Method _summarize_execution_result

src/harness/agentic_loop/loop.py:806–881

Summarize a raw plan execution result into a compact, structured form. Uses the plan generator model (cheap, e.g. gpt-5) to produce a summary that captures key outputs, findings, files created, and any information subsequent tasks might need. This mirrors the autocom

(
        self, raw_result: str, task_description: str
    )

        pass

    def _summarize_execution_result(
        self, raw_result: str, task_description: str
    ) -> str:
        """Summarize a raw plan execution result into a compact, structured form.

        Uses the plan generator model (cheap, e.g. gpt-5) to produce a
        summary that captures key outputs, findings, files created, and
        any information subsequent tasks might need. This mirrors the
        autocompact/agent-summary pattern from mature agentic harnesses:
        a separate cheap LLM call summarizes output at an execution boundary,
        so only compact context crosses into the orchestrator's awareness.

        If the raw result is already short enough (<= 500 chars), it is
        returned directly without an LLM call to save cost.

        Args:
            raw_result: Full text output from PlanExecutor.execute().
            task_description: Description of the task (for summarization context).

        Returns:
            Compact summary string (typically 200-500 words).
        """
        # Short results don't need summarization
        if len(raw_result) <= 500:
            return raw_result

        # Cap the input to avoid blowing the summarizer's context
        input_text = raw_result[:10000]
        if len(raw_result) > 10000:
            input_text += (
                f"\n\n... [truncated: {len(raw_result) - 10000:,} chars omitted]"
            )

        messages = [
            {
                "role": "system",
                "content": (
                    "You are a concise summarizer for task execution results. "
                    "Given the task description and its execution output, produce "
                    "a structured summary that captures:\n"
                    "1. Key outputs and findings\n"
                    "2. Files created or modified (with paths if available)\n"
                    "3. Important decisions or data produced\n"
                    "4. Any information that subsequent tasks might need\n\n"
                    "Be concise but comprehensive. Max 500 words. "
                    "Use bullet points for clarity."
                ),
            },
            {
                "role": "user",
                "content": (
                    f"Task: {task_description}\n\n" f"Execution output:\n{input_text}"
                ),
            },
        ]

        try:
            response = self.llm_client.completion(