[Agentic Context Engine] Enhance execution trace analysis with cross-trace correlation

I combined two recent approaches, Stanford's ACE and the Reflective Language Model pattern, to build agents that write code to analyze their own execution traces. **Quick context on both:** * **ACE** ([arxiv](https://arxiv.org/abs/2510.04618)): agents learn from execution feedback through a Reflector (LLM-as-a-judge) and SkillManager that curate a Skillbook of strategies. No fine-tuning, just in-context learning. * **RLM** ([arxiv](https://arxiv.org/abs/2512.24601)): instead of loading full input into context, an LLM writes and executes code in a sandbox to selectively explore the data. **The problem ACE had:** the Reflector reads execution traces in a single pass. Works fine for a few conversations, but once you're analyzing hundreds of traces, patterns get buried and single-pass analysis misses cross-trace correlations. **The combination:** the Recursive Reflector uses the RLM pattern to analyze ACE's execution traces. Instead of reading traces directly, it receives metadata in the prompt and gets full trace data injected into a sandboxed REPL namespace. It then writes Python to programmatically query, cross-reference, and explore the traces -> finding patterns that single-pass reading misses. **Benchmark results (τ2-bench, Sierra Research):** Measured on τ2-bench, a benchmark that challenges agents to coordinate with users across complex enterprise domains. I ran offline trace analysis on past runs, extracted strategies, and appended them to the agent's policy. The improvement grows with stricter consistency requirements: |Metric|Baseline|With my engine|Improvement| |:-|:-|:-|:-| |pass^(1)|41.2%|52.5%|\+27.4%| |pass^(2)|28.3%|44.2%|\+56.2%| |pass^(3)|22.5%|41.2%|\+83.1%| |pass^(4)|20.0%|40.0%|\+100.0%| *Claude Haiku 4.5 · pass\*\***^(k)* *measures consistency across k consecutive runs* Open-sourced it here: [https://github.com/kayba-ai/agentic-context-engine](https://github.com/kayba-ai/agentic-context-engine) Happy to discuss the approach or answer questions about the architecture.