496 stories
We present SmartSalesBench, the first multi-turn conversational benchmark explicitly designed to evaluate large language model (LLM) sales agents against psychologically grounded, resistant buyer personas.
On-behalf-of tokens (Databricks), live ACL re-checks (AWS), and parent-level fine-grained authorization: how each keeps an AI assistant from showing users data they can't access, and when to use which.
Beyond basic ReAct loops: six production-oriented architectures for building autonomous, reliable, and scalable LLM agents. The post Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns appeared first on Towards Data Science.
Autonomous AI agents can read requests, retrieve data, reason through options, and trigger actions in seconds. That speed is useful, but it also creates risk when the next step affects money, customer records, or external systems.
RAG and fine-tuning are two powerful strategies for adapting large language models (LLMs) to domain-specific tasks. This post compares their use cases.
Retrieval-Augmented Generation (RAG) and fine-tuning are powerful ways to adapt large language models. Learn the key differences, trade-offs, and when to.
In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context.
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👉 AI Agents Studio : https://www.youtube.com/channel/UCAawqobkJZ28OLcYcMgqYaw?sub_confirmation=1 "This video covers the top trending open-source GitHub projects this week, picked for practical developer productivity.
The $80,000 Hallucination: Why RAG Fails at Healthcare Eligibility Verification Reading comprehension is not transactional verification. When an autonomous intake bot checks a static PDF brochure …
Chatbot agents vs agentic apps: why enterprises are moving to structured, workflow-based AI with deterministic logic instead of single chat agents.
Knowledge orchestration is the practice of connecting every AI and human agent to one centrally managed knowledge layer, and keeping that layer current, consistent and available on every channel. Many contact centers today run AI tools separately, with disconnected sources of truth.
Amazon S3 Vectors metadata pre-filtering is now available in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Pre-filtering evaluates metadata filters before running similarity search, returning up to 5x more of the matching vectors when your filter is selective.
Run 50+ AI models locally on iPhone and Android. Chat, read PDFs and documents with on-device RAG, understand images with vision models, and write code offline. Dictate prompts, customize responses and import GGUF models (experimental).
Building an AI agent that works in a demo is a different problem from running one for 40 million developers. Postman and AWS share the architectural patterns behind Agent Mode: controlling tool sprawl, exposing schema-based reads, and treating context as the real bottleneck, plus how it runs on…
The cost of control The post Where Does the Money Go Across Long-Running Coding Agents? appeared first on Towards Data Science.
EmbeddingGemma 2 launched on October 6, 2026 under Apache 2.0. It is a sub-1B model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space. This article covers the architecture, the benchmarks, and runnable scripts to provide measured results.
Build a production-grade data science toolkit without spending a dollar, using open-source AI tools that match, and sometimes exceed, their paid alternatives.
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