Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse. The post I Replaced a 15-Minute Booking Process with a LangGraph AI Agent appeared first on Towards Data Science .
Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments, policies, execution protocols, and recovery. Compressing these agent control contexts (ACCs) can reduce input cost and context use, yet existing prompt-compression evaluations do not reveal whether the resulting control remains operationally reliable.
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast.
A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior The post Put the Agent Inside the Workflow appeared first on Towards Data Science .
How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science .
In five to ten years, the sharpest manager in your company might not be human, might not sleep, and might exist entirely in shared GPU memory. This is the systems-level view of the algorithmic corporation — why middle management collapses into a protocol, what breaks in the current AI stack, and what has to be built for autonomous agents to actually run a business.
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
arXiv:2601. 20797v1 Announce Type: cross Abstract: This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions.
arXiv:2607. 26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives.
arXiv:2607. 26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules.
arXiv:2607. 26181v1 Announce Type: new Abstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin.
arXiv:2607. 26307v1 Announce Type: new Abstract: Contemporary LLM-based coding agents produce code as black-box outputs: the rationale behind each line is hidden, the evolution of the code through benchmark-driven repair is ephemeral, and post-hoc auditing is impossible.
arXiv:2607. 26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them.
arXiv:2607. 26773v1 Announce Type: new Abstract: Latent communication in large language model (LLM)-based multi-agent systems (MAS) transmits continuous internal representations instead of text, but greater representational capacity does not establish that the receiver uses task-relevant information.
arXiv:2607. 26935v1 Announce Type: new Abstract: Bot detectors deployed at scale treat traffic as binary: human or bot.
arXiv:2607. 27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching.
arXiv:2607. 27155v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to assist users in completing tasks.
arXiv:2607. 26068v1 Announce Type: cross Abstract: Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability.
arXiv:2607. 26069v1 Announce Type: cross Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen.