🤖 AI Summary
This work addresses critical challenges in configuration management for large language model (LLM)-based coding agents, including configuration reuse ambiguities, unclear permission boundaries, and inadequate versioning. To tackle these issues, the authors propose Rel(AI)Build—the first deterministic, tool-agnostic configuration governance framework specifically designed for LLM coding agents. Treating agent definitions as managed supply chains, Rel(AI)Build enforces configuration integrity through SHA-256 content addressing, HMAC-signed lockfiles, hash-chain audit logs, hierarchical access controls, and a state-machine-driven development workflow. The framework also supports multi-IDE target compilation. Empirical evaluation demonstrates that Rel(AI)Build effectively preserves configuration immutability under adversarial compliance tests, thereby validating its reliability and security guarantees.
📝 Abstract
LLM coding harnesses grant agents broad file and shell access, yet the configuration layer that steers them -- rules files, agent definitions, IDE-specific markdown -- is largely unmanaged. A prevalence study of 10,008 public GitHub repositories (n=6,145 agent config files) finds that agent configurations propagate as undeclared shared components: 10.1% of tracked paths are SHA-256 exact duplicates across independent repositories (fork-adjusted, threshold-independent), with 75.5% of clone pairs crossing organisational boundaries. Two further patterns are indicative: configurations are rarely revised (58% single-commit; 0.4 vs 0.6 commits/month age-normalised against CI/CD workflows), and rarely declare permission boundaries (<1% of agent configs vs 33% of Actions workflows, n=31 true positives).
We propose a deterministic control plane above the harness that maps one-to-one to these gaps. Rel(AI)Build treats agent definitions as a managed supply chain (SHA-256 content addressing, HMAC-stamped lockfiles, hash-chained audit logs); enforces tiered permissions and attack-derived blocklists before LLM invocation; gates feature work through a phase state machine with requirement-to-file-to-test traceability; compiles a single canonical definition to seven IDE targets; and detects prompt drift via Jaccard similarity. Conformance tests on injected violations confirm each mechanism enforces its stated invariant; developer outcomes remain future work. Governance of this layer must be deterministic and tool-agnostic -- not delegated to further LLM orchestration.