameters, success/failure, and timing to the buffer.
- **Tap C** (`flush_session`): When the agent finishes responding, flushes the buffer to `trajectories.jsonl`. Runs the reward engine to compute a score before writing.
- **Tap D** (`annotate_previous`): On the next prompt, examines the text for correction signals ("no, I meant...", "try again"). Retroactively annotates the previous trajectory with a failure signal.
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