Computer Science editorial
Agentic Configuration Management (ACM): A Reference Configuration Model for Governed Agentic Systems
The core problem
Agentic systems are increasingly composed of heterogeneous agents, prompts, tools, models, skills, composite subsystems, policies, and execution workflows whose configurations evolve across frameworks and runtime environments. Existing LLMOps and AgentOps platforms support orchestration and observability but do not provide a common configuration-governance model for representing and governing these systems as coherent, versioned configurations.
This paper introduces **Agentic Configuration Management (ACM)**, a framework-independent governance and configuration reference model for heterogeneous agentic systems. ACM addresses the gap by combining:
- typed and independently versioned **Agentic Configuration Items**;
- immutable revisions and baselines;
- explicit configuration-runtime separation;
- lifecycle and assurance semantics;
- dependency-aware impact propagation; and
- runtime provenance.
Heterogeneous native configurations are normalized through **semantic projection** into a canonical **Configuration Graph** on which common governance semantics operate. The work is positioned within the taxonomy candidates of Architecture, Cybersecurity, Network, and Cryptography, given
Innovation
For the evaluated configurations, the three frameworks yield **governance-equivalent ACM representations** and **reproducible governance outcomes** after projection. The evaluation combined **27 governance scenarios** with **nine quantitative impact-propagation cases**.
The impact semantics are formalized as monotone propagation over a finite lattice, establishing:
- **Convergence:** the iterative application of the monotone function converges.
- **Termination:** the process terminates in finite steps.
- **Uniqueness:** the least fixed point above the initial impact valuation is unique.
These results provide evidence that common governance semantics can support reproducibility, auditability, dependency analysis, and interoperability across heterogeneous agentic execution abstractions within the evaluated scope.
The reference implementation is a Python package with adapters for LangGraph, CrewAI, and the OpenAI Agents SDK, demonstrating that the ACM model can be applied across different agentic frameworks without modifying their native execution semantics.
Why it matters
The ACM model addresses a critical gap in the governance of heterogeneous agentic systems. By normalizing native configurations into a canonical Configuration Graph, ACM enables common governance semantics that are framework-independent. This supports:
- **Reproducibility:** governance outcomes are reproducible across frameworks after projection.
- **Auditability:** immutable revisions and baselines provide a clear audit trail.
- **Dependency analysis:** dependency-aware impact propagation allows understanding of how changes affect the system.
- **Interoperability:** heterogeneous agentic execution abstractions can be governed under a common model.
The formalization of impact semantics as monotone propagation over a finite lattice provides strong theoretical guarantees (convergence, termination, uniqueness). This is particularly important for cybersecurity and architecture concerns, where understanding the blast radius of configuration changes is essential.
The evaluation scope is limited to the three frameworks and the 27 governance scenarios plus nine quantitative cases. Future work may extend the model to additional frameworks and more complex governance scenarios. Nevertheless, the results provide evidence that common governance semantics can support reproducibility, auditability, dependency analysis, and interoperability across heterogeneous agentic execution abstractions within the evaluated scope.
A conceptual view of the configuration-runtime separation and provenance flow is:
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