Agents
How to Create Reliable Conversational AI Agents Using Parlant?
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 at…
Building AI agents is 5% AI and 100% software engineering
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 with…
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
class MCPAgent: “””Advanced MCP Agent with evolved capabilities – Jupyter Compatible””” def __init__(self, agent_id: str, role: AgentRole, api_key: str = None): self.agent_id = agent_id self.role = role self.api_key = api_key self.memory = [] self.context = AgentContext( agent_id=agent_id, role=role, capabilities=self._init_capabilities(), memory=[], tools=self._init_tools() ) self.model = None if GEMINI_AVAILABLE and api_key: try: genai.configure(api_key=api_key) self.model = genai.GenerativeModel(‘gemini-pro’) print(f”✅…
Why Every SaaS Product Will Launch With Built-in AI Agents by Default
Late at night, in a coworking space filled with the blue glow of screens and the quiet hum of anticipation, a SaaS startup team is rehearsing their demo. They’ve got dashboards, KPIs, charts, and clean UX flows. But something feels… flat. A team member asks, “Can we make it answer questions? Like, if a user…
AI agents are science fiction not yet ready for primetime
This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on all things AI, follow Hayden Field. The Stepback arrives in our subscribers’ inboxes at 8AM ET. Opt in for The Stepback here. It all started with J.A.R.V.I.S. Yes, that J.A.R.V.I.S. The one from the Marvel movies….
Zhipu AI Unveils ComputerRL: An AI Framework Scaling End-to-End Reinforcement Learning for Computer Use Agents
In the rapidly evolving landscape of AI-driven automation, Zhipu AI has introduced ComputerRL, a groundbreaking framework designed to empower agents with the ability to navigate and manipulate complex digital workspaces. This innovation addresses a core challenge in AI agent development: the disconnect between computer agents and human-designed graphical user interfaces (GUIs). By integrating programmatic API…
Nebius AI Advances Open-Weight LLMs Through Reinforcement Learning for Capable SWE Agents
The landscape of software engineering automation is evolving rapidly, driven by advances in Large Language Models (LLMs). However, most approaches to training capable agents rely on proprietary models or costly teacher-based methods, leaving open-weight LLMs with limited capabilities in real-world scenarios. A team of researchers from Nebius AI and Humanoid introduced a reinforcement learning framework…
FAQs: Everything You Need to Know About AI Agents in 2025
TL;DR Definition: An AI agent is an LLM-driven system that perceives, plans, uses tools, acts inside software environments, and maintains state to reach goals with minimal supervision. Maturity in 2025: Reliable on narrow, well-instrumented workflows; improving rapidly on computer use (desktop/web) and multi-step enterprise tasks. What works best: High-volume, schema-bound processes (dev tooling, data operations,…
A Code Implementation to Build a Multi-Agent Research System with OpenAI Agents, Function Tools, Handoffs, and Session Memory
In this tutorial, we begin by showcasing the power of OpenAI Agents as the driving force behind our multi-agent research system. We set up our Colab environment with the OpenAI API key, installed the OpenAI Agents SDK, and then defined custom function tools, web_search, analyze_data, and save_research, to harness the agents’ capabilities. We instantiate three…
Google Cloud’s data agents promise to end the 80% toil problem plaguing enterprise data teams
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Data doesn’t just magically appear in the right place for enterprise analytics or AI, it has to be prepared and directed with data pipelines. That’s the domain of data…