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Phase 4: advanced agents (reflection, memory determinism, providers)
- env/agents/reflector.py: deterministic propose -> critique -> revise agent. Proposes via BaselinePolicy, critiques against env/safety guardrails (prompt injection / risky content / forbidden escalation), and revises unsafe proposals to a safe action within a bounded retry cap. Provider-free and reproducible. - benchmark/agents.py: ReflectiveAgent benchmark wrapper (name "reflective"); registered in runner.py (run_agent) and scripts/run_benchmark.py (offline agent). - Memory determinism: tests/test_memory_augmented.py locks the few-shot path (trajectory_store -> example_extractor -> prompt_enhancer) as a deterministic, read-only function of its inputs (INV-2 for the LLM prompt path). - More providers: kept the OpenAI-compatible client design (works with OpenAI, Azure, Groq, Ollama, and Anthropic's OpenAI-compatible endpoint via base_url); tests/test_providers.py locks custom base_url/model resolution. Per the claude-api skill's guardrail, did NOT inject the Anthropic…474cdf7