The evolution from reactive to proactive AI systems represents a paradigm shift in software engineering, enabling autonomous agents to anticipate requirements, plan complex workflows, and execute multi-step development tasks without human intervention. This paper presents a novel multi-agent architecture for autonomous code generation and software maintenance in enterprise environments. Our framework integrates specialized AI agents for requirements analysis, code synthesis, testing, and deployment, coordinated through a hierarchical planning system with adaptive decision-making capabilities. The system employs reinforcement learning to optimize task allocation, learn from code review feedback, and improve over time. Experimental evaluation on real-world enterprise codebases demonstrates that our proactive agent system achieves 87.3% automated issue resolution, reduces bug fix latency by 62%, and maintains code quality metrics comparable to human developers. The framework successfully handles complex refactoring tasks, security vulnerability remediation, and feature implementation with minimal human oversight, representing a significant advancement toward fully autonomous software engineering workflows.
Saher Elsayed, Samer Abubaker, M. Ali et al.· Annual International Compute...· 0 citations
Federated learning (FL) on heterogeneous edge networks faces a fundamental tension: standard aggregation protocols assume client homogeneity, yet real-world edge deployments span device tiers with $7 \times$ compute and $\mathbf{1 0} \times$ bandwidth disparities. Slow clients become stragglers that stall synchronous rounds, while uniform gradient compression degrades accuracy on bandwidth-constrained devices. This paper presents FedEdge-Adapt, a novel adaptive federated learning framework that jointly addresses device heterogeneity, straggler mitigation, and communication efficiency without sacrificing model quality. FedEdge-Adapt introduces three tightly coupled mechanisms: (1) tier-aware gradient compression that applies device-class-specific sparsification ratios, (2) drift-corrected aggregation that reweights client updates based on staleness and data heterogeneity, and (3) predictive client selection that anticipates dropout-prone devices using a lightweight resource oracle. We evaluate FedEdge-Adapt on a 30-node heterogeneous edge network over 150 communication rounds using the CIFAR-10 dataset under non-IID distributions ($\alpha=0.5$ Dirichlet) and compare against FedAvg, FedProx, and SCAFFOLD baselines. FedEdge-Adapt achieves 85.44% global accuracy, a 6.85 percentage-point improvement over FedAvg, while simultaneously reducing round latency by $\mathbf{6 7. 4 \%}$, communication overhead by $\mathbf{3 4. 0 \%}$, and client dropout rate by $\mathbf{5 4. 0 \%}$. Convergence is reached in 18 rounds versus 31 for FedAvg. Extended experiments across 100+ rounds confirm long-term stability with no late-stage divergence.
Saher Elsayed, Mohamed Ali, Samer Abubaker et al.· Annual International Compute...· 0 citations