How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
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 […]
Time to first token (TTFT) explains a familiar LLM behaviour: a noticeable pause before the first word, followed by a stream of much faster tokens. If a model needs 1.5 seconds to begin but delivers later tokens roughly every 30 ms, the gap is usually the result of how transformer inference works, not simply a […]
AI product development has changed the cost of making software. A product idea that once needed a specification, design work, and an engineering sprint can now become an interactive prototype in hours. An engineering task that might have taken days can be explored, implemented, reviewed, and revised much faster with coding agents. That is a […]
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 […]
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 […]
I have been following the rapid progress of AI-assisted software development, and games with Astra caught my attention because they show a different side of what coding agents can do. OpenAI’s recent developer post demonstrates how an AI coding agent can work across architecture, rendering, testing, assets, and optimisation rather than simply generating isolated code. […]