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DZone.com Feed Recent posts on DZone.com The Agent Changed Its Plan Mid-Run: Reconciling AI Decisions With Completed Temporal Activities 28 Sep 2026, 15:00 by Akhil Madineni An AI agent rarely follows a long plan exactly as first proposed. Tool results expose missing facts, external systems change, policies arrive through human input, and a model may discover that an earlier assumption was wrong. ReAct-style agents were explicitly designed around this interleaving of reasoning, action, observation, and plan updates rather than one immutable plan. The engineering difficulty begins when those actions are durable side effects.  In Temporal, a completed Activity is not an intention that can be edited out of a revised plan; its completion and result are part of the Workflow Event History. Replanning therefore has to reconcile a new decision with an already-real past.  ‍ Building a Product Recommendation Engine With Neo4j — No ML Library Required 28 Sep 2026, 14:00 by Akmal Chaudhri When many developers think about recommendation engines, they think of machine learning: collaborative filtering models, matrix factorization, embedding vectors, and training pipelines. What surprises many people is that you can build a genuinely useful recommendation system with nothing more than a graph database and several Cypher queries. No scikit-learn, no TensorFlow, no model training. Just the natural structure of the data doing the work. In this article, we'll build a product recommendation engine on top of Neo4j Aura using two Jupyter notebooks. The first generates a realistic synthetic dataset and loads it into Aura. The second runs four recommendation queries directly in Cypher and visualizes the results with Plotly. Everything runs locally in a Python virtual environment against a free cloud Neo4j instance. ‍ Why Incident Response Needs Memory, Not Just Intelligence 28 Sep 2026, 13:00 by Akshay Pratinav, Chaitanya Bhatt Every production incident starts with a simple question: "Has this happened before?" I've lost count of how many incident bridges I've joined where that question came up within the first few minutes. Before anyone proposes restarting a service or rolling back a deployment, someone inevitably starts searching. They look through Slack conversations from previous outages, browse old postmortems, compare dashboards with similar incidents, or dig through runbooks to see whether another team has already solved the same problem. ‍ Stop Paying a Model to Make Decisions You Already Made 28 Sep 2026, 12:00 by Amith Reddy Ravuru If your team distributes AI development skills (as a Claude Code or Cursor plugin or a shared rules file), you own a catalog. That catalog covers things like: How to structure a service What must pass before a commit Which internal library to use instead of rolling your own Skills load cheaply, thanks to progressive disclosure. Only the name and one-line description sit in context until the description matches the work. So when token spend climbs, the intuitive read is context bloat, and the obvious lever is tuning descriptions to fire less. ‍ ‍ You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our terms. If you no longer wish to receive these emails, you can unsubscribe from this feed, edit this subscription, or manage all your subscriptions.