Agents
Goodbye Prompt Engineering Embrace Process Engineering with AI Agents
[ad_1] A year ago, prompt engineering was the hottest buzzword in AI. Everyone was talking about the art of crafting the “perfect prompt” to get the most out of large language models (LLMs). Entire courses, communities, and job postings emerged around it. But AI doesn’t stand still. Models have improved, businesses demand more, and the…
Agentic AI Explained: How AI Agents Go Beyond GenAI Prompts
[ad_1] Generative AI (GenAI) has transformed how professionals work—making it possible to draft content, summarize reports, and brainstorm ideas in seconds. These capabilities have been revolutionary, but they also reveal limitations. GenAI often operates in isolated moments, requiring users to re-prompt, copy, paste, and manually integrate outputs into real workflows. This gap has led to…
The missing data link in enterprise AI: Why agents need streaming context, not just better prompts
[ad_1] Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time. The challenge is infrastructure. Most enterprise data lives in databases fed by extract-transform-load (ETL) jobs that run hourly or daily — ultimately too slow for agents that must…
How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3
[ad_1] In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO and A2C, and develop our own training callbacks for performance tracking. As we progress, we train, evaluate, and visualize agent performance to compare algorithmic efficiency, learning curves, and…
Forget Apps vs. Agents — The Future Is AI-Powered Apps with Agents Inside
[ad_1] Open your favorite SaaS app. Maybe it’s a CRM, a project tracker, or a financial dashboard. Now imagine instead of clicking through menus or exporting reports, you simply ask: “Why did churn spike last month?” or “What tasks are blocking this sprint?” And the app doesn’t just display numbers. It explains. It highlights the…
Meta AI’s ‘Early Experience’ Trains Language Agents without Rewards—and Outperforms Imitation Learning
[ad_1] How would your agent stack change if a policy could train purely from its own outcome-grounded rollouts—no rewards, no demos—yet beat imitation learning across eight benchmarks? Meta Superintelligence Labs propose ‘Early Experience‘, a reward-free training approach that improves policy learning in language agents without large human demonstration sets and without reinforcement learning (RL) in…
An Intelligent Conversational Machine Learning Pipeline Integrating LangChain Agents and XGBoost for Automated Data Science Workflows
[ad_1] In this tutorial, we combine the analytical power of XGBoost with the conversational intelligence of LangChain. We build an end-to-end pipeline that can generate synthetic datasets, train an XGBoost model, evaluate its performance, and visualize key insights, all orchestrated through modular LangChain tools. By doing this, we demonstrate how conversational AI can interact seamlessly…
How to Evaluate Voice Agents in 2025: Beyond Automatic Speech Recognition (ASR) and Word Error Rate (WER) to Task Success, Barge-In, and Hallucination-Under-Noise
[ad_1] Optimizing only for Automatic Speech Recognition (ASR) and Word Error Rate (WER) is insufficient for modern, interactive voice agents. Robust evaluation must measure end-to-end task success, barge-in behavior and latency, and hallucination-under-noise—alongside ASR, safety, and instruction following. VoiceBench offers a multi-facet speech-interaction benchmark across general knowledge, instruction following, safety, and robustness to speaker/environment/content variations,…
How to Create Reliable Conversational AI Agents Using Parlant?
[ad_1] Parlant is a framework designed to help developers build production-ready AI agents that behave consistently and reliably. A common challenge when deploying large language model (LLM) agents is that they often perform well in testing but fail when interacting with real users. They may ignore carefully designed system prompts, generate inaccurate or irrelevant responses…
Building AI agents is 5% AI and 100% software engineering
[ad_1] Production-grade agents live or die on data plumbing, controls, and observability—not on model choice. The doc-to-chat pipeline below maps the concrete layers and why they matter. What is a “doc-to-chat” pipeline? A doc-to-chat pipeline ingests enterprise documents, standardizes them, enforces governance, indexes embeddings alongside relational features, and serves retrieval + generation behind authenticated APIs…