Explore how JEPA, Hierarchical Planning, and Energy-Based Models (EBMs) enhance AI Agentic Workflows. These techniques improve reasoning, reduce LLM reliance, and optimize decisions in smart assistants, healthcare, robotics, and finance.
Discover the key principles, best practices, and frameworks for building scalable AI agentic workflows. Learn how to optimize AI agents for reasoning, decision-making, and real-world applications.
This blog explores practical strategies to optimize Agentic AI workflows, focusing on reducing latency and improving throughput in LLM-based applications. Learn how to enhance efficiency through hybrid models, caching, and hardware optimization.
Discover how AI agentic workflows powered by LLMs and Generative AI are revolutionizing industries. Explore 16 transformative use cases showcasing the future of automation, personalization, and innovation in business.
How Magentic-One’s multi-agent patterns evolved into AutoGen 0.4’s actor-model stack—covering architecture, event contracts, observability, and a pragmatic migration plan.
A practitioner’s blueprint to deploy LLM agents in contact centers—architecture, safety, RAG, metrics, and a 30/60/90 rollout that drives automation and lowers cost per contact.
A practical playbook to cut LLM calls—adaptive routing, one-shot multi-head prompts, deterministic tools, precise RAG, and caching—while protecting task success and user experience.
A practitioner’s guide to building reliable agentic AI systems—planning, tools, memory, safety, and evaluation—plus a minimal blueprint you can ship now.
