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Connecting an agent to more tools sounds like pure progress. Each new MCP server adds capability, and each one also adds text that the model must read before it can do anything useful. Tool names, descriptions, and input schemas all take up space in the context window. Large tool results take up more. Left unmanaged, this context bloat raises cost, slows responses, and can make the agent worse at choosing the right tool. This blog explains where the tokens go, how to measure

Teams building AI agents keep running into two ideas that sound similar and do different jobs: skills and MCP. Both extend what an agent can do. Mixing them up leads to bloated prompts, missing connections, and systems that are harder to maintain than they need to be. In short, MCP gives an agent access to tools and data. Skills give an agent know-how: instructions, procedures, and resources for doing a kind of task well. One answers the question of what the agent can reach.

AI agents are learning to use websites the way people do. They read the page, find a button, click it, and hope the layout has not changed since the last visit. It works, and it is fragile and slow. WebMCP proposes a different approach. Instead of guessing how a page works, an agent asks the page what it can do. The website publishes a list of tools with clear names, descriptions, and inputs, and the agent calls them directly. This blog explains what WebMCP is, how it works,

Most teams build their first MCP server as a single process on one machine. It works well in development, and the design questions only appear later, when traffic grows, a second instance is added, or a deployment restarts the server in the middle of someone's work. At that point, one question decides a lot: does the server remember anything between requests? A stateless MCP server does not. Every request carries what the server needs, and any instance can answer it. This blo

Two protocol names keep appearing in discussions of AI agents: MCP and A2A. They are often mentioned together, and they are sometimes treated as rivals. In practice, they solve different problems. MCP, the Model Context Protocol, connects an agent to tools and data. A2A, the Agent2Agent protocol, connects one agent to another. A single agent system can use both, and many serious multi-agent designs will. This blog explains what each protocol does, how they differ, how they fi

Reviewing AI outputs by hand is slow and expensive. So many teams ask a language model to do the grading. A judge model reads an answer, scores it against a rubric, and moves on to the next thousand. The idea works well in some settings and fails quietly in others. A judge that agrees with your reviewers on easy cases can still miss the mistakes that matter most, and it can reward the wrong things without anyone noticing. This blog explains when an LLM judge tends to agree wi

Most AI projects do not fail because the model was wrong. They fail because the goal was fuzzy, the data was not ready, nobody owned the result, or the team and the client stopped understanding each other somewhere between the second and third week. Those problems look the same in every industry and every country. They get harder when the people involved sit in different time zones, speak different languages, and work under different rules. This blog shares the lessons that m

A large language model does not need to know a customer's name, address, or account number to answer most questions. Yet personal data finds its way into prompts, documents, logs, and caches, often without anyone deciding that it should. Once it is there, it is copied. A support message becomes a stored trace, a retrieved document becomes an embedding, and a prompt becomes a record at a vendor. Each copy is a place where the data can leak, linger, or be hard to remove. This b

Every team that builds an AI agent reaches the same moment. The agent works in testing, and someone asks whether it can just send the email, issue the refund, or update the record without a person checking first. The honest answer is that it depends, and the dependence can be made explicit. Autonomy is not a switch. It is a set of decisions about which actions an agent may take alone, which need a person, and which it should never touch. This blog lays out how to make those d

Sooner or later, an AI system says something false to a real customer. It sounds confident, it looks official, and it was never in any of your documents. The first reaction is usually to blame the model. That is rarely the whole story. Hallucinations that reach customers almost always pass through several weak points: a missing document, a search that returned the wrong passage, a prompt that rewards guessing, and no check before the answer went out. This blog explains how to

A voice bot can look impressive in a demo and still lose callers in the first thirty seconds. Customers do not complain about the language model. They notice the pause before it answers, the way it talks over them, and the moment it says it did not understand for the third time. Then they hang up, and the call center sees the same issue arrive later as an angry call. This blog explains why customers hang up on voice bots and the three fixes that address most of the causes: fa

A model that is right 95 percent of the time sounds excellent, and it is one of the most misleading numbers in machine learning. If only five percent of transactions are fraud, a model that labels every transaction as legitimate is 95 percent accurate and catches nothing. The score looks great while the model is worthless. This blog explains why accuracy fails on many business problems, which metrics to use instead, and how to choose one that reflects the real cost of mistake

Choosing between a small language model and a frontier model is one of the most expensive decisions in a production AI project, and it is often made on reputation rather than evidence. Frontier models are more capable on hard problems. Small models are cheaper, faster, and easier to run privately. The right choice depends on the task, and most businesses have several different tasks. This blog compares the two on five common business tasks, explains where each tends to do wel

Most AI assistants stop working the moment the conversation ends. You ask, they answer, and the next step waits until you come back. In September 2026, OpenAI introduced dots, always-on agents inside ChatGPT that keep working toward a goal after the chat is over, using their own cloud computer, a browser, and the apps you connect. This blog explains what OpenAI dots are, how they work, who can use them today, which safety controls come with them, the risks enterprises still n

Every enterprise AI project hits the same wall: the model is capable, but it cannot reach the systems where the work happens. Connecting an assistant to a ticketing tool, a document store, a database, and a CRM has usually meant a custom integration for each pairing, rebuilt whenever anything changes. The Model Context Protocol (MCP) is an open standard that replaces this with one common way for AI applications to discover and use tools and data, which is why it is now a prio

A prompt that sounds better to the person who wrote it is not the same thing as a prompt that performs better in production. Many teams still judge prompt changes by reading a handful of outputs and deciding which one feels right, but that approach cannot tell whether a change improved accuracy, quietly increased hallucinations, or opened the door to a prompt injection attack. Measuring prompt optimization properly replaces opinion with evidence, and for leaders funding AI pr

Most enterprise AI assistants answer questions by finding the passages that look most similar to the question and handing them to a language model. That works well for lookups, but it struggles when the answer depends on how things are connected, such as which systems fail when one service goes down, or what the main themes are across thousands of documents. GraphRAG addresses this by adding a knowledge graph to retrieval, so the system can follow relationships and reason acr

An AI system that reads loan documents, summarizes customer emails, or drafts responses to account queries can save a bank thousands of hours, but only if it can be trusted with sensitive data and cannot be manipulated into doing something it should not. A secure AI banking operations workflow is the set of controls, checkpoints, and monitoring that makes that trust possible, and for banking leaders it is less about the model itself and more about everything built around it.

Struggling with endless red error messages after pasting your ML assignment into ChatGPT? Discover why AI models fail at complex Python data science tasks—like matrix dimensions and data leakage—and learn how to debug your broken code before the deadline.

You've spent hours building the perfect Convolutional Neural Network (CNN) for your image classification assignment. You hit model.fit() or run your PyTorch training loop, sit back, and watch the epochs roll in. Epoch 1/10: loss: 0.6931 - accuracy: 0.5000 Epoch 2/10: loss: 0.6930 - accuracy: 0.5000 Epoch 3/10: loss: 0.6931 - accuracy: 0.5000 Your accuracy is completely flatlined. In a binary classification problem, an accuracy of 50% means your model is learning nothing — it'