An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science .
By Mahnoor Javed
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.
By Mostafa Ibrahim
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 .
By Abdullahi Dattijo
The article discusses how an agent can continue to produce confident answers even when its tools fail, leading to a misleadingly positive dashboard display. It highlights the issue of agents generating responses from nothing after tool breakdowns, which can create a false sense of reliability. The piece serves as a cautionary note for developers deploying agentic AI systems.
By Benjamin Nweke
AI agents can make incorrect decisions or waste resources even when their tools and infrastructure work normally. Their nondeterministic behavior makes these problems difficult to reproduce and diagno...
arXiv:2610.18579v1 Announce Type: new
Abstract: System One models choose an agent's next action by scoring a supplied set of options in a single pass. This makes them fast and cheap, but when they ch...
By Yubo Li, Ramayya Krishnan, Rema Padman