{"slug":"distributed-systems","label":"Distributed Systems","item_count":3,"day_count":3,"source_count":3,"first_seen":"2026-10-19T11:00:00+00:00","last_updated":"2026-11-02T17:56:28+00:00","generated_at":"2026-11-04T00:12:28.797841+00:00","sources":["arxiv_agent_systems_research","arxiv_llm_reliability","infoq_ai_ml"],"days":[{"date":"2026-10-19","items":[{"title":"Article: From Microservices to Agents: the Next Evolution of Distributed Systems","url":"https://www.infoq.com/articles/distributed-systems-microservices-agentic-ai/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=AI%2C+ML+%26+Data+Engineering","source":"infoq_ai_ml","type":"news","summary_1line":"The article examines the transition from microservices to agentic AI architecture in distributed systems. Microservices allow for functional decomposition, while agentic systems focus on decision decomposition. This s...","why_it_matters":"Matches feed focus: agentic.","sid":"640c8e1b8359ba47","published":"2026-10-19T11:00:00+00:00","editor_note":"Architectural framing: agents decompose decisions, not functions."}]},{"date":"2026-10-28","items":[{"title":"Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness","url":"http://arxiv.org/abs/2610.38793v1","source":"arxiv_llm_reliability","type":"paper","summary_1line":"Distributed machine learning (DML), including conventional distributed training and federated learning (FL), enables collaborative model development across cloud-edge-IoT systems without fully centralizing raw data. H...","why_it_matters":"Matches feed focus: evaluation.","sid":"cd86ac953a59e37c","published":"2026-10-28T20:00:47+00:00","editor_note":"Survey covering security and trust in distributed ML and federated learning."}]},{"date":"2026-11-02","items":[{"title":"DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?","url":"http://arxiv.org/abs/2611.03523v1","source":"arxiv_agent_systems_research","type":"paper","summary_1line":"LLM agents show strong potential in software engineering tasks such as competitive programming and code synthesis. Existing benchmarks, however, largely focus on small-scale settings such as self-contained Python modu...","why_it_matters":"Matches feed focus: agent, evaluation.","sid":"3b1d851d80fa773c","published":"2026-11-02T17:56:28+00:00","editor_note":"First benchmark here aimed at agent diagnosis of distributed-system bugs."}]}],"editorial":{"tldr":"InfoQ argued on Oct 19 that agentic AI shifts distributed systems from functional decomposition to decision decomposition. A survey of trustworthy distributed ML followed on Oct 28.","stale":false,"whats_new":"DistDebug-Bench (Nov 2) tests whether LLM agents can diagnose bugs in distributed systems, beyond the small self-contained modules existing benchmarks use.","why_it_matters":"Agents are being placed inside distributed architectures, but there is little evidence yet that they can debug them; a benchmark is the first way to measure that.","take_for_builders":"Before giving an agent on-call duties in a multi-service system, run it on distributed-failure scenarios; single-module coding scores do not predict this.","beats":[{"kicker":"FRAMING","tone":"launch","headline":"Microservices to agents: decision decomposition","sids":["640c8e1b8359ba47"]},{"kicker":"RISKS","tone":"rising","headline":"Survey of security and trust in cloud-edge-IoT distributed ML","sids":["cd86ac953a59e37c"]},{"kicker":"NOW","tone":"now","headline":"DistDebug-Bench targets agent debugging of distributed systems","sids":["3b1d851d80fa773c"]}],"open_questions":["How do current LLM agents score on DistDebug-Bench?","Do agent frameworks adopt distributed-systems failure testing?"],"generated_at":"2026-11-04T00:00:00+00:00"}}