Towards Data Science
Jun 7
Building a Multi-Agent System in Python
An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science .
Why Most Multi-Agent Systems Fail Even When Evaluation Passes
The article explains how to detect a payload that appears correct yet is not, by employing a watchdog pattern in Python. It discusses the challenges that cause many multi‑agent systems to fail even when their evaluations succeed. The post was originally published on Towards Data Science.
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Towards Data Science
Aug 31
AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.
Towards Data Science
Aug 6
I Built a Tool-Calling Agent in Python. Here’s How I Debugged It
A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework The post I Built a Tool-Calling Agent in Python. Here’s How I Debugged It appeared first on Towards Data Science .
arXiv AI
Aug 20
Position: Behavioral Systems Require Behavioral Tests
The paper argues that artificial agentic systems, which operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time, should be evaluated through systematic observation, perturbation, and interpretation of their actions rather than solely on performance outcomes. It draws on lessons from behavioral sciences to motivate this position and proposes a research agenda that includes methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi‑agent systems. These directions aim to establish a rigorous science of AI behavior.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv:2606. 29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision.
arXiv AI
Jul 10
Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems
arXiv:2607. 07989v1 Announce Type: cross Abstract: Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures.
Towards Data Science
Jun 3
What AI Agents Should Never Do on Their Own
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
arXiv Machine Learning
Aug 11
Activation Probes Surface Code-Security Signals that the Model's Output Misses
arXiv:2608. 09643v1 Announce Type: cross Abstract: AI coding agents now write a growing share of production code, and human security review does not scale at the rate code is generated.
arXiv AI
Jun 19
Trustworthy Multi-Agent Systems: Mitigating Semantic Drift with the Argent Signaling Protocol
arXiv:2606. 19356v1 Announce Type: cross Abstract: When multi-agent LLM systems produce bad answers, not all failures are equal: some answers are grounded in the right material but incomplete, while others are simply ungrounded and should be stopped.
arXiv AI
Jul 9
AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
arXiv:2607. 06624v1 Announce Type: new Abstract: We present AgentLens, a production-assessed benchmark for interactive code agents.
arXiv AI
Aug 26
Rebuild Dossier: Mechanically-Enforced Specs for Agentic App Rebuilds, and What Model-Tier Failures Reveal
The paper introduces rebuild‑dossier, an open‑source tool that locks an application’s real interface before code is written and enforces one‑test‑at‑a‑time building through automated checks. In experiments, a compliant agent failed a held‑back test while a rule‑breaking agent passed, showing that a passing test suite can be gamed. The study also demonstrates that the automated check mechanism, rather than interface‑locking alone, is crucial for reliable rebuilds, and that multi‑level verification catches errors that single‑level checks miss.
arXiv AI
Jul 17
AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
arXiv:2603. 15727v3 Announce Type: replace-cross Abstract: Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplored.