Browsing: multiagent

In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes.…

A junior loan officer handling data intake, risk screening, and final decisions alone is prone to mistakes because the role demands too much at once. The…

My first attempt at building a travel planning agent looked exactly like most early prototypes: one big model, a few tools, and a long system prompt.…

Enterprises are managing ever-growing volumes of content, ranging from product catalogs and support articles to knowledge bases and technical documentation. Ensuring this information remains accurate, relevant,…

@tool def sql_investigate(query: str) -> dict: try: df = con.execute(query).df() head = df.head(30) return { “rows”: int(len(df)), “columns”: list(df.columns), “preview”: head.to_dict(orient=”records”) } except Exception as e:…

Modern AI applications rely on intelligent agents that think, cooperate, and execute complex workflows, while single-agent systems struggle with scalability, coordination, and long-term context. AgentScope AI…

Multi-agent solutions, in which networks of agents collaborate, coordinate, and reason together, are changing how we approach real-world challenges. Enterprises manage environments with multiple data sources,…

Our work with large enterprise customers and Amazon teams has revealed that high stakes use cases continue to benefit significantly from advanced large language model (LLM)…