Runtime Self-Healing Policy Loops

• Runtime self-healing policy loops are adaptive closed-loop mechanisms that monitor, analyze, plan, and execute repairs to maintain system resilience in real time.
• They leverage diverse techniques such as finite-state automata, Bayesian inference, deep Q-learning, and LLM-based code synthesis for dynamic error detection and remediation.
• Key architectures like Healer, Proactive Libraries, and AutoGuard demonstrate successful applications in API recovery, SDN management, and secure DevSecOps.

A runtime self-healing policy loop is an adaptive, closed-loop mechanism that observes and diagnoses failures in a software or system environment, plans corrective actions based on policy or learned mappings, executes these actions at runtime, and then observes the results to ensure system resilience or to trigger further corrective cycles if necessary. These loops are foundational to autonomic computing, modern software runtime repair, and resilient AI deployment, manifesting in diverse contexts such as LLM-driven error recovery, API misuse remediation, safety-critical policy repair, secure DevSecOps, self-healing agents, and robust deep learning. Several formal and empirical frameworks exist, with leading contributions such as Healer (LLM-assisted repair) (Sun et al., 2024), Proactive Libraries (Riganelli et al., 2017), SDN self-healing with probabilistic diagnosis (Sánchez et al., 2015), closed-loop neural self-healing (Chen et al., 2022), VIGIL reflective agents (Cruz, 8 Dec 2025), AutoGuard for DevSecOps (Anugula et al., 4 Dec 2025), and runtime safety-driven policy repair (Zhou et al., 2020).

1. Formal Models and Operational Semantics

Runtime self-healing policy loops are most commonly formalized as closed feedback loops with explicit stagewise separation of monitoring, analysis, planning, and execution (MAPE). Variants are instantiated using finite-state automata over event streams (Riganelli et al., 2017), Bayesian inference models for fault diagnosis (Sánchez et al., 2015), deep Q-learning agents (Anugula et al., 4 Dec 2025), or differentiable control/optimization layers (Chen et al., 2022, Zhou et al., 2020).

A common specification is:

• Let $S_t \in \mathcal{S}$ be the full system state at step $t$.
• Let $e_t \in \mathcal{E}$ be an observed error event (exception, alarm, violation).
• Let $a_t \in \mathcal{A}$ be an action synthesized by the self-healing planner.

The closed policy loop iteratively computes:

$a_t = \pi(S_t, e_t) \ S_{t+1} = f(S_t, e_t, a_t)$

where $\pi$ may be a code-synthesizing LLM (Sun et al., 2024), an automaton-triggered fix (Riganelli et al., 2017), or an RL-generated remediation action (Anugula et al., 4 Dec 2025), and $f$ is the next-state transition.

For proactive API policy enforcement, the module is represented as a tuple $(\Sigma, Q, q_0, \delta, H)$ with $\Sigma$ the event alphabet, $Q$ automaton states, $q_0$ initial state, $\delta$ transition function, and $H$ a healing action mapping (Riganelli et al., 2017). In neural self-healing, the system is defined by the dynamical evolution $x_{t+1} = F_t(x_t + u_t)$, where $u_t$ minimizes a control objective to keep the state on a low-loss manifold (Chen et al., 2022).

2. Canonical Architectures and Loop Realizations

Runtime self-healing loops are implemented at varying abstraction levels:

• Healer wraps each program statement in try-except constructs; upon exception, it leverages a prompt-engineered LLM to synthesize a code patch, executes it in a sandbox, validates state updates, and resumes execution. This instrumentation is achieved via AST-level rewriting (Sun et al., 2024).
• Proactive Libraries intercept host/library events at runtime via bytecode weaving; modules maintain automata over these events, trigger healing actions (e.g., insertion/suppression of method calls) upon policy violation, and can be loaded or unloaded at runtime (Riganelli et al., 2017).
• SDN Self-Healing integrates alarm monitoring, probabilistic diagnosis via Bayesian networks (auto-instantiated from topology and alarm streams), a policy registry mapping faults to reconfiguration actions, and actuator enforcement via southbound APIs (e.g., OpenFlow, SNMP, NETCONF) (Sánchez et al., 2015).
• AutoGuard in DevSecOps uses a three-stage loop: telemetry aggregation into a risk-based state vector, deep Q-learning action selection, and a healing orchestrator that applies or simulates remediations, feeding results back for policy refinement (Anugula et al., 4 Dec 2025).
• Policy Repair in Control collects runtime traces, detects unsafe states using a model-predictive safety controller, and periodically solves a constrained trajectory optimization to repair the policy and reduce future unsafe interventions (Zhou et al., 2020).
• VIGIL for agentic LLM systems implements a multi-stage supervised maintenance loop: log appraisal and aggregation to an affective memory bank, structured diagnosis (roses/buds/thorns), guarded patch planning, code or prompt adaptation, and strict state-machine gating to enforce loop invariants (Cruz, 8 Dec 2025).

The following table summarizes core architecture patterns for select systems:

System	Monitoring Granularity	Planning Component	Execution/Actuation
Healer	Python statements	LLM code synthesis	Sandboxed code patch
ProactiveLib	API events/callbacks	Automata-based edit	API call injection
AutoGuard	DevSecOps telemetry	DQN agent	Remediation playbook
SDN-SelfHeal	Network/service alarms	BN+policy engine	Controller commands
VIGIL	Agent logs/events	Affective/heuristic	Prompt/code patch
PolicyRepair	Control trajectory points	QP policy update	Policy parameter fit

3. Algorithms, Pseudocode, and Policy Loop Variants

Closed-loop self-healing algorithms embody the following recurrent structure:

1. Monitor: Observe or intercept events (errors, alarms, API calls).
2. Analyze/Diagnose: Contextualize event (exception type, location, system state; probabilistic fault attribution).
3. Plan: Select or synthesize an action (edit automata, LLM prompt synthesis, RL policy, optimization-based repair).
4. Execute: Apply corrective action; validate resulting system state.
5. Repeat: If further errors or policy violations occur, re-enter the loop.

For example, the core algorithm for Healer is:

for i in 1..n: try: execute(L_i) except Exception as e: ctx = collect_context(L_i, e, S) a = LLM.generate(ctx) for attempt in 1..MAX_RETRIES: try: S' = exec_in_sandbox(a, state=S) S = merge_states(S, S') break except Exception: if attempt == MAX_RETRIES: raise e

Proactive modules follow:

state = q0 onIntercept(event e): nextState = delta(state, e) if nextState == err: actions = H(state, e) for a in actions: inject(a) state = q0 else: state = nextState forward(e)

RL-based loops (AutoGuard) update Q-networks from episodic interaction:

for episode=1..N: s = getState() for t=1..T: if random()<epsilon: a = random_action() else: a = argmax_a Q(s,a;theta) success, deltaU, cost = Orchestrator.apply(a) r = alpha*deltaU + beta*success - gamma*cost s' = getState() store_transition(s,a,r,s') train_Q(theta) s = s'

4. Evaluation, Metrics, and Empirical Findings

Across systems, key quantitative metrics are reported as follows:

• Healer (Sun et al., 2024):
  • PROCEED: Completion without pending exceptions; GPT-4 achieves 72.8%.
  • CORRECT: Output correctness; GPT-4 reaches 39.6%.
  • Per-error-type breakdown: AttributeError (88.1%), IndexError (80.9%), FileNotFoundError (50.0%).
  • Overhead: <1 ms per 10⁶ normal statements, LLM inference mean latencies (1.6–3.1 s).
• Proactive Libraries (Riganelli et al., 2017):
  • 16/27 real-world Android apps exhibited violations; 100% of violations automatically healed.
  • Runtime overhead: ≤2% slowdown; memory <1 MB; energy impact <5%.
• AutoGuard (Anugula et al., 4 Dec 2025):
  • Detection Accuracy: 95.6% (vs. 77.5% for anomaly detection).
  • Mean Time to Recovery: ≈82 s (~38% improvement).
  • False Positive Rate: 6.4% (~34% reduction).
  • Convergence: ~2000 episodes in simulation.
• SDN Self-Healing (Sánchez et al., 2015):
  • End-to-end loop latency: 1–3 s.
  • Fault attribution accuracy: >90%.
  • Policy recovery: 100% on injected failures.
  • Service restoration: 95–100% throughput recovery.
• Policy Repair (Zhou et al., 2020):
  • Empirical case studies: safety controller removes velocity cap violations but degrades performance; repaired policies maintain safety with minimal performance loss.
  • Theoretical bound: naive controller switch incurs quadratic loss; optimized repair substantially reduces intervention frequency and preserves trajectory efficiency.
• Self-Healing Neural Networks (Chen et al., 2022):
  • AA-$\ell_\infty$ CIFAR-10, ResNet-18: 0%→64% with self-healing.
  • TRADES model: Clean: 82.4%→87.5%; AA-$\ell_\infty$: 48.7%→66.6%.
  • Overhead: 3–5× normal inference.

5. Variants: Adaptive, Probabilistic, and Reflective Loops

Distinct architectural and methodological trends have emerged:

• LLM-Driven Loops: Healer (Sun et al., 2024) uses prompt-engineered LLMs for code synthesis at the granularity of source statements, demonstrating high zero-shot performance and improvability via fine-tuning.
• Rule-Based Automata: Proactive Libraries (Riganelli et al., 2017) rely on edit automata or temporal logic policies, yielding lightweight, explainable healing tied to well-understood resource usage policies.
• Probabilistic Diagnosis and Policy Mapping: SDN self-healing (Sánchez et al., 2015) applies Bayesian networks for root-cause analysis, mapping probabilities to concrete remediations; similar separation of diagnosis and planning is seen in agentic frameworks like VIGIL (Cruz, 8 Dec 2025).
• Data-Driven and RL-Based Self-Healing: AutoGuard (Anugula et al., 4 Dec 2025) and neural self-healing (Chen et al., 2022) demonstrate learned, adaptive policy loops capable of handling error or attack landscapes not covered by static rules.
• Reflective and Meta-Healing: VIGIL (Cruz, 8 Dec 2025) introduces a layered approach operating "next to" agents, processing emotional traces and embedding self-diagnosis, guarded adaptation, and meta-level self-repair in agentic LLM stacks.

6. Limitations and Open Challenges

Open challenges across paradigms include:

• Security Trust Boundary: LLM-generated code may be malicious or unsafe, requiring sandboxing, taint tracking, or vulnerability analysis not yet fully realized (Sun et al., 2024).
• Policy Expressiveness: Rule-based systems are limited by the event alphabet and cannot capture semantic correctness or subtle context (Riganelli et al., 2017).
• Overhead and Scalability: Deep learning-based and optimization-based loops can have substantial computational or latency overhead, which may be prohibitive in ultra-low latency or large-scale deployments (Chen et al., 2022, Anugula et al., 4 Dec 2025).
• Policy Interaction and Composition: Multiple self-healing loops (modules, layers, agents) may interact or conflict, necessitating coordination, prioritization, or global state reasoning (Riganelli et al., 2017, Cruz, 8 Dec 2025).
• Human Auditing and Trust: The transparency of synthesized actions, the ability to audit or revert changes, and the preservation of core identity semantics are critical for adoption in safety-critical domains (Cruz, 8 Dec 2025).
• Generality and Language/API Coverage: Most frameworks are evaluated in constrained domains (e.g., Python, Android API, neural classifiers), with substantial engineering required to generalize to compiled languages, multi-component distributed systems, or mixed-criticality environments (Sun et al., 2024, Riganelli et al., 2017, Sánchez et al., 2015).

7. Future Directions

Several avenues for advancing runtime self-healing policy loops are identified:

• Hybrid Approaches: Combining learned repair, rule-based recovery, and static analysis for robustness across known and unforeseen failure modes (Sun et al., 2024).
• Adaptive Policy Synthesis: Integrating empirical learning so that healing actions and policies evolve from observed traces or feedback (e.g., learned policy automata, experience-based RL, cached successful repairs) (Anugula et al., 4 Dec 2025, Cruz, 8 Dec 2025).
• Formal Verification and Assurance: Embedding static or runtime verification steps to guarantee functional and security properties following repair (Riganelli et al., 2017).
• Human-in-the-Loop and Auditable Adaptation: Facilitating transparent, explainable healing cycles with options for human vetting, rollback, and diff-based audit trails (Cruz, 8 Dec 2025).
• Systematic Multilayered Self-Healing: Extending reflective maintenance layers (such as VIGIL) across heterogeneous agentic and software systems to ensure system-wide resilience.
• Resource-Efficient Inference: Modeling and reducing computational overhead via quantization, fast approximation algorithms, or hierarchical repair scheduling, especially for LLM-assisted and control-based healing (Sun et al., 2024, Chen et al., 2022).

Runtime self-healing policy loops have demonstrated significant resilience and recovery improvements in complex, modern software and AI systems (Sun et al., 2024, Riganelli et al., 2017, Sánchez et al., 2015, Anugula et al., 4 Dec 2025, Cruz, 8 Dec 2025, Chen et al., 2022, Zhou et al., 2020). Ongoing research focuses on broadening coverage, improving security and transparency, and optimizing adaptive response in the face of evolving system and threat landscapes.