| name | rerec-agent-development-release-engineering |
| title | AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.04620 |
| keywords | ["LLM Agents","Self-Improvement","Release Engineering","Quality Assurance"] |
| description | Apply software release engineering practices to self-improving LLM agents to achieve stable evolution. AgentDevel uses implementation-blind critique, executable diagnosis, and flip-centered gating to prevent regression while enabling auditable improvement trajectories. |
When to Use This Skill
- Building self-improving LLM agents for production deployment
- Scenarios requiring auditability and non-regression guarantees
- Multi-turn agents where failure modes must be preventable
- Systems needing transparent improvement tracking
- Continuous evolution with stability requirements
When NOT to Use This Skill
- One-time agent optimization (release engineering overhead unnecessary)
- Experiments requiring rapid iteration
- Scenarios where regression risk is acceptable
Problem Summary
Current self-improving LLM agents suffer from instability and lack auditability. Improvement trajectories are difficult to track, causing regression into previously-fixed failure modes. Practitioners cannot easily identify what causes improvements or verify that fixes remain stable. This creates risk in deployed agents where stability is critical.
Solution: Release Engineering Framework for Agents
Apply proven software engineering practices: implementation-blind critique, executable diagnosis, and flip-centered gating.
class AgentReleaseManager:
def __init__(self, agent, test_suite):
self.agent = agent
self.test_suite = test_suite
self.version_history = []
def self_improvement_cycle(self, max_iterations=10):
"""Controlled agent evolution via release engineering"""
current_version = self.snapshot_agent()
self.version_history.append(current_version)
for iteration in range(max_iterations):
failures = self.identify_failures(current_version)
if not failures:
print()
diagnoses = []
failure failures:
execution_trace = failure[]
diagnosis = .diagnose_from_trace(execution_trace)
diagnoses.append(diagnosis)
patterns = .aggregate_patterns(diagnoses)
improvements = .synthesize_improvements(patterns)
improvement improvements:
new_version = .apply_improvement(current_version, improvement)
regression_risk = .assess_regression_risk(
current_version, new_version
)
regression_risk < REGRESSION_THRESHOLD:
current_version = new_version
.version_history.append(current_version)
()
:
()
current_version
():
failures = []
test_case .test_suite.get_execution_benchmarks():
result = version.execute(test_case)
result.failed:
failures.append({
: test_case,
: result.execution_trace,
: result.error_message
})
failures
():
failure_signature = {
: execution_trace.get_failure_step(),
: execution_trace.get_state_before_failure(),
: execution_trace.get_action(),
: execution_trace.get_outcome()
}
failure_signature
():
regressions =
total_tests = (.test_suite)
test .test_suite:
old_result = old_version.execute(test)
new_result = new_version.execute(test)
old_result.passed new_result.failed:
regressions +=
regression_rate = regressions / total_tests
regression_rate
