As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering.
arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv:2606. 19992v1 Announce Type: cross Abstract: In the agentic web era, LLM-based agents increasingly invoke web services as tools, yet most interfaces remain \emph{static endpoints} that poorly express long-horizon workflows with loops, conditionals, joins, and retries.
AegisFlow is a multi‑agent AI framework that automates the detection and remediation of failures in data pipelines, using a Watchdog agent for telemetry and a Repair agent that generates, tests, and deploys code patches via large language models. It employs a non‑intrusive Parallel Shadow Patching approach based on the MAPE‑K loop to validate patches in digital twin environments. Experiments across five failure scenarios show a 98.1% reduction in mean time to repair—from 170 minutes to 3.2 minutes—and a 92% patch success rate, freeing up 98% of data engineering on‑call time for innovation.
arXiv:2511. 00651v2 Announce Type: replace Abstract: Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging.
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...