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Automate autonomous agent lifecycles by chaining initialization, baseline capture, parallel agent spawning, and result evaluation into a single workflow to handle complex code refactoring tasks.

The best way to benchmark refactoring agents is to evaluate their solutions against a captured baseline using metric-based quantitative evaluation and LLM-based qualitative judgment to rank final integration.

Template-based agent dispatching works by spawning multiple agents in parallel during the execution phase, ensuring consistent task generation and measurable outcomes across complex engineering workflows.

Yes, you can use external evaluation tools to rank agent performance. The workflow supports integrating these tools to apply both quantitative metrics and qualitative LLM judgment for result evaluation.

Yes, autonomous agent orchestration supports test generation and performance optimization. It automates these complex engineering workflows end-to-end, from initial baseline capture to final result merging.

Use a unified workflow for agent initialization and evaluation to solve the fragmentation of agent-based development, ensuring consistent and measurable outcomes by merging parallel results automatically.