AutoGen

TL;DR Plan‑then‑Execute (P‑t‑E) agents separate strategy from action: a Planner writes a machine‑readable plan; an Executor carries it out. This simple split dramatically improves predictability, cost control, and—crucially—security. Hardened correctly (least‑privilege tools, sandboxed code, human sign‑offs), P‑t‑E becomes an enterprise‑grade pattern rather than a lab demo. Why today’s agents need a spine, not vibes Reactive patterns like ReAct feel nimble because they “think, act, observe, repeat.” But that short‑horizon loop is exactly what makes them fragile in production: they meander, retry the same failing step, and are easy to hijack by indirect prompt injection embedded in web pages or PDFs. P‑t‑E locks the control‑flow before the agent ingests untrusted data. The plan becomes an auditable artifact and the execution stage can be cheap, parallel, and tightly permissioned. ...

TL;DR Agentic AI is moving from toy demos to systems that must coordinate, persist memory, and interoperate across teams and services. A new survey maps the landscape—frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, Agno, Google ADK, MetaGPT), communication protocols (MCP, ACP, A2A, ANP, Agora), and the fault lines that still block production scale. This article distills what’s ready now, what breaks in production, and how to architect for the protocols coming next. ...

In the world of chemical engineering, optimization is both a science and an art. But when operating conditions are ambiguous or constraints are missing, even the most robust solvers stumble. Enter the next-gen solution: a team of LLM agents that not only understand the problem but define it. When Optimization Meets Ambiguity Traditional solvers like IPOPT or grid search work well—if you already know the boundaries. In real-world industrial setups, however, engineers often have to guess the feasible ranges based on heuristics and fragmented documentation. This paper from Carnegie Mellon University breaks the mold by deploying AutoGen-based multi-agent LLMs that generate constraints, propose solutions, validate them, and run simulations—all with minimal human input. ...