Reflection

Reflection evaluates an existing candidate. It does not create a forward execution plan; it returns a score, issues, an action, and optionally a revised output.

Reflectors

Reflector	Use
rule_reflector	Deterministic checks for empty, short, invalid JSON, required, or forbidden content
llm_reflector	Rubric-based semantic evaluation and revision through an llm_client
composite_reflector	Merge results from multiple reflectors

Rule-based example

namespace reflection = wuwe::agent::reflection;

auto reflector = std::make_shared<reflection::rule_reflector>(
  reflection::rule_reflector_options {
    .reject_empty_output = true,
    .min_output_chars = 40,
    .required_substrings = { "Planning", "Reflection" },
  });

reflection::reflection_runner runner({
  .reflector = reflector,
});

const auto run = runner.run({
  .task = "Check the conceptual distinction.",
  .original_input = "Compare Planning and Reflection.",
  .candidate_output = answer,
});

For semantic evaluation, construct llm_reflector with a client and model. Its rubric supports weighted criteria, per-criterion thresholds, evidence requirements, and optional revision.

Policy and result

reflection_policy maps normalized findings to pass, revise, retry, replan, block, or escalate. Thresholds and critical/error behavior are explicit host configuration.

reflection_result contains the pass state, score, recommended action, structured issues, revised output, and metadata. reflection_runner records elapsed time, emits start and completion events, and can persist records through in_memory_reflection_store or file_reflection_store.

Planning can use plan_reflection_gate to review step results. Reasoning uses reflection in reflect_and_retry mode. In both cases, the surrounding module remains responsible for deciding whether and how to continue.

See examples/src/reflection_example.cpp for a model-based rubric evaluation.