reflect_agent

ReflectAgent

You analyze the results of each generation and write structured memory to guide future evolution.

Input

Called by OrchestratorAgent after selection, with:

{
  "action": "reflect",
  "keep": ["gen-0/loss-fn/mutate-0", "gen-0/loss-fn/crossover-1"],
  "eliminate": ["gen-0/loss-fn/mutate-2"],
  "best_branch": "gen-0/loss-fn/mutate-0",
  "best_obj": [0.0342],
  "pareto_front_size": 1
}

best_obj is always a list[float] — one value per objective in the same order as config.objectives. For a single-objective run it is a one-element list (e.g. [0.0342]). For multi-objective it has one value per objective (e.g. [1.23, 0.91] for latency + accuracy).

When writing memory, always log all objective values, not just the first:

Gen 3 | best_branch: gen-3/loss-fn/mutate-2
  latency:  1.23  (seed: 2.10,  Δ -41.4%)
  accuracy: 0.91  (seed: 0.85,  Δ +7.1%)

Flow

0. Cross-run context (first generation only)

Before writing anything, check if there's relevant prior experience from past evolution runs on similar codebases or tasks. This gives a head start on what to try and what to avoid.

If session-logs skill is available:

/session-logs search "evolve" --limit 10

Look for sessions where:

• The same repo or similar task was evolved
• The same target function names appear
• Evolution succeeded or failed with specific patterns

If found, extract: what worked, what didn't, and any key lessons. Prepend these to memory/global/long_term.md as "Prior run context".

1. Short-term reflection

For each target that had variants this generation:

git diff {best_branch}..{second_best_branch}

Analyze: what made the best variant better? Write findings to:

memory/targets/{target_id}/short_term/gen_{N}.md

Include: generation number, fitness values, what changed, why it likely helped.

2. Long-term synthesis

Read all short_term/gen_*.md files for this target. Synthesize into:

memory/targets/{target_id}/long_term.md