AI agent evaluation starts with a simple reality: an agent can produce a polished final response and still fail the task. Once an AI system can retrieve information, call tools, request approval and change an external system, the final text is only a report of what happened. It is not proof that the work was […]
LangGraph evaluation turns a convincing multi-agent demo into a workflow you can improve with evidence. A system may return a plausible answer while choosing the wrong specialist, calling unnecessary tools, or becoming too slow and expensive for real users. This tutorial shows how to measure those failures before they reach production. In the previous lessons, […]
LangGraph persistence is what makes a multi-agent workflow safe to pause, inspect, and continue later. Without it, a process restart, a human review step, or a temporary failure can mean starting the whole graph again—and possibly repeating work that already happened. In the previous tutorial on routing and parallel execution, a router selected specialist agents […]
LangGraph routing is the point where a multi-agent graph stops broadcasting work and starts making deliberate choices. It decides which specialist should handle a request, which tasks can run at the same time, and when their findings are ready to combine. In the previous tutorial on LangGraph shared state, we defined safe information flow. Here, […]
LangGraph shared state is where a multi-agent workflow becomes predictable. A supervisor can choose the right specialist and still produce a weak answer if every worker receives the same noisy conversation history. The next step is deciding what each agent can read, what it may return, and what must remain private. This tutorial continues the […]
LangGraph Supervisor Tutorial · Multi-Agent SystemsPart 2 of 6 · View the series guide ← Part 1: Build a conversational agent with LangGraph A LangGraph supervisor tutorial should begin with a simple idea: a useful multi-agent system is not a group of bots talking at random. It is a workflow with a clear decision-maker. One […]
This LangGraph multi-agent systems tutorial series takes you from a single conversational agent to a reliable workflow of a supervisor and specialist agents. Each tutorial adds one practical capability: delegation, shared context, routing, recovery, or evaluation. Who this series is for This series is for Python developers who understand the basics of language models and […]
GPT-6 Astra has not broken the rules of AI system design. It has made the limits of those rules much harder to ignore. OpenAI’s new model is built for work that stretches across browsers, files, code, research, and professional software. It can plan, use tools, inspect results, adapt to new instructions, and continue through a […]
If you are comparing MCP vs API, the short answer is that they solve different integration problems. APIs expose capabilities; MCP gives AI applications a standard way to discover and use tools and context. Key takeaway: An API is a contract for interacting with a system. MCP is a protocol that helps AI applications discover […]
When OpenAI President Greg Brockman took the stage to unveil GPT-6 Astra, he did not hedge his words: “Welcome to the AGI era.” It is the kind of statement that makes Silicon Valley stop in its tracks. However, beneath the slick announcement and leaked feature flags, a stranger and higher-stakes story is unfolding. OpenAI has […]
AI agent frameworks, including Vercel AI SDK, LangGraph, CrewAI, AutoGen and LangChain, are often grouped together. However, they do not solve the same problem. Choose from the architecture you need to build rather than searching for one “best” framework. This comparison uses the supplied five-tool overview as a research prompt, not as a source of […]
The fastest way to get value from a personal AI agent is to stop imagining an AI employee. Start with one routine that is mildly annoying, happens often, and produces work you can easily review. In a couple of hours, you can prototype a useful workflow. You cannot reasonably expect a dependable, unattended assistant that […]
An agent evaluation flywheel can expose the path an AI agent took to reach an answer. A final-answer score can hide wrong tools, needless retries, weak evidence, or a failure to adapt when new information appears. That creates a practical question: what should change next? This post turns that diagnosis into a five-stage operating model. […]
Trajectory evaluation shows how a final answer can look perfect while the agent behind it has already failed. Imagine an agent that tells a support team: “The customer record has been updated.” The sentence is clear and reassuring. However, the trace may show a different story. It may select a search tool instead of an […]
This multi-agent workflow roadmap introduces coordinated AI roles and workflows working toward one outcome—not magical, fully autonomous AI teams. It is the starting point for the series, and it will become a linked learning path as each tutorial is published. Key takeaways A multi-agent system divides a broader job among defined AI roles and workflow […]
The Agentic AI Stack is a modern framework designed to build intelligent agents in artificial intelligence applications. These agents are not just static tools, they observe, reason, act, and improve over time. To build such dynamic systems, we need a well-structured framework. That is where the Agentic AI Stack comes in. Layer 1: Tool / […]
As AI moves beyond simple chatbots, building AI agents that can reason and act autonomously has become a key engineering challenge. This guide explores how to develop production-ready agents using practical, real-world techniques from OpenAI. AI agents represent a transformative leap in automation, transitioning from reactive chatbots to intelligent systems that can independently execute complex, […]
Imagine building powerful multi-agent systems with LangGraph Swarm, where agents collaborate autonomously for seamless AI workflows. That’s LangGraph Swarm: a lightweight, decentralized multi-agent system where agents dynamically hand off tasks and the system retains memory of the last active agent for seamless conversation flow Unlike rigid supervisor architectures where a central agent dictates the flow, […]
Context engineering for AI agents becomes essential when a promising demo meets a real workflow. After a few turns, an agent may repeat a question, follow an outdated instruction from the chat history, or spend most of its prompt on tool logs that do not affect the next decision. The answer is not to carry […]